Data source: The atlas of urban expansion (2016 Edition) http://www.atlasofurbanexpansion.org/ (accessed on 20 April 2021). *\** AGR (annual growth rate) is calculated as *AGR* = √*<sup>n</sup> <sup>P</sup>*2014/*P*<sup>2000</sup> <sup>−</sup> <sup>1</sup> ·100%, where *P* is the population, built-up area, or population density of a city, and *n* is the interval in years.

> São Paulo is the largest city in Brazil and the economic, cultural, and technological center. Nearly 20 million people lived in São Paulo in 2014 with the total built-up area

being around 1700 km<sup>2</sup> . Mexico City is the capital of Mexico and is located in the valley of the plateau of central Mexico, at an altitude over 2000 m. It is the political, economic, cultural and transportation center of Mexico. Buenos Aires is the capital and largest city of Argentina and it is a coastal city on the southern bank of the La Plata River. Bogotá is the capital and largest city in Colombia and it is located in the center of Colombia, on a high plateau known as the Bogotá savanna. Due to the eastern mountains, Bogotá can only expand to the west (Figure 1c). Santiago is the capital and largest city in Chile, which is entirely located in the country's central valley. We identified city centers for the five cities using road maps and high resolution images from Google Map (Figure 1, Table 1).

#### *2.2. Land Use and Population Data*

We collected urban land use maps of five Latin American cities in 2000 and 2014 from *The Atlas of Urban Expansion* (*2016 Edition*), which were interpreted using Landsat imagery (30 m resolution) [46]. They classified land use into three categories, namely, built-up area, open space, and water bodies, using the unsupervised classification method (clustering analysis) with the ISODATA (Iterative Self-Organizing Data Analysis Technique) algorithm. The classification result has a high accuracy with verification using Google Earth high resolution imagery. The user's accuracy for the built-up area is 91% and the producer's accuracy is 89.3% [46]. Detailed information on the land use classification procedures can be found in Hurd [48]. The data from *The Atlas of Urban Expansion* (*2016 Edition*) has been successfully used in related studies on urban expansion and urban form [49–51]. The spatial distributions of urban land in 2000 and 2014 and open space and water bodies in 2014 of five Latin American cities are shown in Figure 1.

The population data in Table 1 also comes from *The Atlas of Urban Expansion* (*2016 Edition*), but it is statistical data without information on its spatial distribution. We further collected *LandScan* population data for the five sample cities in 2000 and 2014. *LandScan* is a community standard for global population distribution data at approximately 1 km (30" × 30") spatial resolution, and it represents an ambient population (average over 24 h) distribution [52–54]. The spatial distribution of population data supports the correlation analysis between urban expansion and population growth using grid-based analysis.

#### *2.3. Concentric Ring and Grid-Based Analysis*

We generate a series of 1-km equidistant buffer rings to cover almost the entire built-up areas in 2014 (Figure 2a). Previous studies calculated landscape metrics in concentric rings and analyze their spatial variations [15]. In this study, we calculate the urban land density (built-up density) in each concentric ring, and then fit its distance decay using the inverse *S* curve (See Section 2.4). The urban land density is defined as the proportion of urban land to the area of buildable land in each ring (Equation (1)).

$$\text{Density} = \frac{\text{S}\_{\text{turbam land}}}{\text{S}\_{\text{bubble land}}} \tag{1}$$

where *Dens* is the urban land density in a concentric ring (doughnut). *Surban land* is the area of urban land (built-up area) in a ring, and *Sbuidable land* is the area of buildable land in a ring. The buildable land is the total land area excluding water bodies and mountains in a ring. The concentric rings are developed and urban land density in each ring are calculated in ArcGIS 10.6 (ESRI, Inc., Redwoodds, CA, USA).

The unit of grid-based analysis is 1 km, which is consistent with the resolution of *LandScan* population data. We first clip the *LandScan* population data in 2000 and 2014 for each city using the spatial extent of land use map of the same city (Figure 2b). We convert the population raster file to a vector file in 1 km × 1 km grid segments. We calculate the area of built-up area in each grid for two years, 2000 and 2014, using zonal statistics, and then calculate the change of built-up area in each grid from 2000 to 2014. Finally, we use map algebra to calculate the change in population from 2000 to 2014 in each grid of each city and convert the result to a vector file. We combine the changes in built-up areas and population for the 2000–2014 period based on the ID of each grid. All of these spatial analytics, such as format conversion, zonal statistics, and map algebra, are conducted in ArcGIS 10.6 (ESRI, Inc., Redwoodds, CA, USA).

**Figure 2.** An example of concentric ring analysis (**a**) and grid-based analysis (**b**) in São Paulo, Brazil.

#### *2.4. Inverse S Curve*

Jiao (2015) proposed a function with an inverse *S* curve (Figure 3) to characterize the spatial distribution of the urban land density within a city, as shown in Equation (2) [19]:

$$f(r) = \frac{1 - c}{1 + e^{a((2r/D) - 1)}} + c \tag{2}$$

*α*

*α*

where *f* is the urban land density (built-up density) in each concentric ring that is calculated using Equation (1), *r* is the distance to the city center, *e* is the Euler's number, and *α*, *c* and *D* are parameters, which vary across cities and over time.

*α* **Figure 3.** Curves of the urban land density function with different *α* parameters.

Parameters in the inverse S curve have explicit physical meanings. The *α* parameter controls the form of the fitting curve and a higher *α* indicates a more compact urban form (Figure 3). The *D* parameter denotes the approximate boundary of an urban extent. The *c*

*α α*

*α*

*α*

*α*

parameter represents the background value of the urban land density. The *D* parameter generally increases over time because the urban extent expands. Detailed explanations of these three parameters can be found in Jiao (2015) [19].

The inverse S curve has been an effective and quantitative tool in urban studies. It can be used to not only fit the distance decay of urban land density but also can be applied to other urban indicators, such as the population density, road density, and even land surface temperature (LST) [19,55,56]. The inverse S curve was first proposed using Chinese cities and then it has been applied to cities in other countries across the world [57–60].

#### **3. Results**

#### *3.1. Distance Decay of Urban Land Density*

We calculate the urban land density in each concentric ring using Formula (1). Overall, the urban land density decreases slowly around the city center, and then decreases quickly to a relatively low level, and finally decreases slowly again to the background level of urban land density, showing an inverse *S*-shape (Figure 4). The inverse S curve can fit the distance decay of urban land density very well in all cities in both 2000 and 2014. Parameters for fitted curves are shown in Table 2. The urban physical size varies among the five cities. The radius of the urban extent (represented by the *D* parameter) is the largest for Buenos Aires, which is over 50 km. The radii of urban extents of São Paulo and Mexico City are around 45 km and 42 km in 2014, respectively. The space size is relatively small for Bogotá and Santiago with urban extent radii of 27 km and 26 km, respectively, in 2014.

**Figure 4.** Distance decay of urban land density and fitted results using inverse S curve in concentric rings in five Latin American cities in 2000 and 2014.

*α* The *α* parameter reflects the compactness of urban form and a higher *α* means a more compact urban form. Bogotá has the highest value of *α* (=4.49) in 2014, indicating the most compact urban form among the five cities. The urban population density in Bogotá in 2014 is also the highest among the five cities (24,460 person/km<sup>2</sup> , Table 1), further supporting its compact form. Generally, the *α* parameter increases from 2000 to 2014 in four cities except for Santiago, which has a small decline. Cross-sectionally, Buenos Aires has the lowest value of *α*, indicating that it has the most relatively dispersed urban form.


(9421 person/km<sup>2</sup> , Table 1).

The urban population density in Buenos Aires is also the lowest among the five cities

The gap between the two curves of the same city for 2000 and 2014 reflects the dynamics of the urban expansion, which varies among the five cities. Mexico City has the largest gap between the two curves, indicating the most obvious urban expansion in this city. Comparatively, the two curves of São Paulo nearly overlap, implying a limited urban expansion in this city. The gap between the two curves also reflects the spatial disparities in urban expansion. The new urban land is mainly added around 10 km from the city center in Bogotá from 2000 to 2014, while in Santiago the urban expansion took place in its outskirts. This is why the urban form of Santiago has become more dispersed during 2000–2014 (Table 2). The new urban land is evenly distributed in space in Buenos Aires (Figure 4).

#### **Table 2.** Parameters of the inverse S curve for five Latin American cities in 2000 and 2014.

#### *3.2. Directional Heterogeneity in Urban Form*

Urban expansion is generally not homogeneous, but there is usually a directional heterogeneity due to the influence of natural conditions or urban planning [15,61]. Taking São Paulo as an example, we divided the concentric ring into four directions, namely East, South, West, and North, as shown in Figure 5a. Due to the limited urban expansion from 2000 to 2014 in São Paulo (Figure 4), we only take the land use information in 2014 to present the heterogeneity of urban form in different directions. Overall, São Paulo has an urban form with a longer-extent in the east-west direction but a more limited extent in a north-south direction, which is restricted by the macro physical geography (mountains in the north and water bodies in the south). The built-up area in the easterly direction is 717 km<sup>2</sup> , which is the largest among the four directions, taking 42% of the whole built-up area (1724 km<sup>2</sup> , Table 1). The built-up area in the northern direction is only 248 km<sup>2</sup> .

We calculate the urban land density in four directions in São Paulo using concentric rings (Figure 5a). There are obvious disparities in the distance decay of urban land density and fitted curves in the four directions (Figure 5b). The parameters for the inverse S curve in the four directions are shown in Table 3. The fitted urban extents (parameter *D*) are 55 km, 47 km, 47 km, and 27 km in the East, South, West, and North, respectively. Unsurprisingly, the urban extent in the east is the largest, which is more than twice of that in the north. Remember that the *α* parameter indicates the compactness of urban form. Urban expansion in the south is restricted by water bodies, resulting in the most compact form in this direction (*α* = 8.81), while the urban form is relatively dispersed in the west (*α* = 4.37). There is an obvious bump of the urban land density in the north at around 26 km from the city center, which is caused by leapfrogging urban patches in the north of the city center (Figure 5b).

**Figure 5.** The concentric ring in four directions (**a**) and the spatial attenuation of urban land density in four directions of São Paulo in 2014 (**b**).


#### *α 3.3. Correlations between Land Expansion and Population Growth*

*α α α* We use grid-based analysis to investigate urban land expansion, population growth and their correlations. Changes in population and built-up areas in 1 km grid squares for 2000 and 2014 are presented in Figure 6. There are apparent disparities in space for both land expansion and population growth. Population increases in some places while it decreases in other locations. Taking São Paulo as an example, the urban population decreased around the city center and urban core area, while it increased in the urban fringe and suburban areas (Figure 6a), reflecting the process of suburbanization [62]. Mexico City, Buenos Aires, and Santiago also experienced the transformation of population from the city center to suburban areas, but with different characteristics (Figure 6b,c,h). In Mexico City, urban population transferred from the east to the west during 2000–2014 (Figure 6b). Although the central part of Buenos Aires had an increased population, the urban population obviously decreased near the city center, forming a doughnut with a reduced population around the center (Figure 6c). The urban population change in Santiago shows the strongest heterogeneity, which declined in the main corridors passing through the city center. Different from the other four cities, there is no obvious decline in population around the city center in Bogotá (Figure 6g).

Generally, the expansion of urban land shares a similar pattern as seen in the population growth. The new built-up areas mainly occurred at the urban fringe and suburban areas, while a limited newly built-up area was found around the city center (Figure 6d–f,i,j). In São Paulo, the urban expansion hotspots are located in the east and west from 2000 to 2014 (Figure 6b). The urban land spread evenly around the city center in Mexico City, particularly in the north and southeast (Figure 6e). As a coastal city, the new urban land can only spread to the inland side of Buenos Aires and most of these lie along main roads (Figure 6f). Bogotá shows a massive urban expansion northwest of the city center (Figure 6i). The urban expansion surrounded the city center of Santiago with more new urban land located in the northwest of the city (Figure 6j).

**Figure 6.** Spatial distributions of population changes ( s **a**–**c**,**g**,**h**) and built-up area changes (**d**–**f**,**i**,**j**) from 2000 to 2014 in five Latin American cities. Only three types of roads (trunk, primary, motorway) were preserved from the OSM road net (https://www.openstreetmap.org) (accessed on 20 April 2021).

The frequency distributions of changes in population and built-up area from 2000 to 2014 in 1 km grid squares are shown in Figure 7a,b, and their correlations are presented in Figure 7c. Population and built-up areas remain unchanged in most grids during 2000–2014. The mean values of population change in the 1 km grid are 563 persons (São Paulo), 536 persons (Mexico City), 518 persons (Buenos Aires), 944 persons (Bogotá), and 200 person (Santiago). The mean values of changes in the built-up areas using the 1 km grid are 2.49 hm<sup>2</sup> (São Paulo), 9.88 hm<sup>2</sup> (Mexico City), 4.64 hm<sup>2</sup> (Buenos Aires), 1.96 hm<sup>2</sup> (Bogotá), and 3.15 hm<sup>2</sup> (Santiago). From the mean values for each grid, Bogotá experiences the highest population increase but with the least expansion in use of urban land, verifying its high population density (Tables 1 and 2) and compact urban form. Scatter plots show that the population change and the built-up area change are positively correlated (Figure 7c).

**Figure 7.** Frequency distribution and correlation analysis between the population growth and the urban land expansion from 2000 to 2014 in five Latin American cities. (**a**) Frequency of population change; (**b**) Frequency of built-up area change; (**c**) Scatter plots of population change and built-up area change in each 1 km grid.

#### **4. Discussion**

The city is composed of different spatial units. In addition to administrative divisions, the two most commonly used units in urban studies are concentric-rings and grids. Concentric-ring analysis, also called gradient analysis, is developed based on the monocentric model of cities, which does well in measuring macro pattern (urban-rural gradient) of urban expansion and urban form. Grid-based analysis has advantages in portraying the local microscopic dynamics of urban expansion. Grid-based analysis is usually used to build correlations between urban land and population changes. Urban expansion is a process from micro dynamics to macro patterns. Every land use change occurred in a local area, but the accumulation of local micro-processes shaped the macro pattern of urban expansion. The combined use of concentric-ring and grid-based analysis can measure urban expansion from both macro and micro perspectives. Although the concentric-ring

analysis and grid-based analysis have been used in previous studies, they are all used in a single way. We propose for the first time that these are two different strategies for measuring urban expansion.

We combined these two approaches to quantify urban expansion for five major Latin America cities from 2000 to 2014. The urban land density (built-up density) in concentric rings decreases from the city center to suburban areas, presenting an inverse S-shape (Figure 4). Partitioning concentric rings into different sectors can effectively quantify the directional heterogeneity of urban expansion (Figure 5). The grid-based analysis particularly presents the process of suburbanization where population transferred from central areas to suburban areas from 2000 to 2014 (Figure 6). Meanwhile, the newly added urban land mainly expands in the urban fringe and suburban areas to accommodate the increasing population there (Figure 6).

Parameters of the inverse *S* curve reveal the urban extents and compactness of urban form (Figure 4, Table 2). Although São Paulo has the largest built-up area, Buenos Aires has a larger urban extent than São Paulo. Buenos Aires experienced the most dispersed urban form, while the urban form is the most compact in Bogotá. The compactness of the urban form quantified by the inverse S curve is consistent with urban population densities that is the highest in Bogotá but the lowest in Buenos Aires (Table 1). The calculation of population change and built-up change using the grid-based analysis also confirms that the compact growth in Bogotá, where the urban population averagely increase by 944 person at a cost of 1.96 hm<sup>2</sup> for each 1 km grid during 2000–2014 (Figure 7).

In the global context, the speed of urban land expansion in Latin American cities is relatively low. The intensity of the urban expansion is reflected by the gaps between the two fitting curves using the inverse S curve. The gaps between the two curves in 2000 and 2014 are very close and even overlapped in the five major Latin American cities (Figure 4). Not to mention the fast urban expansion in China and other countries in Asia and Africa, even the expansion of urban land in the United States and Europe is greater than in Latin America [19,44,45,63]. On the other hand, the urban form of Latin American cities is more compact than for cities in other regions, which is quantified by the *α* parameter of the inverse S curve. The *α* parameter for the five major Latin American cities varies in 3.39–4.49, while that for Chinese and European cities is around 2.5, and is less than 2 for cities in the United States for the same two years (2000 and 2014) [6,20,44]. Many Latin American cities grow in rugged topographies which constrains expansion, resulting in the overall compact urban form [43].

The distance-decay of urban population densities from the city center has long been revealed in urban geography and urban economics [64]. Classical models include the Clark's negative exponential model [64], the Batty's inverse power function [65] and many other models [66]. The urban land density, defined as the proportion of built-up areas to the buildable land in concentric rings, also declines with distance to the city center, but there are limited quantitative models to measure this process [28]. Urban land density declines slowly around the city center followed by a quick decline outward, and then declines slowly again in the urban periphery, showing an inverse *S* shape as a whole [19]. Inspired by the widely used sigmoid function with an *S* curve, Jiao (2015) proposed an inverse *S* curve to quantify the distance-decay of urban land density from the city center [19]. The urban land density function (inverse S curve) has been not only used to quantify past urban expansion but also to guide the spatial allocation of future urban land, which has potential applications in urban planning [67]. Recently, a geographic micro-process model was proposed to explain why the distance-decay of urban land density presents an inverse S shape [20].

This study also has limitations. This study only quantifies the growth of cities from a spatial perspective, neglecting the underlying driving forces of urban transformation [25]. Secondly, we chose the city center based on the road network and we only identified one city center for each city. With the development of cities, those large cities tend to be polycentric. There are slight bumps of urban land densities around 12 km from the

city center in Buenos Aires (Figure 4). These bumps are closely related to the polycentric structure of cities, which can be controlled using polycentric scenarios in concentric ring partitioning [19,45]. In addition, the intervals (1 km, 2 km, and so on) in concentric-ring analysis and the size of grids (1 × 1 km, 2 × 2 km, and so on) have an effect of scale [68], which is not further discussed in this study.

#### **5. Conclusions**

This study contributes to the characterization of urban expansion by summarizing two approaches: Concentric-ring analysis and grid-based analysis. Concentric-ring analysis is developed based on the monocentric model of cities and it is easy to present the distance decay of urban elements from the city center to suburban areas. Grid-based analysis partitions a city into regular grids, which reflects local dynamics of urban growth.

Taking five major cities in Latin America as examples, this study combines concentricring analysis and grid-based analysis to quantify their urban expansion and population growth from 2000 to 2014. Urban land density in concentric rings declines with the distance from the city center. The inverse *S* curve not only can well fit the distance decay of densities but also reflects disparities in the urban extent and compactness of the urban form. Grid-based analysis characterizes the transformation of population from central areas to suburban areas. It is suggested to combine concentric-ring and grid-based analysis to fully quantify urban expansion from both perspectives of macro patterns and micro dynamics in other global cities.

**Author Contributions:** Conceptualization, G.X. and S.W.; methodology, S.W., T.D., and G.X.; software, S.W.; data curation, S.W.; writing—original draft preparation, S.W. and G.X.; writing—review and editing, N.S.S., T.D., G.X. and Y.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources (KF-2019-04-036).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data used in this study is publicly available. Land use maps retrieved from Landsat images are downloaded from http://www.atlasofurbanexpansion.org/ (accessed on 20 April 2021). The spatial distributions of population are downloaded from https://landscan.ornl.gov/ (accessed on 20 April 2021).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Exploring the Patterns and Drivers of Urban Expansion in the Texas Triangle Megaregion**

**Jiani Guo and Ming Zhang \***

School of Architecture, University of Texas at Austin, Austin, TX 78712, USA; jiani\_guo@utexas.edu **\*** Correspondence: zhangm@austin.utexas.edu

**Abstract:** As the world becomes increasingly urbanized, it is vital for planners and policy-makers to understand the patterns of urban expansion and the underlying driving forces. This study examines the spatiotemporal patterns of urban expansion in the Texas Triangle megaregion and explores the drivers behind the expansion. The study used data from multiple sources, including land cover and imperviousness data from the National Land Cover Database (NLCD) 2001–2016, transportation data from the Texas Department of Transportation (TxDOT), and ancillary socio-demographic data from the U.S. Census Bureau. We conducted spatial cluster analysis and mixed-effect regression analysis. The results show that: (1) urban expansion in the Texas Triangle between 2001 and 2016 showed a decreasing trend, and 95% of the newly urbanized land was in metropolitan areas, especially at the periphery of the central cities; (2) urban expansion in non-metropolitan areas displayed a scattered pattern, comparing to the clustered form in metro areas; (3) the expansion process in the Texas Triangle exhibited a pattern of increased development compactness and intensity; and (4) population and economic growth played a definitive role in driving the urban expansion in the Texas Triangle while highway density also mattered. These results suggest a megaregion-wide emerging trend deviating from the sprawling development course known in Texas' urban growth history. The changing trend can be attributed to the pro-sustainability initiatives taken by several anchor cities and metropolitan planning agencies in the Texas Triangle.

**Keywords:** megaregion; urban expansion; spatiotemporal patterns; driving forces; the Texas Triangle

#### **1. Introduction**

Urbanization has long interested academia, policy-makers, and international agencies. One important aspect of urbanization pertains to urban land expansion. Urban land expansion (or urban expansion in short) is a process of creating urban land for the needs of urban population and activities [1]. According to the U.S. Census Bureau, about 80% of Americans lived in urban areas in 2018. Urban expansion is often accompanied by many ecological and environmental challenges [2], for example, ecosystem damage, traffic pollutions [3], climate change [4], and resource depletion. These challenges also adversely affect people and environment in both urban and rural areas [2]. Furthermore, massive and aggressive urban expansion has resulted in worsening social issues such as inequality, urban and rural poverty, and housing unaffordability [5].

There are many different perspectives to understand urban expansion. The neoclassical perspective of urban expansion emphasizes the role of free market in deciding the land to be developed for urban functions. This perspective holds that land price, transportation cost, income, and population distribution are predominant driving forces of urban expansion [6]. Researchers in this domain have developed sophisticated statistical models to explain and quantify the extent to which these forces drive urban expansion. On the other hand, the institutional perspective pays close attentions to the importance of institutional factors such as land use control, capital investments, and organizational capacities in the urban expansion process [7].

**Citation:** Guo, J.; Zhang, M. Exploring the Patterns and Drivers of Urban Expansion in the Texas Triangle Megaregion. *Land* **2021**, *10*, 1244. https://doi.org/10.3390/land 10111244

Academic Editors: Iwona Cie´slak, Andrzej Biłozor and Luca Salvati

Received: 5 October 2021 Accepted: 10 November 2021 Published: 13 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Common types of driving forces for urban expansion include changing geographic environment, economic development, population growth, technological advance, and public policies [8–12]. Geographic models and location choice theories have been developed and advanced widely in geography and urban economics. Geographers believe that humans tend to move to flat and warm places of rich and easily accessible resources. Location choice theories in urban economics state that industries would choose a location to minimize production costs and maximize profits [8]. In combination, urban expansion is most likely to happen in places satisfying both habitation preferences and economic wellbeing [13].

Economic development and its relationship to urban expansion has long been investigated. Jones and Kone found the positive relationship between per capita income and urbanization in the US, in late 20th century [14]. Lately, research also confirmed this relationship in other country settings. For instance, a study by Zeng et al. on the expansion in Wuhan, Hubei Province, China showed that gross domestic product (GDP) positively correlated with urban expansion at both micro and macro scale [9]. Scholars have also found that built environmental factors such as the distance to employment centers and/or major facilities (e.g., schools and hospitals) and the existing transportation network are also key contributors to urban expansion. Wang and Zhou used remote sensing data to fit logistic regression models to explore the urban expansion in Beijing-Tianjin-Hebei megaregion in China from 1984 to 2010 [15]. They found that "both local and tele factors statistically significantly affected the urban expansion process while the local factors played a relatively prominent role".

Public policies and governmental control play an essential role as well in affecting urban expansion outcome. One study in Puerto Rico has shown that "the ineffective plan of land development has left a high degree of urban sprawl in 40% of the island, where cities and towns appear typically surrounded by sprawl" [16,17]. Pham et al. discussed the different policy influences on urban expansion in four different cities worldwide [11]. They thought Shanghai's urban expansion patterns followed the policy guide of transition from mono-centric to multi-centric megaregion to decentralize the population and economic activities purposed by China's local and central government. With the continuous expansion of urban land, however, potential side effects emerge. To fight against the negative externalities, many local and federal governments have imposed restrictions on urban expansion. For example, the urban growth boundary initiated in 1979 in Portland, Oregon, was designed to limit urban development for resources. Research on this policy mainly focuses on measuring urban form [18] regarding urban sprawl and housing density. However, the urban expansion process under this particular urban growth boundary policy in the region is neglected. Several studies outside the U.S investigated the urban expansion patterns under local government policy. For instance, in Japan, the City Planning Act, which was promulgated in 1968, controlled the urban expansion, and their research confirmed the most urban expansion patterns only happened in limited places.

The United States has a long history of regional planning [19]. Extensive urban sprawl happening during the post-WWII development in the United States and many other countries have raised increasing concerns over the negative societal and environmental consequences [20]. Actions to counter sprawl have been taken, as some studies have found that urban expansion in major metropolitan regions has become more aggregated rather than ceaselessly expanding outwards [21]. Recent interests in megaregions call for improved understanding of urban expansion from a megaregional perspective, which motived this study.

With the rapid and foreseeable expansion trend, it is an urge for planners and policymakers to accommodate the shifting needs and to cultivate efficient land use via updated knowledge learned from analyses involving up-to-date data and comprehensive methods. As advocated in the planning field, managing urban expansion can be one key to balance sustainability's 3E triangle (i.e., equity, environmental protection, and economic development) and achieving sustainable development [22]. Thus, to generate a more

sustainable outcome, policy-makers should better understand the process and impacts of urban expansion and incorporate the findings in their policy guidelines. The Texas Triangle megaregion is one of the most populous and fast growing megaregions in the United States. Known for its affordable land price and business-friendly environment, the Texas Triangle is the future home to many major companies and populations [23]. Based on the context, we used remote sensing and U.S. census data to answer the following two questions:


To answer those two questions, we first performed geospatial analysis to visualize the changing expansion patterns in the study area from 2001 and 2016 and analyzed the clustering effect during the period. Then, we fit a mixed-effect regression model to explore the relationship between the expansion intensity and socio-economic, transportation, institutional, and location factors. The findings of the study are expected to inform policy-making and strategic transportation investments for sustainable regional development.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The Texas Triangle is one of the eleven megaregions in the continental U.S., identified by researchers from the University of Pennsylvania with RPA and the Lincoln Institute [24]. The megaregion lies within Texas and geographically encompasses four major metropolitan areas: Austin, Dallas-Fort Worth, Houston, and San Antonio. The Texas Triangle is connected by Interstate 45 (I-45), Interstate 10 (I-10), and Interstate 35 (I-35) (Figures 1 and 2). Most places in the Texas Triangle megaregion have a flat terrain while the west is hilly with elevation below 500 m. The climate in central Texas (including Austin, Waco and San Antonio) is semi-arid with average yearly precipitation from 530 mm to 890 mm [25]. The eastern region of Texas which is within the humid subtropical climate zone (including Dallas and Houston) has more than 1500 mm of annual precipitation.

We follow the definition of the Texas Triangle by Butler et al. [27] and Zhang et al. [28] with minor modifications. Using county as the geographic unit of analysis, megaregion is predominately defined by its economic and transportation connectivity, ecological and cultural similarity [28]. We replaced Delta County with Burnet County, in the original definition in Butler et al. study. The reason for adding Burnet County is concerning its inclusiveness in the Capital Area Metropolitan Planning Organization (MPO). Delta county is deleted because of its remote distance to the major highway and is not included in any MPO. Figure 2 presents a total of 66 counties and principal metropolitan areas, cities, and highways in the Texas Triangle megaregion.

The Texas Triangle had a population of over 21 million in 2018. Specifically, the Triangle megaregion has five of the top 20 most populous cities (Houston, Dallas, San Antonio, Austin, and Fort Worth) in the country. Moreover, there are four major Corebased Metropolitan Statistical Areas (CMSA) in this megaregion; they are Dallas-Fort Worth-Arlington, Houston-The Woodlands-Sugar Land, San Antonio-New Braunfels, and Austin-Round Rock.

(**b**)

**Figure 1.** The Texas Triangle. (**a**): The location of the Texas Triangle in the U.S.; (**b**) the location of the Texas Triangle in the State of Texas. The elevation information is from the Texas Water Department Broad [26].

**Figure 2.** The Texas Triangle.

The Dallas-Fort Worth-Arlington MSA is the most populous metropolitan area in Texas and the fourth populous metropolitan area in the nation. It consists of 11 counties and a total area of 9286 square miles. This metropolis is home to 25 Fortune 500 companies, only behind New York City and Chicago. Houston-The Woodlands-Sugar Land is the second largest MSA in Texas and fifth most populous MSA in the U.S. This MSA includes nine counties (Harris, Fort Bend, Montgomery, Brazoria, Galveston, Liberty, Waller, Chambers, and Austin County) with a total area of over 10,000 square miles. Besides, this metropolitan area, is one of the fastest-growing MSA in the country. San Antonio-New Braunfels is an 8-county metropolitan area (Atascosa, Bandera, Bexar, Comal, Guadalupe, Kendall, Medina, and Wilson County), which covers a total area of 7387 square miles. As a famous historical city, the city of San Antonio is one of the top tourist cities in the U.S. Lastly, the Austin-Round Rock MSA includes six counties, Travis County, Bastrop County, Williamson County, Caldwell County, and Hays County. The Austin-Round Rock MSA is another rising metropolitan in which the population has increased from less than 300 thousand in 1970 to over 2 million in 2016 [29]. Austin was established in 1839 as the capital city of the Republic of Texas. The city is now a major education, technology, and economic center in the state, home to a flagship public university and world-renowned technology companies such as IBM (Endicott, NY, USA), Dell (Austin, TX, USA), and Apple (Los Altos, CA, USA).

#### *2.2. Data*

First, we retrieved the developed land from the National Land Cover Dataset (NLCD), U.S. Geographic Survey (USGS), to quantify the urbanized area. NLCD is a multi-year pre-prepared remote sensing data with a resolution of 30 m. The performance of the developed strategies and methods were tested in twenty World Reference System-2 path/row throughout the conterminous U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was achieved for all tested areas and all years [30]. This remote sensing data has been used as a valuable data source in urban expansion research because of its broad and consistent area coverage and the virtue of being repeatedly updated regularly [31]. Rifat and Liu used NLCD and Coastal Change Analysis Program (C-CAP) datasets to study the urban expansion in the Miami Metropolitan area [32]. Terando et al. also used NLCD data to predict future urban sprawl in the Southern megapolis region [33]. In this research, the four categories of developed areas are treated as the urbanized area: developed open space, developed low density, developed medium density, and developed high density. The urbanized areas are calculated as the sum of the four types of developed land cover.

Moreover, we used the imperviousness data layer to retrieve the weighted urbanized area. Imperviousness data present impervious urban surfaces, representing the percentage of the developed surface. In the NLCD imperviousness dataset, each pixel is from 0% to 100%, where 80% to 100% pixels were classified as high intensity developed area. Several studies also used impervious information to measure the intensity of urban land [21,34]. We considered each pixel's imperviousness as an urbanized area's intensity weight. The weighted urbanized area is calculated as Equation (1):

$$\text{Weighted urbanzized area} = \sum \text{pixel}\_{i} \ast \text{impreviousness} \tag{1}$$

Because of the data availability, we used the NLCD data layers for the years of 2001, 2006, 2011, and 2016. Other data in this research correspond to the four years.

Highway data were collected from the Texas Department of Transportation (TXDOT), Roadway Inventory 2019. This dataset provides all the roadways records in Texas up to 2019, including length, width, road type, start date, and traffic volume. We selected major highways (including interstate highway, state highway and U.S. highway) and calculated their density at the county level as the transportation indicator. The highway density was measured as the total length of the highway dividing the total area of each county.

To calculate the indicator for innovations and technological advances, we used the patent data which were collected from the U.S. Patent and Trademark Office (USPTO). USPTO posts the number of patents that were registered in the corresponding year and registration county. We retrieved county-level patent data in corresponding years as an indicator of technology level in those years. Finally, other ancillary social-demographic data were from the Bureau of Economic Analysis (BEA), including population, employment, and GDP. Table 1 presents the descriptive data of urban land change and the key drivers by major MSAs in the Texas Triangle from 2001 to 2016.

#### *2.3. Methods*

To answer the first research question, we conducted an Anselin Local Moran's I cluster and outlier analysis. For the second research question, we performed a mixed-effect regression analysis to determine the factors related to the Texas Triangle's urban expansion. Detailed descriptions of the methods are as follows.


**Table 1.**Descriptive data in the Texas Triangle, 2001–2016.

#### 2.3.1. Anselin Local Moran's I Cluster and Outlier Analysis

We identified hot-spot clusters and spatial outliners of urbanized land at the Census tract level through the Anselin Local Moran's I statistic [35]. This method is widely used in many fields, such as economics [36], demographics [37], and geography [38]. The Anselin local Moran's I cluster and outlier analysis (cluster analysis) is adopted because of its ability to capture the spatial patterns in not only their general but also abnormal trends. We used this method in our study to categorize four types of spatial clusters of urban expansion in the Texas Triangle. If the Local Moran's I test statistic turns out to be positive, this area belongs to a statistically significant cluster of either a high value (a high-high cluster) or a low value (a low-low cluster). On the contrary, if the test statistic is negative, this area is an outlier of either high value surrounded by low-value areas (a high-low outlier) or otherwise (a low-high outlier). We tested the absolute increase area of urbanized land at the census tract level in 2001 to 2006, 2006 to 2011, and 2011 to 2016 and to see if a place is the hotspot of urban expansion, or if this place is the outlier with the abnormal increasing urbanized land while its surrounding areas are not.

#### 2.3.2. Regression Analysis of the Driving Forces of Urban Expansion

We estimated mixed-effect regression models to measure the relationship between urban expansion and its potential driving forces. The mixed-effect regression model can cancel out the unobserved the error from different geographic entities and other potential error. The time variables were fixed to test its relatively growth in different periods.

The dependent variable urban expansion is measured by the percentage of urbanized area and the percentage of the weighted urbanized area in the county. Besides selecting the urbanized area, we added the weighted urbanized area to model the intensity growth in this megaregion. The comparison of absolute urbanized area and weighted urbanized area can depict a more comprehensive urban expansion process beyond the horizontal land cover changes.

To model the urban expansion, we selected six widely discussed variables in the literature that can capture the most context at a higher level, such as the megaregion level used in this study. The details and rationales are elaborated and explained in the following paragraphs. Independent variables were categorized into five types, as is shown in Table 2.


**Table 2.** Selected major drivers of urban expansion.

We selected the population as one of the major predictors. It is indubitable that population growth and land expansion are two inseparable aspects of the urbanization process [39]. Research has shown that population migration from rural to urban areas is a major driving force of urban expansion [40]. Therefore, it was our expectation that the most important driving force of urban expansion was population growth, measured as the total population in the county in the corresponding years.

Second, we included the economic indicators in the model because urban economists think urban expansion results from the market and economy agglomeration and expansion [6]. Economic development and increasing economic activities have accelerated the urban expansion process in recent decades. On the other hand, urbanization also may, in return, promote economic development. The economic advantages in the urbanized

area further attract more population and migration from rural to urban areas. Hence GDP and the number of employments widely serve as two indicators to measure the economic development in different counties.

The number of patents in the county was selected as an indicator of technology innovation. Technology development is wildly considered a significant factor of the prosperity of a region. The first technological revolution in the later 18th century in the United Kingdom is also the time the urbanization began. The second and the third technological revolution in the U.S. accelerated the urbanization process and urban expansion. From industrialization and informatization, technology innovation is always one of the central forces pushing the urban expansion process [41]. Friedman once argued that the technology is one of the reasons that the geographical location is less important nowadays [42]. In this research, the number of patents in the county is used as a proxy of technology innovation, as was used in Florida's research [41].

Highway density was chosen to represent the capital investments in transportation infrastructure. Early from the bid-rent theory, the distance to major transportation facilities is essential to location choice [43]. Later on, Dr. Adam's four stages model further emphasized how transportation infrastructure can shape the urban form and lead to urban expansion in different phases [44]. Moreover, transportation density is also an important indicator of built environments, influencing the urban expansion process. Therefore, the highway density is calculated to measure the supply of transportation infrastructure in each county.

The institutional perspective focuses on institutional or municipalities' role in the urban expansion [7]. The governmental policy is another factor for urban expansion. Due to the intricate and fragmented municipalities and governmental systems at the megaregion level, we considered being in a metropolitan area an institutional factor to investigate whether a county belongs to a larger administrative unit will make a difference in their urban land expansion. A metropolitan statistical area, defined by the U.S. Office of Management and Budget, is region with a principal city and its periphery containing more than 50,000 population. A micropolitan statistical area similarly is a place with population between 10,000 and 50,000.

The initial status (in the year 2001) of the urbanized area in each county varies; however, the growth rates of urbanized areas are relatively similar among different counties. Therefore, we did not use a growth model because of the low variation in slopes. In this case, we fixed the time effects, setting the initial year, 2001 as the baseline, while setting the geographic entities, that is each county, as random effects. The choice and form of variables are presented in Table 3.

The form of the equation is shown in Equations (2) and (3):

lglandpct<sup>i</sup> = β0i + β1i∗metro + β2i∗lgpop<sup>i</sup> + β3i∗lgjob<sup>i</sup> + β4i∗cpatent<sup>i</sup> + β5i∗lggdp<sup>i</sup> + β6i∗lghwden<sup>i</sup> + β7i∗year2006i + β8i ∗year2011<sup>i</sup> + β9i∗yeari<sup>2016</sup> + U<sup>i</sup> + ε<sup>i</sup> (2)

$$\begin{array}{c} \text{lgimpact}\_{\text{i}} = \beta\_{0\text{i}} + \beta\_{1\text{i}} \ast \text{metro} + \beta\_{2\text{i}} \ast \text{lgpop}\_{\text{i}} + \beta\_{3\text{i}} \ast \text{lgjob}\_{\text{i}} + \beta\_{4\text{i}} \ast \text{spatert}\_{\text{i}} + \beta\_{5\text{i}}\\ \ast \text{lgdp}\_{\text{i}} + \beta\_{6\text{i}} \ast \text{lghwden}\_{\text{i}} + \beta\_{7\text{i}} \ast \text{year}\_{2006\text{i}} + \beta\_{8\text{i}}\\ \ast \text{year}\_{2011\_{\text{i}}} + \beta\_{9\text{i}} \ast \text{year}\_{2016\_{\text{i}}} + \mathsf{U}\_{\text{i}} + \varepsilon\_{\text{i}} \end{array} (3)$$


**Table 3.** Description of the variables in the mixed-effect regression.

#### **3. Results**

#### *3.1. Spatiotemporal Patterns of Urban Expansion in the Texas Triangle*

Figure 3 shows the change in urbanized area from 2001 to 2016 in the Texas Triangle. The newly developed urban land is mainly concentrated in the periphery of the major metropolitan area evident in the figure. The newly developed urban area in other counties presents scattered patterns. Additional cluster analysis further confirms that there are no cluster effects in those counties.

**Figure 3.** Urbanized Area in the Texas Triangle.

Figures 4 and 5 show the spatial and temporal patterns of urban expansion in the Texas Triangle during the three time periods. The results, first, illustrate higher growth rates in metropolitan counties than other counties. From 2001 to 2016, the urbanized area has increased by 2887 km<sup>2</sup> , while 95% of those expansions occurred in metropolitan areas. Moreover, Figure 5 shows the decreasing growth rate over time in the Texas Triangle.

(**c**)

**Figure 4.** Maps of Urbanized Land Growth Rate in the Texas Triangle from 2001 to 2016: (**a**) 2001–2006; (**b**) 2006–2011; (**c**) 2011–2016.

**Figure 5.** Urbanized land growth rate in different geographic area in the Texas Triangle.

Maps of growth spatial clusters and outliers in the megaregion (Figure 6) illustrate the spatial characteristics of urban expansion from 2001 to 2016 in the Texas Triangle. To start with, counties in four major metropolitan areas presented more high-high clusters of urban growth than micropolitan counties and other counties. The four major MSAs commonly exhibited patterns from principal cities outward: low-low cluster, low-high outlier, high-high cluster, and high-low outliers. This pattern was location-irrelevant in all periods. Specifically, central counties in major metropolitan areas, i.e., Travis county in Austin MSA, Dallas and Tarrant County in Dallas MSA, Bexar County in San Antonio MSA, and Harris County in Houston MSA, had a relatively low increase rate. Principal cities, Austin, San Antonio, Houston, Dallas, and Fort Worth, all presented significant low-low growth clusters, while the high-high cluster aggregated in slightly different localities at different time intervals. However, the urban expansion has an extraordinary intensity in the periphery area around the central county of metropolitan areas like Fort Bend County in Houston metropolitan, Rockwall County in Dallas metropolitan, and Williamson county in Austin metropolitan. Moreover, the urbanized land growth rates decreased over time, and the growth moves farther away from the principal cities. Interestingly, many census tracts with comparatively high growth rates are on the north side of metropolitan areas.

Besides the commonalities, four major metropolitan areas exhibited different urban expansion patterns. The Houston MSA shows relatively consistent and intensive expansion patterns in the outer ring on the north and east sides. This pattern might relate to its adjacency to the ocean to the south. Whereas the San Antonio MSA presents a relatively low expansion pace overall, and those expansions are concentrated on the north side. The Austin MSA has the fastest growth rate in the Texas Triangle. While development on the north Austin MSA dated back from 2001, the south side started to consume significantly more land as urbanized land from 2006. Lastly, the Dallas MSA has a high growth rate in the periphery places around Dallas and Fort Worth from 2001 to 2006. Then, from 2006 to 2011, the urbanized land grew primarily on the north and southwest in the metropolitan region. In the last time period, the urban land mainly expanded only on the north side.

In other smaller metropolitan areas, urban expansion patterns are slightly different from the major ones. Growth rates in those areas are generally slower, except for the College Station metropolitan. Besides, there are no prominent spatial clusters of high or low values in urbanized land growth. It is worth noting that, in the connecting MSA counties between four major MSAs, the urban expansion patterns are different over time. Specifically, counties between Dallas and Houston experienced a sizeable urban expansion

in the first two periods, whereas more expansion was found between Austin and San Antonio in the third.

**Figure 6.** Spatial cluster and outliners of newly developed urbanized land (**a**) 2001–2006; (**b**) 2006–2011; (**c**) 2011–2016.

However, in non-metropolitan counties, the scale of urban expansion is relatively small. Moreover, from the cluster analysis, there are no significant urban expansion clusters in all periods, indicating a scattered expansion pattern in those counties.

To sum up, the newly developed urbanized land concentrates mainly on the periphery of core cities of major metropolitan areas. Overall, the urbanized land in the metropolitan area has expanded to suburbs, but the growth rate has declined over time. Even though more undeveloped land has changed to urbanized land during the whole period, the results above show that the expansion happened in a more aggregated manner instead of randomly sprawling in the metropolitan counties. Those patterns accord with the urban expansion patterns in major urban areas in the US Great Plains from 2000 to 2009 [45] with showing a compact development trend. Comparably, the growth in non-metropolitan counties is slower and more scattered.

#### *3.2. Regression Results*

Table 4 presents the result of the regression analysis. There are in total 262 observations, representing 66 counties in 4 years (the Milam County had no highway in 2001 and 2006 and therefore omitted in the regression models). The overall r<sup>2</sup> in the two models are 0.86 and 0.66, which explains most variations by the models.


**Table 4.** Results of the mixed-effect regression.

Note: significance level: \*\*\*: *p* < 0.001; \*\*: *p* < 0.01; \*: *p* < 0.05.

First of all, the models coincide with previous literature and show that population growth statistically significantly leads to greater expansion. Surprisingly, albeit 95% of the expansion in the Texas Triangle happened in the metropolitan areas during the entire period, being a metropolitan county shows no statistically significant advantages than other counties in both models. The result implies that metropolitan setting does not explain the trend of urban expansion in the Texas Triangle megaregion.

In terms of the time variable, compared to 2001, while controlling for other factors, there is no significant growth of the urbanized area from 2001 to 2016. However, the weighted urbanized area shows totally different results, and all-time variables are highly positively correlated to weighted urban areas. Unlike economic development or GDP, where we assume there might be natural growth because of productivity or efficiency improvement, the total urbanized area shows no such natural growth in the Texas Triangle from 2001 to 2016 while controlling population, economic development, and other factors. Nevertheless, it is worth noting that the intensity of urban areas has such growth from 2001 to 2016 when controlling other variables. That is to say, from 2001 to 2016, the urban expansion process in the Texas Triangle is more compact rather than low-density development. From this aspect, it is possible to control the urban growth if policies are controlling for population and transportation infrastructure. Moreover, it shows the results of promoting compact development.

As for economic factors, the models reveal a complicated relationship to urban expansion. On the one hand, economic development requires land investment as space and capital. On the other hand, economic activity agglomeration is an important driving force to a greater urban expansion. The model results show that, while employment has no significant statistical relationship to urban expansion, GDP positively influences the urban expansion process in the Texas Triangle. In contrast, the patent variable as a measurement of technology innovation shows no significant relationship to urban expansion in the Texas Triangle.

As much concern to urban transportation planners, the result shows that the highway density is highly positively related to an urbanized area. However, interestingly, the highway density presents no relationship to a weighted urbanized area. That means the highway density might influence the urban in changing non-urban land to urban land but has little relationship to its intensity. This result provides information for planners to rethinking the use the transportation infrastructure to guide future urban growth. Planners should also consider their role in compact urban development.

#### **4. Discussion and Conclusions**

This study examines the urban expansion pattern influenced by six major driving factors in the Texas Triangle Megaregion from 2001 to 2016. We first conducted a spatial cluster analysis to explore where the expansion occurred and its magnitude during the period. We then employed a mixed-effect model at the county level to explain the relationships between urban expansion and the focal driving forces, including affiliation to a metropolitan area, population, employment, GDP, technology innovation, and transportation infrastructure. The cluster analysis results show that the urban expansion rate displayed a decreasing trend in the Texas Triangle between 2001 and 2016; 95 percent of new development occurred within the Triangle's metropolitan areas. While clustering patterns varied between the metropolitan areas, the expansion occurred largely in the periphery of central cities. Contrastingly, the urban expansion in non-metropolitan counties was rather scattered. The mixed-effect modeling shows that population, GDP, and highway density were significant predictors of urban expansion.

Between 2001 and 2016, metropolitan areas in the Texas Triangle displayed different patterns despite a shared experience of overall urban expansion. In particular, the Dallas-Houston corridor area showed clustered growth in the periods of 2001–2006 and 2006–2011. This clustered growth pattern, however, did not occur in 2011–2016. The San Antonio-Austin corridor presented clustered expansion throughout the study period of 2001–2016. The two metropolitan areas have now become contiguous, prompting Texas DOT to coordinate joint planning efforts by their respective metropolitan planning organizations [46]. The finding accords with previous studies by demonstrating the heterogeneous expansion patterns at the periphery of large cities [47]. Amid shifting expansion patterns in the areas between large cities and metro areas, state or joint state efforts are necessary to foster cooperation beyond the municipal or agency's jurisdictional boundaries.

Metropolitan planning agencies can play an important role in leveraging regional resource distribution to guide urban expansion [32,48], despite that municipalities make local land use decisions. Improved coordination and cooperation between local and regional entities could lead to desired development outcome. For example, transit-oriented development (TOD) has been widely considered as a tool to facilitate smart urban growth [49]. Since regional transit lines typically traverse multiple municipalities, coordination between MPOs, transit agencies, and local communities is essential to implement TOD strategy at the regional scale. A best-practice example from the Texas Triangle exists from the Dallas region where NCTCOG (North Central Texas Council of Governments), DART (Dallas Area Rapid Transit), City of Dallas as well as other communities along DART routes have coordinated joint efforts to practice TOD in the region [50]. City of Houston initiative Livable Places echoes H-GAC's (Houston-Galveston Area Council) Livable Centers program to promote walkable places and TOD [51,52].

The second regression model estimated in this study considered imperviousness or development intensity, that is, the model of weighted urban land expansion. Adding intensity information into the urban expansion modeling resulted in the loss of statistical significance for the variable highway density. However, the predictor number of patents which was statistically insignificant in the first model, turned to be significant (*p* < 0.005). The contrasting results between the two models suggest that capital investments in highways tend to drive urban expansion horizontally, whereas innovations and technological advances likely push urban expansion vertically towards increased land use efficiency. As concerns over climate change and sustainability grow, local and state governments need to rethink about the conventional strategy of investing in highways to accommodate population and economic growth.

This study has several limitations, suggesting directions for future research. First, this study utilized data on land cover from satellite images, which provide very limited information on land uses for various urban functions. In future research, detailed land use information may be incorporated to allow analyses on variations of urban expansion by different functional types of land uses. Second, this study used imperviousness information from NLCD as a proxy for development intensity; the information provides a rather coarse measure that cannot adequately capture the variation of vertical urban expansion across cities and regions. Lidar data could be used to enhance this study with detailed urban form and vertical development characteristics [53,54]. Lastly, considering the vast area of the Texas Triangle, this study selected county as the geographic unit of analysis. The study findings and discussions are thus limited to the county level. There exist significant within-county variations that this study did not capture. Hence, the study can be refined with use of finer-scale data, for instance, at the census track or block group level.

Despite these limitations, the study results suggest a megaregion-wide emerging trend deviating from the sprawling development course known in Texas' urban growth history. The changing trend can be attributed to the pro-sustainability initiatives taken by several anchor cities and metropolitan planning agencies in the Texas Triangle. Future planning and policy-making efforts should foster this trend toward a sustainable megaregion.

**Author Contributions:** Conceptualization, J.G. and M.Z.; data curation, J.G.; formal analysis, J.G.; funding acquisition, M.Z.; investigation, J.G.; methodology, J.G.; visualization, J.G.; writing—original draft, J.G.; writing—review and editing, M.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by USDOT UTC Cooperative Mobility for Competitive Megaregions (CM2) (Grant #: 69A3551747135) and the Snell Endowment Grant from the University of Texas at Austin Center for Sustainable Development (CSD).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data are retrieved from The U.S. Census Bureau (https://www.census. gov/data.html/ accessed on 9 November 2021), Texas Department of Transportation (https://www. txdot.gov/inside-txdot/division/transportation-planning/roadway-inventory.html/ accessed on 9 November 2021), National Landcover Database (https://www.mrlc.gov/data/nlcd-land-coverconus-all-years/ accessed on 9 November 2021) and Bureau of Economic Analysis (https://www. bea.gov/data/gdp/ accessed on 9 November 2021), and U.S Patent and Trademark Office (https: //developer.uspto.gov/data/ accessed on 9 November 2021).

**Acknowledgments:** Thanks to CM2 and CSD for providing funding support to this research. We also want to acknowledge all colleagues, especially Shunhua Bai, in the School of Architecture, University of Texas at Austin for their insightful advice.

**Conflicts of Interest:** The authors declare no conflict of interest. We certify that this submission is original work and is not under review at any other publication. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**


## *Article* **Urban Innovation Efficiency Improvement in the Guangdong–Hong Kong–Macao Greater Bay Area from the Perspective of Innovation Chains**

**Wenzhong Ye <sup>1</sup> , Yaping Hu <sup>1</sup> and Lingming Chen 1,2,\***


**Abstract:** Against the background of globalization and informatization, innovation is the primary driving force for regional economic and social development. Urban agglomerations are the main body of regional participation in global competition, and promoting the construction of the Guangdong– Hong Kong–Macao Greater Bay Area is an important strategy for China's regional economic development. Aimed at the differences in location advantages among cities in the Guangdong–Hong Kong–Macao Greater Bay Area, based on the theory of innovation chain, we developed a three-stage model of "knowledge innovation-scientific research innovation-product innovation". A three-stage DEA model was used to measure the innovation efficiency of cities in the Greater Bay Area at different stages, and two progressive two-dimensional matrices are constructed to locate the innovation development of cities according to the efficiency value. The results show the following: 1 The overall innovation efficiency of the Greater Bay Area urban agglomerations gradually decreased in the process from knowledge innovation and scientific research innovation to product innovation, and the innovation efficiency among cities was unbalanced. 2 Shenzhen, Guangzhou, and Hong Kong all performed well in the whole innovation stage, while other cities in the Greater Bay Area showed weakness in innovation at different stages. Based on this, this paper puts forward relevant countermeasures and suggestions for promoting and optimizing collaborative innovation in the Greater Bay Area taking into account factor flow, industrial structure, and innovation network of urban agglomerations.

**Keywords:** innovation value chain; Guangdong–Hong Kong–Macao Greater Bay Area (GBA); innovation efficiency; urban agglomerations

#### **1. Introduction**

China's economy has moved from a stage of rapid growth to a new era of highquality development, and innovation is a significant engine for high-quality regional development. Urban agglomerations are concentrated areas of economy, technology, workforce, information, and a leading area of national technological innovation. In recent years, China has attached great importance to the building of regional innovation centers, with key cities such as Beijing, Shanghai, Guangzhou and Shenzhen as the core, forming representative regional innovation urban agglomerations in the Beijing–Tianjin–Hebei Region, the Yangtze River Delta, the Pearl River Delta and the Guangdong–Hong Kong– Macao Greater Bay Area, highlighting these major cities' function as innovative hubs and driving the sustainable development of local economy [1]. The Guangdong–Hong Kong– Macao Greater Bay Area (hereinafter GBA) is one of the most open and economically active regions in China and plays an important strategic role in China's overall development and global economic development. The Outline Development Plan for the Guangdong–Hong

**Citation:** Ye, W.; Hu, Y.; Chen, L. Urban Innovation Efficiency Improvement in the Guangdong–Hong Kong–Macao Greater Bay Area from the Perspective of Innovation Chains. *Land* **2021**, *10*, 1164. https://doi.org/ 10.3390/land10111164

Academic Editors: Iwona Cie´slak, Andrzej Biłozor and Luca Salvati

Received: 11 October 2021 Accepted: 29 October 2021 Published: 30 October 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Kong–Macao Greater Bay Area issued by China at the beginning of 2019 sets out the strategic positioning of the GBA to become an international innovation center with global influence. It stresses the need to deepen innovation cooperation in the GBA and build an open regional collaborative innovation community for integrated development [2].

As the innovation center in China, the GBA is a crucial spatial carrier leading regional innovation and high-quality development. Its innovation intensity determines the level of regional economic high-quality development. With significant connotations, urban innovation efficiency has become a typical index to evaluate regional innovation and high-quality development. Thus, at this stage, has the GBA collaborative innovation community achieved coordinated progress? How can the innovation efficiency of the GBA be improved? Therefore, we adopted the DEA model based on the perspective of the innovation value chain to study the innovation efficiency of cities in the GBA at the stages of knowledge innovation, scientific research innovation, and product innovation, and we propose paths for improving the innovation efficiency of each city to provide a reference for decision-making to accelerate the innovation-driven strategy of the GBA and build a world-class innovation bay area.

The structure of this paper is as follows: The first section is the introduction, which introduces the research background. The second section is the literature review, which summarizes the research results of previous scholars and has a systematic and comprehensive cognition of the issues discussed in this paper. The third section provides the theoretical mechanism, discusses the connotation of the innovation value chain and the two-dimensional innovation efficiency matrix. The fourth section introduces the empirical model and discusses the data source, variable selection and data processing. The fifth section introduces the result analysis. According to the efficiency value measured by the three-stage DEA model, the positioning of each city in the two-dimensional matrix of innovation efficiency is analyzed. The sixth section is the research summary and recommendations.

#### **2. Literature Review**

As for the research on innovation efficiency, innovation efficiency is an important index reflecting the relationship between innovation input-output and the operation level of the regional innovation system [3,4]. In the efficiency measurement method, the existing research adopts the Stochastic Frontier Analysis (hereinafter SFA) based on parameters and the Data Envelopment Analysis (hereinafter DEA) based on non-parameters [5–9]. In terms of regional innovation efficiency, earlier research mostly focused on the research of innovation efficiency and its influencing factors at the national level [10,11]. Some scholars take provincial samples as research units and use the SFA analysis method to measure regional innovation efficiency [12–14], while others use the DEA method to research interprovincial innovation efficiency [15–17]. The influencing factors of innovation efficiency mainly include human capital, economic development level and spillover effect [18,19], as well as the degree of marketization [20] and openness to the outside world [21].

The mode of the innovation value chain is an incremental process of multi-subject phased participation and continuous evolution of functional nodes. Its essence is the generation, transfer and diffusion of knowledge [22]. The core idea of the innovation value chain lies in the openness of innovation elements, the synergy of the whole operation and the added value [23]. Based on this idea, the innovation value chain can reflect the linkage process of innovation resources in an urban agglomeration to realize the added value of innovation value through the chain's activity structure. The interaction of different links in the innovation value chain affects the combination, flow and diffusion of innovation elements, and the coupling and synergistic development of each link contributes to the improvement of the overall innovation level and international competitiveness of urban agglomeration [24]. From the perspective of research, many scholars have also paid attention to the research on the phased efficiency of innovation, believing that the innovation process is divided into two stages: R&D of innovation results and application of innovation

results [25,26], and analysis of the efficiency of the two stages, respectively. Based on the perspective of the innovation value chain, the innovation efficiency of knowledge innovation, R&D innovation and product innovation in China has been analyzed by using the three-stage DEA model and selecting the appropriate promotion path according to the advantages and disadvantages of the innovation efficiency in different regions at different stages [27].

For the research on the innovation efficiency of urban agglomerations, Zhao (2018) adopted the DEA method to study the innovation efficiency of cities in the GBA and compared the scale efficiency and technical efficiency [28]. Sheng et al. (2019) used the SFA method and spatial econometric model to study innovation efficiency and spatial spillover effect of urban agglomerations in Beijing–Tianjin–Hebei, the Yangtze River Delta and the Pearl River Delta in China [29]. Ye and Xu et al. (2021) conducted a comparative study on the innovation efficiency and influencing factors of the three major urban agglomerations in eastern China through the construction of an input-output index system [30,31]. The Bay Area economy, represented by the GBA, is an advanced form of regional economic development [32]. The collaborative innovation of urban agglomerations and their innovation efficiency play a driving role in developing a regional economy. Because of the spatial characteristics, collaborative innovation linkages and influencing factors of innovation efficiency within the GBA urban agglomeration, some Chinese scholars adopted a fixed-effect stochastic frontier model, the DEA–Malmquist index, the grey correlation analysis method, social network analysis and negative binomial regression model to conduct research and discussion [33–35]. Some scholars also studied the characteristics and influencing factors of innovation efficiency in the GBA from static and dynamic perspectives [36].

In the existing studies, many scholars have conducted in-depth analyses on the innovation efficiency of urban agglomerations from different perspectives and using different methods, which can provide important theoretical support for the development of this paper. At the same time, however, the current research findings still have the following shortcomings. Firstly, most of the literature uses provincial data to measure innovation efficiency, focusing on examining the mechanisms for improving innovation efficiency at the national level for the region as a whole. However, there is less literature that examines the optimization of innovation efficiency across cities in urban agglomerations. Secondly, the existing literature is relatively limited in its study of the innovation process in urban agglomerations and its efficiency at different stages. In fact, collaborative innovation between urban agglomerations occurs not only between different cities at the same stage of the value chain, but also at different innovation stages in the same city. Finally, most existing literature discusses the efficiency of scientific and technological innovation, while scientific and technological innovation is only a part of innovation activities, and the two cannot be generalized.

In the background of the new era, the study of innovation efficiency of urban agglomeration and its promotion path need to be further studied. The main marginal contribution and innovation of this paper mainly include the following aspects. First of all, from the perspective of the innovation value chain, a three-stage analysis framework of "knowledge innovation–scientific research innovation–product innovation" is constructed to clarify the main components and relations of each stage of innovation value chain. Secondly, the innovation efficiency of different stages of the GBA is measured by the broad innovation input–output index, and the improvement path of the innovation efficiency of each city is proposed, which provides decision-making reference for accelerating the innovation-driven strategy of the GBA and developing the world-class innovation Bay Area. Eventually, the research results of this paper will also provide development ideas and countermeasures for promoting the innovative development and sustainable development of urban agglomeration around the world.

#### **3. Innovation Value Chain Theory and Innovation Path Setting**

*3.1. Innovation Process and Innovation Value Chain Theory*

Previous research regards the innovation process as a "black box". Innovation is an output process that is input by multiple factors and through multiple innovation activities. Scholars at home and abroad have carried out much work on the research of the innovation process, and they believe that innovation is a whole that is interlinked by several innovation activities. At the same time, innovation activities are composed of interrelated multi-links such as design, research and development, production, and sales.

The concept of the innovation value chain was proposed by Hansen and Birkinshaw (2007), who explained that the generation, transformation and dissemination of ideas in innovation are multistage and interrelated [37]. Roperd et al. believe that it is the process of knowledge acquisition, transformation and utilization [38]. From the perspective of globalization, Kramer (2011) determined the contents of the innovation value chain from value sharing [39]. Lee, J. (2012) extended the perspective of the innovation value chain to developing countries, and further analyzed and supplemented its application value [40]. According to the relevant literature, the innovation chain is market-oriented and based on the innovation needs of enterprises, and takes innovation theory as the core element [41]. The innovation value chain is a new theoretical edge concept that combines technology innovation theory with value chain theory [42].

Based on the decomposition of technological innovation links from the perspective of production, the innovation value chain is recognized as a typical three-stage structure, which is a chain structure pattern formed by multiple innovation subjects based on ordinary interests around the whole process of knowledge generation, research and development, large-scale production and commercialization. Technological innovation is a multi–stage and multi–factor value chain transfer process from the input of innovative elements to the output of innovative products, including three stages from the input of innovation to the condensation of innovative knowledge and then to the realization of innovative results. This concept has a similar meaning to the three stages of basic research, applied research and experimental development in the process of innovation in China's statistics, which can match the research on regional economic reality and innovation efficiency well. The innovation process should include three closely linked stages: knowledge innovation, scientific research innovation and product innovation [27], which promote and link each other. The purpose of knowledge innovation and scientific research innovation is for product innovation, and knowledge innovation is the theoretical basis of scientific research innovation and product innovation. At the same time, they are independent of each other; each is a new stage of innovation, and the whole process is cyclic. Following this research idea and drawing on the model ideas of Yu et al. [27], we constructed the innovation value chain mechanism as shown in Figure 1. This paper analyzes and discusses the innovation efficiency of the Guangdong–Hong Kong–Macao Greater Bay Area from the three stages of knowledge innovation, scientific research innovation and product innovation in the innovation process, so as to further explore the status quo of the innovation and development of the GBA in detail.

**Figure 1.** The three-stage operation mechanism of the innovation value chain in urban agglomerations.

#### *3.2. The Theoretical Model of Innovation Path Setting*

Cities in urban agglomerations show differences between the stages of efficiency of innovation and the three-phase contact operation mechanism of urban agglomeration of the innovation value chain. We constructed a two–dimensional innovation efficiency matrix of "knowledge–scientific research" innovation efficiency and "scientific research–product" innovation efficiency of the regional innovation urban agglomerations (as shown in the diagrams (a) and (b) in Figure 2) [27]. The average efficiency of cities at different stages can be divided into four efficiency combinations according to the value of the two efficiency dimensions. Different combination modes represent different efficiency states, thus providing a basic idea for the improvement of innovation efficiency in urban agglomeration. These four types of efficiency combination regions are, respectively, the combination of "one high and one low" represented by B and D, the combination of "double low" represented by C and the combination of "double high" represented by A. This division is convenient for cities in different quadrants when searching for appropriate theoretical reference paths to improve innovation efficiency according to their development conditions.

**Figure 2.** (**a**) Matrix of "knowledge–research" efficiency values; (**b**) matrix of "scientific research– product" efficiency values.

According to the diagrams (a) and (b) in Figure 2, this paper proposes the following three ways to improve the innovation efficiency: first, the breakthrough improvement path of unilateral supplement from B to A or D to A, which takes the link with low efficiency in one stage as the breakthrough point to improve the overall innovation efficiency on the premise of ensuring the high efficiency in the other stage. Second, C reaches the progressive path of improving the excellent and compensating the poor in region A through B or D; that is, the "double low" region can make up for the disadvantages while maintaining its own advantages step by step according to its situation, to achieve the double high goal. Thirdly, the breakthrough leapfrog promotion path from C to A, that is, the leapfrog of innovation efficiency, can be directly realized by simultaneously promoting the two stages.

#### **4. Model Method and Variable Selection**

#### *4.1. Three-Stage DEA Model*

In the measurement of innovation efficiency, most scholars focus on Data Envelopment Analysis (DEA) based on non-parametric techniques, which was proposed by Charnes, Coopor and Rhodes in 1978 [43]. The principle of this method is to determine the relatively effective production frontier by means of mathematical planning and statistical data by keeping the input or unchanged of Decision-Making Units (DMU), and projecting each DMU onto the production frontier of DEA. The relative effectiveness of DMU is evaluated by comparing its deviation from DEA frontier. The DEA method is an evaluation method based on the concept of relative efficiency using convex analysis and linear programming as tools, and using a data programming model to calculate the optimal input–output scheme of the comparison decision-making unit itself. The advantage of this model is that there is no need to consider the corresponding relationship between input and output, which can solve the linear corresponding relationship between input and output well, and there is no need to assign hypothetical weights to parameters and estimation indexes, which avoids the subjectivity of the research subject, and the measurement of relative efficiency value is more objective, real and reliable. Compared with the traditional efficiency evaluation model, DEA can better reflect the information and characteristics of the evaluation object when dealing with the efficiency evaluation of composite input and multi-type output system.

The classic DEA method has been optimized in the treatment of environmental factors, such as using the DEA-Tobit method. In the first stage, the method uses the classical DEA method to calculate the efficiency value of DMU, and then in the second stage, the efficiency value is taken as the dependent variable and environmental factors as the independent variable to establish a Tobit regression model to investigate the impact of environmental factors. This method can use regression technology to determine the intensity and direction of the impact of environmental factors on efficiency, but its effect is not to strip out environmental factors in the calculation of efficiency, so it does not change the efficiency value measured by the classical method. Due to random noise and environmental factors also having a certain degree of influence on the efficiency evaluation of decision-making units, Frided et al. (2002) [44] proposed a three-stage DEA method based on the classical DEA model that can separate environmental factors to make the calculated efficiency value more accurate. Among the cities in the GBA, there are differences in the level of economic development, the stage of industrial development and the development degree of science, technology and education, and the innovation environment facing each city is highly differentiated and unbalanced. Environmental factors should be taken into account in the innovation efficiency of different stages of innovation activities in different cities. Combined with the above characteristics, We adopted the three-stage DEA model to measure the efficiency of the GBA at each stage in the process of innovation value chain, and its theoretical model is as follows.

4.1.1. In the First Stage, the Initial Efficiency Is Evaluated by Using the Original Input–Output Data

The DEA model can choose different orientations according to the difference of analysis purpose and demand. The traditional DEA model has two forms: the CCR model and the BCC model. Among them, the CCR model assumes that DMU is in a fixed scale return situation, which is used to measure the total efficiency. The BCC model is used to measure pure technology and scale efficiency, assuming DMU is in a variable return to scale situation. In the applications discussed in existing literature, generally speaking, most three-stage DEA models will choose the input—oriented BCC (variable return to scale) model so as to analyze comprehensive technical efficiency from two aspects of pure

technical efficiency and scale efficiency. For any decision unit, the dual-form BCC model under input guidance can be expressed as:

$$\begin{aligned} & \min \theta - \varepsilon (\mathring{\mathbf{e}}^T \mathbf{S}^- + \mathbf{e}^T \mathbf{S}^+) \\ & \text{s.t.} \begin{cases} \sum\_{j=1}^n \mathbf{X}\_j \boldsymbol{\lambda}\_j + \mathbf{S}^- = \theta \mathbf{X}\_0 \\ \sum\_{j=1}^n \mathbf{Y}\_j \boldsymbol{\lambda}\_j - \mathbf{S}^+ = \mathbf{Y}\_0 \\ \boldsymbol{\lambda}\_j \ge \mathbf{0}, \mathbf{S}^-, \mathbf{S}^+ \ge \mathbf{0} \end{cases} \end{aligned} \tag{1}$$

The formula j = 1, 2, · · · , *n* represents the decision unit, and *X*,*Y* are input and output vectors. The DEA model is essentially a linear programming problem.

If *θ* = 1, *S* <sup>+</sup>= *S* −= 0, then the decision-making unit DEA is valid;

If *θ* = 1, *S* <sup>+</sup> <sup>6</sup><sup>=</sup> 0, or *<sup>S</sup>* <sup>−</sup> 6= 0 , then the weak DEA of the decision-making unit is valid; If *θ* < 1, then the decision-making unit is not valid by DEA.

The efficiency value calculated by the BCC model is the comprehensive technical efficiency (TE), which can be divided into scale efficiency (SE) and pure technical efficiency (PTE), that is, TE = SE × PTE. In the first stage, two values should be calculated: <sup>1</sup> the initial DEA efficiency value without filtering environmental factors is used for comparison and analysis in subsequent links; <sup>2</sup> the relaxation variable of input is the dependent variable of the second stage.

4.1.2. In the Second Stage, Environmental Factors and Statistical Noise Were Eliminated by SFA-Like Regression

The relaxation variable calculated in the first stage was taken as the opportunity cost of the decision unit, and the influence of environmental factors and random errors was considered. The relaxation variable [*x* − *Xλ*] can reflect the initial inefficiency, composed of environmental factors, management inefficiency, and statistical noise. The SFA model was used to decompose the slack variables in the first stage into the above three effects. Then the input and output were corrected and readjusted, or only the input or output was adjusted. The following regression function similar to SFA is constructed based on input orientation.

$$S\_{\rm ni} = f(Z\_{\rm i}; \beta\_{\rm n}) + \nu\_{\rm ni} + \mu\_{\rm ni}; i = 1, 2, \cdots, I; n = 1, 2, \cdots, N \tag{2}$$

In the formula, *Sni* is the relaxation value of the input of item *n* of the decision unit *i*, *Zi* is the environment variable; *β<sup>n</sup>* is the coefficient of the environment variable; *νni* + *µni* is the mixed error term; *νni* represents random interference, and *µni* represents inefficiency of management. In the formula, *ν* ∼ *N*(0, *σ<sup>v</sup>* 2 ) is the random error term, representing the influence of random disturbance factors on input relaxation variables; *µ* refers to management inefficiency and represents the influence of management factors on input slack variables. It is assumed that it follows a normal distribution truncated at zero points, *<sup>µ</sup>* <sup>∼</sup> *<sup>N</sup>*+(0, *<sup>σ</sup><sup>µ</sup>* 2 ).

#### 4.1.3. The Third Stage: DEA Efficiency Analysis of the Adjusted Input-Output Variables

The adjusted input-output variables are used to calculate the efficiency of each decision unit again. At this point, the efficiency has removed the influence of environmental factors and random factors, which is relatively authentic and accurate.

#### *4.2. Indicator Selection and Data Source*

From the perspective of the innovation value chain, input–output variables are not the same in different stages according to the different focuses of research in the three stages of innovation activities. In the knowledge innovation stage, since higher education personnel are the main contributors to the output of scientific and technical papers, this paper identifies the number of higher education personnel and the cost of higher education

expenditure as input indicators. In terms of output indicators, referring to the practice of Yu (2014), this paper chooses the number of published scientific papers and the number of scientific monographs [27]. In the stage of scientific research innovation, the number of R&D personnel and R&D investment are firstly considered based on the previous research ideas of scholars [45]. Among them, it is the essence of regional innovation that R&D personnel carry out scientific and technological research, which reflects the regional ability to attract talents. The total amount and intensity of R&D expenditure represent the overall scientific and technological R&D capability of the region as well as the degree of investment in innovation activities. In addition to the two input indicators mentioned above, we argue that the input indicators at the research and innovation stage should also include the outputs at the knowledge innovation stage, the number of scientific and technical papers, and scientific and technical monographs. The output index of scientific research innovation stage is the number of patent applications and the number of patent grants [45]. Similarly, when determining the input indicators for the product innovation stage in this paper, in addition to considering the number of patents granted in the previous stage, the indicator of new product development expenditure should also be included. With reference to the relevant literature [27], the output indicators for this phase were selected as two of the industrial enterprises' new product output and export value.

Combined with existing studies, the environmental variables selected in this paper include higher education investment level [46], government support level, economic development level, informatization level, fixed asset investment [47], scientific research foundation, industrial structure [48] and regional openness [49]. The level of investment in higher education is expressed by the proportion of higher education funding to GDP, the level of government support is expressed by the proportion of government funding in science and technology funding, and the level of economic development is expressed by the per capita GDP of the region. Informatization level refers to innovative information resources, mainly including the number of mobile phone users at the end of the year and the number of broadband Internet users, which can reflect the development level of regional information resources. Investment in fixed assets is a significant component of innovation material resources and the material basis for regional innovation activities. It reflects the degree of enrichment of innovation resources. The scientific research base is expressed as the number of enterprises with R&D activities in the region. The industrial structure is expressed by the ratio of the tertiary industry to the GDP of the region, and the proportion of the total import and export of the region to GDP is used to express the openness of the region. The specific input-output indicators and environmental variables of the three stages of innovation are shown in Table 1:

The index data are from China City Statistical Yearbook 2010–2020, Guangdong Statistical Yearbook and Guangdong Science and Technology Yearbook. For Hong Kong, the data are from Hong Kong Statistical Yearbook 2020, Statistics on Innovation Activities in Hong Kong 2019 and Statistics of Hong Kong Trade Development Council 2019, respectively, of the Census and Statistics Department of the Hong Kong Special Administrative Region. The data for Macau are derived from the Macao Statistical Yearbook 2020, Macao Economic Development Report for the fourth quarter of 2019, and the time-series database of The Macao Statistical Survey Bureau. The amount of Hong Kong dollars and Macau patacas in each indicator is converted into people's value by the exchange rate of Hong Kong dollars and Macau patacas against RMB in the current year.


**Table 1.** Three-stage input-output index system of innovation.

#### **5. Empirical Results and Analysis**

#### *5.1. Efficiency Measurement Analysis of Innovation Chain Three-Stage DEA Model*

According to the three stages of knowledge innovation, scientific research innovation and product innovation in the innovation value chain, the indicators are different. According to the analysis and research of Bruce [50] (1993), innovation output results have a certain lag. We assumed that innovation activities have a lag period of one year from input to output, that is, when the input index of year T is selected, the corresponding output index data should be selected in year T + 1. Therefore, when measuring the innovation efficiency of the GBA in this paper, the index data of the innovation input were selected from 2010 to 2018, and the variable data of innovation output were selected from 2011 to 2019. We followed the three-stage DEA analysis steps. In the first stage, DEAP2.1 software was used to make a preliminary efficiency calculation for input-output indicators. The results are shown in Table 2.

**Table 2.** Traditional DEA innovation efficiency value of GBA (average value of 2010 to 2018).


In the second stage, Frontier4.0 software was used to establish the SFA regression model with the input relaxation variables calculated in the first stage as the dependent variables and the environmental variables in each stage as the explanatory variables. The results are shown in Table 3. In the knowledge innovation stage, higher education

investment level and the number of higher education personnel were positively correlated. However, there was a negative correlation with higher education expenses and description of higher education, and there is a certain redundancy of financial investment; similarly, the government support and knowledge innovation phase 2 class negatively correlated to the inputs, which is also due to the government and the phenomenon of the lack of output conversion on spending too much. Among the four kinds of input resources in the stage of scientific research innovation, the number of scientific papers published has no apparent correlation with other influencing factors except the level of economic development. Economic development was positively correlated with the publication of scientific papers and monographs but negatively correlated with R&D personnel and R&D expenditure. The informatization level and fixed asset investment was beneficial to the number of R&D personnel but hurt R&D expenditure. The research basis had a positive effect on the other three input factors except for the number of scientific papers published. In the stage of product innovation, industrial structure and regional openness had a negative correlation with the number of patents granted. In contrast, new product development funds had a positive correlation with industrial structure and a negative correlation with regional openness.


**Table 3.** Results of stage 2 estimation based on SFA.

The data in the table are the correlation coefficient, and the data in brackets are the standard deviation. \*\*\*, \*\*, and \* mean significant at the level of 1%, 5%, and 10%, respectively.

> In the third stage, DEAP2.1 software was used to adjust the input variables according to the SFA regression results, and the adjusted data were substituted into the BCC model again for efficiency evaluation. The specific innovation efficiency measured by the adjusted three-stage DEA model is shown in Table 4.


**Table 4.** Three-stage DEA innovation efficiency of GBA after adjustment (average value from 2010 to 2018).

According to the comparison between Tables 2 and 4, after eliminating environmental factors and random interference, the average comprehensive efficiency of the GBA in the knowledge innovation stage increased from 0.757 to 0.852, in which the pure technical efficiency also increased while the scale efficiency decreased slightly. The mean value of comprehensive efficiency increased from 0.748 to 0.788 in the stage of scientific research innovation, and both the pure technical efficiency and scale efficiency increased to a certain extent. In the product innovation stage, the average comprehensive efficiency decreased from 0.582 to 0.542, but both the pure efficiency and scale efficiency increased slightly.

As can be seen from Table 4, compared with the latter two stages, the efficiency of the knowledge innovation stage was higher, with an average value of 0.852. Among them, the efficiency of knowledge innovation in Guangzhou, Shenzhen, and Hong Kong was more than 0.9, ranking in the first echelon of innovation. The efficiency values of Zhuhai, Foshan, Huizhou, Dongguan and Zhongshan were between 0.8 and 0.9, which belong to the second echelon. The efficiency value of Jiangmen, Zhaoqing and Macao was below 0.8, the third echelon. In the scientific research innovation stage, the average efficiency was 0.788, which was lower than that in the knowledge innovation stage. Guangzhou, Shenzhen, and Hong Kong had higher efficiency values, which were all higher than 0.9. Among them, the efficiency values of Shenzhen and Hong Kong reached the allocation effective level of 1. In the second tier, including Zhuhai, Foshan, Huizhou, Dongguan and Zhongshan, their efficiency values were between 0.6 and 0.9. The efficiency value of Jiangmen, Zhaoqing, and Macao was below 0.6, among which the efficiency value of Zhaoqing was the lowest, only 0.484. In the product innovation stage, its average efficiency was low on the whole, only 0.542, which is far lower than the previous two innovation stages. Among them, Hong Kong had the highest product innovation efficiency (0.967), followed by Shenzhen, Dongguan and Guangzhou (0.6 to 0.8). The product innovation efficiency value of Macao, Foshan, Huizhou, and Zhuhai was between 0.4 and 0.6, which belongs to the second tier. In the third stage of the innovation chain, the efficiency of Zhongshan, Jiangmen, and Zhaoqing was low, with a value below 0.4. Among them, Zhaoqing was the lowest, with only 0.232. Based on the calculated efficiency value, it can be preliminarily analyzed that the reason why Guangzhou, Shenzhen, and Hong Kong achieved efficient innovation lies in their strong foundation conditions and distinct advantages in innovation platforms [51]. Compared with the three leading innovation cities, the overall innovation efficiency of the other cities was average, and there was a certain gap with the first echelon, which indicates the imbalance of innovation capacity within the GBA.

By comparing the efficiency values of the three stages in the innovation process (as shown in Figure 3), it is not difficult to find that with the progress of innovation, the innovation efficiency becomes increasingly lower in the process from knowledge innovation to scientific research innovation and finally to product innovation. The innovation efficiency decreased from 0.852 in the knowledge innovation stage to 0.788 in the scientific research

innovation stage and was only 0.542 in the product innovation stage. From the composition of the main bodies of innovation activities, the main bodies of innovation in the GBA are the government, enterprises, universities, research institutes and science and technology intermediaries. Knowledge innovation mainly relies on universities. There are many universities (162 in total) in the GBA [52], so the GBA has high efficiency in the stage of knowledge innovation. Scientific research innovation mainly relies on scientific research institutes and scientific research intermediaries, and the number of patent applications is mainly taken into account when testing the results in this stage. The overall efficiency of the scientific research innovation stage was lower than that of the first stage, indicating that the innovation efficiency of the transfer from knowledge innovation to scientific research innovation in the innovation process needs to be enhanced. The relatively low efficiency in the stage of product innovation reflects that the transformation of innovative product results is not ideal. The reason may be that the technical maturity of the scientific and technological achievements of some universities is low, and they lack accurate docking with the industry and market demand [53]. At the same time, the evaluation and pricing mechanism of scientific research results is not perfect; the steps are tedious, time–consuming, and lengthy, which affects the transformation time and leads to the low efficiency of overall product innovation in the GBA. At present, there is a lack of interaction and communication among innovation players in the GBA, and the coordination ability is weak. The synergistic effect between enterprises and other innovation players such as universities and research institutes has not been played. The knowledge spillover benefit and technology diffusion effect produced by innovation activities are non-significant [54]; the synergistic valueadded effect of the value chain innovation between enterprises and the system innovation effect between enterprises and the government did not lead to an effective value [55].

**Figure 3.** Comparison of innovation efficiency values in the three stages of innovation in the GBA.

#### *5.2. The Positioning Analysis of the Two-Dimensional Matrix of Innovation Efficiency of Each City*

Based on the empirical results in Table 4 and the previous research ideas, we put the innovation efficiency of each stage in a two-dimensional distribution map (using the mean value of innovation efficiency of each stage as the division standard). The details are shown in Figures 4 and 5.

According to the analysis of the "knowledge–scientific research" innovation efficiency matrix (Figure 4), cities in the GBA are distributed in regions A and C, namely, intensive and efficient knowledge–scientific research innovation and extensive and inefficient knowledge–scientific research innovation. The cities in Zone A include eight cities: Guangzhou, Shenzhen, Hong Kong, Zhuhai, Foshan, Huizhou, Dongguan and Zhongshan. Higher education and basic scientific and technological innovation facilities in these cities are relatively developed, laying a talent base and material guarantee for knowledge innovation and scientific research innovation. The innovation-leading development cities represented by Guangzhou, Shenzhen and Hong Kong have a high level of economic

development and a strong "siphon effect" to attract resources from surrounding cities. Therefore, a large number of innovative elements are gathered here and their innovation ability is relatively prominent. In recent years, Zhuhai has been gradually developing as an innovation growth pole [56], while Foshan and Dongguan, as extensions of the GBA's "Guangzhou-Shenzhen-Hong Kong-Macao" innovation corridor, have also been steadily improving their innovation capabilities. By connecting with Shenzhen and Guangzhou, Huizhou and Zhongshan will further cluster in the innovation corridor and develop their innovative advantages.

Macao, Jiangmen and Zhaoqing are the two cities with low efficiency in knowledge innovation and scientific research innovation. Although these three cities are all located in region C, the causes of double inefficiency are different. Macao has a relatively high level of economic development. The low efficiency in the knowledge stage may be caused by excessive input of human and material resources in innovation and limited innovation output in this link. In contrast, the low efficiency in the scientific research stage reflects the lack of ability to integrate knowledge innovation into scientific research innovation. Jiangmen and Zhaoqing are cities with relatively low economic strength in the GBA, so their cities have limited innovation resources and lack the momentum of innovation development.

For the three cities with low efficiency of knowledge and scientific research innovation, the optimization path includes the C → A skipping of the development path, and the C → B → A and C → D → A progressive improvement path. The leap-forward development path requires a solid economic foundation and reserves of resource elements, and Macao is suitable for the leap-forward development path. The gradual promotion path is to give full play to the existing advantages and make up the disadvantages on this basis, that is, through the transition of B or D region, and finally towards A region. The low efficiency of Jiangmen and Zhaoqing in the stage of knowledge innovation is mainly due to the lack of input resources provided by the cities, which makes the improvement of efficiency in the stage of knowledge innovation difficult. Therefore, when choosing the path of improvement, the two cities should choose the C → D → A progressive innovation development path that first improves the efficiency of scientific research innovation while maintaining the level of knowledge innovation efficiency.

Let us look at the "scientific research–product" innovation efficiency matrix (Figure 5). In the process of linking scientific research and product innovation, cities in the GBA are distributed in regions A, B, and C, namely, intensive and efficient scientific research–product innovation, high scientific research innovation efficiency and low product innovation efficiency, and extensive and low-efficiency scientific research–product innovation. The cities in the double high efficiency A region, including Hong Kong, Guangzhou, Shenzhen and Dongguan, also performed well in the previous phase of knowledge innovation. Because they had a higher level of economic development of the city itself, the innovation efficiency of the transformation provided the economic foundation, which was also due to the development of the related policy funds, talents, and supporting environment brought by various advantages, making scientific research and product innovation efficiency higher; these areas also reflect the scientific research and innovation of the elements of the transition to product innovation conversion efficiency better.

The cities of high-efficiency scientific research innovation and low-efficiency product innovation are located in region B are Huizhou, Foshan, Zhuhai and Zhongshan. The "scientific research–product" innovation of these four cities also proves that regional scientific research innovation is not able to transform into product innovation. Therefore, cities in Region B should focus on enhancing the efficiency of product innovation while not ignoring the efficiency of scientific research innovation. Located in the geometric center of the GBA, Zhongshan plays a crucial role in "connecting the east with the west". As a start-up resource city, Huizhou can connect with Shenzhen, learn from its research results and complete the transformation suitable for its development. Zhuhai can take advantage of its proximity to Macao to explore new paths of innovative development. Foshan should further upgrade its existing leading industries. At the same time, we should increase the

investment in scientific research and cooperate with Guangzhou to complete the integration of traditional industries and emerging industries [57], develop complementary industrial systems, and increase cooperation with cities on the east coast. The four cities of Huizhou, Foshan, Zhuhai and Zhongshan should strengthen the efficiency of product innovation according to their regional development characteristics and strive to solve the problem of low efficiency of product innovation.

Macao, Jiangmen and Zhaoqing are the two cities with low efficiency in scientific research innovation and product innovation. The efficiency of these three cities in the stage of knowledge innovation is relatively lower than that of other cities. The fundamental reason is the imbalance of input and output of innovation factors, and the transformation of innovative product results is not ideal. These three cities need to strengthen scientific research innovation and product innovation. They can maximize their advantages with the help of functional division of urban agglomeration, and then focus on improving their weaknesses. At the same time, the advantage of the overall coordinated development of the GBA urban agglomerations can be utilized to make full use of the technology spillover effect, absorb the scientific research achievements of other regions and complete the industrialization transformation suitable for local development, to improve the product innovation efficiency of the region.

#### **6. Conclusions and Recommendations for Countermeasures**

#### *6.1. Conclusions*

Based on the three-stage innovation value chain model of "knowledge innovation– scientific research innovation–product innovation", we constructed a generalized innovation input-output index system and use the three–stage DEA model that examines environmental factors to measure the innovation efficiency in each stage of innovation activities in the GBA from 2010 to 2018. The results show the following: <sup>1</sup> On the whole, the innovation efficiency of the Greater Bay Area decreased with the advancement of innovation activities. The average innovation efficiency of knowledge innovation, scientific research innovation, and product innovation was 0.852, 0.788, and 0.542, respectively. <sup>2</sup> From the perspective of different stages in the innovation process of each city, Shenzhen, Guangzhou, and Hong Kong all performed well in the whole stage of innovation, while other cities in the GBA had innovation shortcomings in different stages. Based on the above research results, we constructed the "knowledge–scientific research" innovation efficiency matrix and the "scientific research–product" innovation efficiency matrix using a two-dimensional matrix, and divided the innovation efficiency at each stage into four types. From the matrix analysis, the current state of innovation development in each city was located, and the path to improve its innovation efficiency was analyzed according to the characteristics of each city.

#### *6.2. Recommendations for Countermeasures*

Innovation is an important starting point and strategic path for promoting the development of the GBA. To promote the construction of a national innovation system and strengthen strategic scientific and technological forces, it is necessary to construct and perfect the new mechanism of regional collaborative innovation and improve the efficiency of regional innovation. The GBA is an important bridge linking the internal and external circulation of China's economy, and will become a pioneer and demonstration area for China to build a new development pattern. In recent years, China has issued a series of policy documents on collaborative innovation around the construction of an international science and technology innovation center in the GBA and carried out a series of fruitful explorations of institutional mechanisms to promote the upgrading of cooperation models in collaborative innovation in the GBA [58]. However, due to the lack of resource transformation in the innovation process, the innovation efficiency gradually declines along with the innovation chain, and the imbalance of innovation development among cities in the GBA shows that there is still considerable room for improvement in collaborative

innovation in the GBA. The mechanisms and systems of collaborative innovation in the GBA are not yet perfect, and the scientific collaborative innovation system has not been completely established. The existing government management system has not adapted to the coordinated governance of metropolitan groups. As an important territory to promote innovation in China, the GBA still needs to improve its collaborative innovation mechanism and establish a collaborative system among innovation bodies such as cities, enterprises and R&D institutions. In addition, the cooperation mechanism, communication mechanism and guarantee mechanism based on collaborative innovation in the Greater Bay Area need to be further deepened. Based on this, relevant countermeasures and suggestions for optimizing collaborative innovation in the GBA are put forward from the aspects of factor flow, industrial structure, innovation networks.

6.2.1. Promoting the Flow of Innovation Factors and Building a Circular and Smooth Bay Area

At present, the GBA has problems such as uneven distribution of innovation factors and uncoordinated ability to allocate resources. At the same time, administrative barriers caused by policy and economic factors have placed very significant constraints on the convenient and efficient flow of innovation resources within the GBA, which is not conducive to the formation of technology diffusion effects and hinders the development of collaborative regional innovation. The key to enhancing innovation effectiveness in the GBA city cluster lies in the free flow of innovation factors. Therefore, the Greater Bay Area should improve the phenomenon of excessive siphoning from the central cities, strengthen the diffusion effect of innovation technologies, and accelerate the introduction and market-based allocation of innovation factor resources. These improvements would attract more high-quality elements to the GBA, optimally fill in the missing links of the innovation chain in cities with low and medium levels of coordination, promote positive interaction among the links and enhance the level of chain coordination. At the same time, the mechanism channel for the flow of innovation factors will be unblocked, and the cities in the GBA will be promoted to jointly enhance the level of coupling and coordination of science and technology innovation, so as to achieve the construction goal of smooth circulation of innovation factors in the GBA.

6.2.2. Improving Innovative Cooperation Mechanisms to Build a Mutually Beneficial Bay Area

There is currently a lack of interaction and communication between innovation agents in the Greater Bay Area, and coordination is weak. Universities and research institutes with their enterprises and other innovation subjects are many but not strong, the interaction and coordination between the subjects is insufficient, and the sharing platform of science and technology innovation elements is relatively lacking. The existing mechanism does not play a synergistic role between enterprises and other innovation bodies such as universities and research institutes, thus resulting in problems such as low value-added effect of scientific and technological innovation results and low conversion rate. Therefore, creating a collaborative and mutually supportive partnership among multiple players and improving the innovation cooperation mechanism are the backbone of the GBA's innovation-driven strategy. The GBA should further promote the interconnection of the city's industrial, value and innovation chains, attach importance to the investment and accumulation of talent capital, and cultivate highly qualified talents with core competitiveness. Improving the institutional mechanisms of Guangdong, Hong Kong, and Macao in areas such as mutual recognition of talent qualifications, the use of science and technology funds and customs clearance facilitation would achieve the goal of building a mutually beneficial Bay Area.

6.2.3. Optimizing the Industrial Structure and Building a Bay Area with a Full Industrial Chain

In terms of industrial structure, the GBA has uneven development. Hong Kong and Macao have entered the post-industrialization stage, while the Pearl River Delta is between the post-industrialization stage and post-industrialization stage, and the non-Pearl River Delta region is still in the mid-industrialization stage. In the face of the lack of synergistic added value and technology diffusion effects in the industrial chain, the Greater Bay Area needs to optimize its industrial structure in order to strengthen innovation cooperation, achieve complementary advantages and create an "innovation ecosystem". It is necessary to build the Guangzhou–Shenzhen–Hong Kong and Guangzhou-Shenzhen-Australia Science and Technology Corridors to a high level and to co-ordinate the use of quality science and technology innovation resources from Hong Kong and Macao to create a secure bay area for industrial chains. The development of high–tech industries; the integration of the existing advantageous industries of Hong Kong, Macao; and the leading innovation cities in the Pearl River Delta (Shenzhen, Guangzhou, etc.) form a collaborative division of labor patterns among the cities around the dual-chain integration model of innovation and industrial chains and create an innovation economic belt suitable for the development of the GBA. The optimization of the industrial structure of the GBA and the improvement of the construction of the whole industrial chain are important engines for the construction of a world-class innovation-leading Bay Area posture.

6.2.4. Establishing a Collaborative Innovation Network and Building a Synergistic Development Bay Area

The building of collaborative innovation networks in the Bay Area city clusters is a great boost to the overall flow of innovation resources in the region. Strengthening the construction of collaborative innovation networks in the GBA and neighboring cities and accelerating the construction of infrastructure networks with complementary functions to create a network system with an adequate flow of resource elements would make the spatial connection of the city cluster as a whole even closer. It is necessary to improve the mechanism for sharing innovation platform resources and open services, promote a market-based model for efficient sharing of science and technology innovation platform resources and industrial cooperation resources within the Greater Bay Area, and establish a unified project pool and talent pool. At the same time, the existing basic innovation resources of the GBA will be brought into full play, and global innovation resources will be introduced through the International Innovation Demonstration Zone to build a worldclass innovative Bay Area, enhance the overall innovation strength of the region and raise the status of the Greater Bay Area in global innovation.

**Author Contributions:** Conceptualization, W.Y. and Y.H.; methodology, W.Y. and Y.H.; software, Y.H. and L.C.; formal analysis, Y.H. and L.C.; resources, W.Y. and Y.H.; data curation, Y.H. and L.C.; writing—original draft preparation, Y.H. and W.Y.; writing—review and editing, Y.H. and L.C.; supervision, Y.H. and W.Y.; project administration, Y.H. and W.Y.; funding acquisition, W.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by The National Social Science Fund of China [grant number 20BGL299].

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data supporting the findings of the article is available in the China City Statistical Yearbook 2010–2020, Guangdong Statistical Yearbook and Guangdong Science and Technology Yearbook. For Hong Kong, the data are from Hong Kong Statistical Yearbook 2020, Statistics on Innovation Activities in Hong Kong 2019 and Statistics of Hong Kong Trade Development Council 2019, respectively, of the Census and Statistics Department of the Hong Kong Special Administrative Region. The data for Macau are derived from the Macao Statistical Yearbook 2020, Macao Economic Development Report for the fourth quarter of 2019, and the time-series database of The Macao Statistical Survey Bureau.

**Acknowledgments:** We would like to express our gratitude to all those who helped us during the writing of this article. Our deepest gratitude goes first and foremost to Lu Wang, who is from the Hunan University of Science and Technology in China, for his constant encouragement and guidance. **Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Iwona Cie´slak and Andrzej Biłozor \***

Department of Socio-Economic Geography, Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, Prawoche ´nskiego 15, 10-720 Olsztyn, Poland; isidor@uwm.edu.pl

**\*** Correspondence: abilozor@uwm.edu.pl

**Abstract:** The optimization of space is the priority goal of spatial planning. Spatial planning policies have numerous objectives, including the prevention of land-use conflicts. Conflicts arise whenever two entities have contradictory expectations regarding the surrounding space. In the process of spatial development, humans impart new characteristics to space, which, under specific circumstances, can give rise to land-use conflict. The elements of space that are particularly vulnerable to conflict include boundary points, property boundaries, density of development, or the shared use of infrastructure. The main aim of this study was to develop a procedure for evaluating the risk of land-use conflict based on the characteristic attributes of space. The proposed procedure for assessing the accumulation of conflict-generating traits in space was developed with the use of databases, GIS tools, and statistical data processing methods.

**Keywords:** land management; land-use conflicts; components of space; spatial analysis; GIS tools

**Citation:** Cie´slak, I.; Biłozor, A. An Analysis of an Area's Vulnerability to the Emergence of Land-Use Conflicts. *Land* **2021**, *10*, 1173. https://doi.org/ 10.3390/land10111173

Academic Editor: Robert Gilmore Pontius

Received: 21 October 2021 Accepted: 28 October 2021 Published: 1 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

Conflict is a widespread phenomenon that always involves at least two parties, where one party attempts to derive personal gain at the other party's expense and without its explicit consent [1]. Conflict is a concept that can be examined in various contexts, including as a social (social approach) or an economic (functional approach) process, where one party attempts to maximize its own gain by eliminating or dominating over the other party to the conflict [2].

The potential sources of conflict have to be determined to identify the existing or potential conflicts. The main categories of conflict have been systematized by Bogetoft and Pruzan [3] who posited that conflicts can arise from one or several different factors. These factors can be divided into four main groups:

	- 1.1. The parties have different values and aims;
	- 1.2. The parties take different measures to represent their aims;
	- 1.3. The parties have different preference hierarchies (for example, by applying different weights for different solutions);
	- 2.1. The parties can incur different costs and derive various indirect benefits associated with the anticipated effects of their actions;
	- 2.2. The breakdown of costs and benefits is perceived as uneven and unfair;
	- 3.1. The parties cannot reach agreement on the probable effects of actions;
	- 3.2. The parties are not certain of the effects of their actions;
	- 3.3. The evidence and the rationale behind the effects of their actions may be insufficient or incomprehensible;
	- 4.1. The parties find it difficult to exchange information about their values, aims, criteria, preferences, and expectations.

All of the above factors have a common denominator: the actions undertaken by the parties are contradictory, and each action is localized in space. A big proportion of conflicts are directly or indirectly associated with space. Conflicts that are directly related to space are referred to as spatial conflicts [4].

Spatial conflict is an inseparable element of spatial development and human existence [5]. The significance and progression of land-use conflicts are irreversibly linked to the use of space, and the quality of life is influenced by the quality of the surrounding space [6–8]. Profit maximization has been the driving force behind land-use conflict ever since humans discovered that land can be a critical source of capital and wealth [9,10]. The struggle for land took on various forms and progressed on a different scale, and due to the nature and the object of that struggle, it became known as spatial conflict or land-use conflict [11,12].

Spatial conflicts are widely encountered, and they can occur at any time and in any location. Changes in the hierarchy of needs and needs satisfaction, as well as individual traits (greed, envy, etc.) can promote the belief that the existing space is insufficient. This conviction increases the value of space, and it is the main cause of spatial conflict.

Most spatial conflicts have highly negative consequences for the economy, society, space, and the environment [13]. The above applies particularly to developing and transition countries, where the real estate market is weakly developed and where illegal practices involving land create an opportunity for rapid wealth accumulation [14]. In this case, land ceases to be a public good, and land ownership is often transferred to a small and wealthy social group. However, the extent to which the parties to a conflict manifest their views is correlated with the level of democracy in a given country [15]. The causes, course, and consequences of land-use conflict should be analyzed to develop effective spatial planning tools [16].

In theory, land-use conflict does not differ from other types of conflict, but space is always the subject matter of such disputes [17]. Land-use planning shapes our living and working environment; the integrity and sophistication of the overall planning process govern the sustainability of proposed spatial allocations in urban and rural areas [18]. Spatial conflict is usually driven by various land-use options, contradictory interests, and goals, including those pertaining to the use of natural resources [19–23].

Spatial conflicts can have different causes, and they are rarely homogeneous. In other words, spatial conflict is not driven by a single factor, and it is a complex and multidimensional phenomenon. This complexity can be attributed not only to the physical characteristics of space, such as size, diversity, and form, but also to changes in space and subjective perceptions of space [24]. Humans have different needs and expectations that are satisfied in space [25]. Land is a common good that is essential for human activity. Due to the diversity of human needs and the limited supply of land, land has been the source of conflict throughout human history [26]. Owing to the complexity of space, various spatial relationships can be identified, but not all of them can be described in detail. However, they can be generalized [27]. The main types of conflict relationships are associated with environmental features, economic activities, and social expectations [28]. The conflict relationships based on these three main pillars of human existence are presented in Figure 1.

**Figure 1.** Conflicts arising from relationships in space. Source: Own compilation based on [29].

**Conflicts between the economy and the environment** relate to the use of natural resources and the extent to which the environment is transformed by human activity [30]. Mineral resource mining and the acquisition of land for development projects are prime examples of the above. The expectations associated with environmental protection stand in opposition to the maximization of profit derived from resource use.

**Conflicts between the society and the environment** have a similar context, but they focus on the quality of human life. The expected comfort and convenience of the modern lifestyle, such as access to public infrastructure or pest-free households, run contrary to environmental protection.

**Conflicts between the economy and the society** generally result from contradictory expectations regarding access to public infrastructure and natural resources between the consumers, investors, and public administration bodies that analyze the economic consequences of various uses of space. Disputes surrounding the location of land development projects are an example of such conflicts [31]. These types of conflict gave rise to NIMBY (not in my backyard) syndrome, where residents generally agree that a facility producing goods or services is needed, but oppose its construction in their neighborhood [32]. Conflicts can also erupt in a reverse scenario, when local communities advocate for the construction of certain types of infrastructure, but are faced with opposition from supralocal groups that derive benefits from the use of space in its existing form [33].

Conflicts waged on a wider scale are referred to as functional-economic conflicts [34], where factors leading to the aggregation and dispersion of various land-use functions occur simultaneously and undermine the welfare of local communities [6,35]. These factors are usually associated with considerable disproportions in the development of adjacent areas. Selected functions are excessively concentrated in some areas (such as cities), but they are lacking in peripheral and underdeveloped areas. Globalization contributes to the emergence of spatial conflict in the social-economic dimension. Urban sprawl increases the prices of agricultural land, leads to conflict over farmland protection and, consequently, drives conflict over land ownership [5].

The presented classification of spatial conflicts is not exhaustive or sufficiently detailed. Many conflicts combine the features of all of the above categories, where contradictory expectations regarding land use are influenced by the object of the dispute and the involved parties [36].

Spatial conflicts can emerge over land ownership and land-use rights, but also over land itself, the neighborhood, or the use of private or public land [37]. Regardless of the above, space is always the essence of conflict. The object of conflict can also differ in scale or magnitude, and it can include buildings, undeveloped land, or property boundaries. Entire regions and countries can be embroiled in spatial conflict.

In view of the above, the object of spatial conflict can be classified based on its location, scope, and specific claims. When these criteria are taken into consideration, the objects of spatial conflicts can be divided into the following groups:

	- property boundaries.

ownership, perpetual usufruct, co-ownership; other rights to derive income from land: easement, lease, tenure, etc.; specific land-use (land management) rights.


immediate space (own—private); local space (neighborhood, municipality); regional and ethnic space; national space; global space.

The object of spatial conflict should be classified to identify the scope of the dispute and the involved parties. The resulting classification also contributes to the selection of the optimal conflict resolution tools [38].

Many land-use conflicts are caused by different expectations regarding the attributes of space [39]. The above implies that space cannot be evaluated unambiguously [40]. However, fragments of space with a high accumulation of potentially conflict-generating attributes can be identified [41]. Most spatial conflicts are caused by social factors as conflicts are driven by humans and do not exist without humans [42]. Conflicts are often fueled by personal traits which are difficult to assess and are rarely analyzed in studies on spatial conflicts. However, the risk of social conflict can also be exacerbated by the characteristic traits of entire social groups [43]. These include:


The above factors strongly influence the quality of life and individual expectations towards space. In less affluent societies, land-use strategies are implemented to generate economic benefits and improve the standard of living, and these goals are accomplished at the expense of other attributes, such as the quality of the natural environment [44]. Social attributes that contribute to spatial conflict can also influence economic factors, including:


These differences are often responsible for various expectations towards land use and land management. In many cases, different expectations can be the secondary cause of local and personal land-use conflict [45].

The risk of spatial conflict increases in communities and regions with diverse social characteristics and contradictory expectations towards land management. These differences are manifested by:


The specificity of land-use conflict has to be explored to expand our understanding of the scope and potential consequences of such conflicts, which could play a key role in rational and sustainable management of space [46–48].

The attributes of space where land-use conflicts occur usually have a local character. Land division, changes in land-use, and urbanization take place locally, and spatial conflicts are generally intensified by anthropogenic factors [49]. Some of these attributes can be identified and evaluated intuitively, whereas others become apparent only when a conflict erupts or when the causes of the conflict are analyzed in detail [50].

One of the goals of spatial planning is to limit the negative impact of conflict-generating attributes on human existence [51]. Such attributes have to be identified, eliminated, or minimized [52]. However, conflict-generating factors can be reliably assessed only if they can be unambiguously identified and described in the entire area under analysis. The above can be achieved with the use of GIS databases which are characterized by extensive thematic content and broad spatial coverage [53,54].

The characteristic attributes of space that incite spatial conflict usually evolve at the local level [55]. Land division, changes in land use, and urbanization are processes that occur locally, and spatial conflicts are fueled mostly by anthropogenic factors.

As previously mentioned, space has many attributes that can generate land-use conflict. Some of these attributes are recognized and evaluated intuitively, whereas others are identified only when a conflict erupts or after its causes have been thoroughly analyzed [49]. The authorities responsible for land management have to limit the negative effects of these factors on human life [56]. Therefore, conflict-generating factors need to be identified, eliminated, or minimized. However, such factors can be determined, and their conflict-generating potential can be evaluated only when these factors are unambiguously identified and described during the entire research process. Not all factors that increase the risk of spatial conflict are described; therefore, they are not listed in the databases that are accessible to analysts [57,58]. Undoubtedly, such factors can be described in individual cases for the needs of specific analyses [59]. However, this is a highly laborious process that is not always cost-effective. The identified factors also tend to have limited spatial coverage. Therefore, the use of the existing databases appears to be a much simpler and sensible solution [60].

Local space is usually described in considerable detail. Legal regulations and, increasingly often, market processes necessitate the creation of geospatial databases with different content and significance. Geospatial databases differ in the homogeneity, validity, and reliability of the accumulated information [61]. Databases developed by centers for geodetic and cartographic documentation appear to be the most robust sources of homogeneous, valid, and reliable data.

In view of the above, the main aim of this study was to develop a procedure for evaluating an area's vulnerability to land-use conflict based on the spatial attributes described by geodetic and cartographic databases and with the use of GIS tools.

#### **2. Materials and Methods**

#### *2.1. Study Area and Data*

For the needs of this study, the attributes of space that can potentially contribute to the risk of land-use conflict were identified based on an analysis of the existing databases and the relevant literature. Spatial attributes were selected for the study based on the extent to which they can be identified in space. The studied area was divided into comparative units for the needs of delimitation based on the results of the conducted analysis. Comparative units were created within the cadastral districts of the examined municipalities. This approach was adopted to ensure the cohesiveness of research stages, to minimize susceptibility to spatial conflict, and, potentially, to integrate the results with the findings

of social studies [62]. Cadastral districts are often associated with settlement units (in particular in rural areas) or city districts, which implies that they can be linked with local communities. Therefore, the extent to which the characteristic traits of local communities contribute to the risk of spatial conflict can be analyzed in greater detail.

Spatial features that can contribute to the intensification of land-use conflicts were identified with the use of GIS data and based on a review of the literature [63,64]. The analyzed traits were selected based on the extent to which they can be identified and based on the results of a survey involving fifteen teams of real estate and spatial planning experts, including researchers and practitioners such as land surveyors and real estate agents. In each of the fifteen teams, the experts and the moderator (leading researcher) discussed the extent to which the identified attributes contribute to spatial conflict. The results of these discussions were used to prepare 15 questionnaires and determine the conflict-generating potential of each attribute. A questionnaire survey was conducted between 20 May and 25 September 2020 in organizations employing real estate and spatial planning experts. This approach was adopted due to the complex nature of the undertaken research. Direct communication with the experts generated valuable insights about the studied topic. The two-tiered survey procedure also enhanced the objectivity of the results.

The study area was the rural municipality of Purda in the Olsztyn county, Region of Warmia and Mazury in Poland. The Region of Warmia and Mazury has a rather unique settlement structure. The region abounds in natural resources, and agriculture has long been the main source of income for the local population [65]. The rapid development of Olsztyn county drives growth in the surrounding municipalities, including Purda. The Purda municipality has an area of more than 300 km<sup>2</sup> , with a prevalence of agricultural land and forests. Due to local specificity and the proximity of a large urban center, Purda is susceptible to spatial conflict in both the social (progressive exploitation of natural resources resulting from local population growth) and the economic dimension (growing number of land development projects). The influx of new residents whose land-use preferences differ from those of the local population also drives conflict. These problems are typically encountered at the rural–urban interface. The location of Purda municipality is presented in Figure 2.

Purda is undergoing rapid urbanization, and it is an important source of land reserves for the spatial development of Olsztyn, the capital city of the Region of Warmia and Mazury. To facilitate a cohesive evaluation and classification of results, the municipality was divided into territorial units corresponding to cadastral districts. The availability of cohesive information regarding the intensity of the evaluated attributes in the analyzed units and the calculation of indices relating to every attribute played an important role in the selection process.

The analyzed municipality was divided into comparative units within the cadastral districts of Purda municipality. The aim of this procedure was to guarantee the consistency of the adopted measures for investigating possible triggers of land-use conflict and to consolidate our findings with research into social causes of conflict [66]. Cadastral districts are often equated with settlement units (in particular in rural areas) or city districts, which implies that they are closely linked to local communities [67]. The above assumption can be taken into account in spatial analyses to determine the extent to which the characteristic features of local communities can incite or exacerbate land-use conflict. The attributes were selected for analysis based on two considerations. The first consideration was the list of attributes that were identified by the surveyed experts. Each of the fifteen surveyed expert teams developed a list of attributes that could potentially contribute to spatial conflict. The second consideration was the extent to which the identified attributes could be measured with the use of the existing geospatial databases. Not all attributes can be evaluated in this approach. Databases characterizing local communities are not available. A total of 12 features that are described in the geospatial database were identified and assessed in 33 districts of Purda municipality.

**Figure 2.** Location of Purda municipality and the local land-use pattern. \* B—residential areas; Ba—industrial areas; Bi other built-up areas; Bp—urbanized areas that are not built-up or are under construction; Br—developed agricultural land; Bz—recreational areas; E—ecological sites; K—mining sites; Ls—forests; Lz—areas with tree and shrub cover; N—fallow land; Ps—permanent pastures; R—arable land; S—orchards; T—transport routes; Wp—flowing waters; Ws—standing waters; dr—roads; and Ł—permanent meadows.

Most conflict-generating features have anthropogenic origin. Land-use conflicts can also be driven by factors that are not always associated with human activity, including topography and various types of land cover such as water bodies and forests. However, human activities exert a considerable influence on these attributes of space [68,69].

The significance of the identified spatial features was determined. The extent to which the analyzed attributes contribute to land-use conflict was described with the use of dedicated measures or indices [70]. These indices and the relevant measurement methods are described below:

Length of cadastral plot boundaries (**A.1**)—expressed by the ratio of the total length of all cadastral plot boundaries to the area of the cadastral district.

Complex boundaries (**A.2**)—expressed by the ratio of the number of boundary points in cadastral districts to the area of the cadastral district.

Area of cadastral plots (**A.3**)—expressed by the average area of a cadastral plot in a cadastral district.

Density of cadastral plots (**A.4**)—expressed by the ratio of the number of cadastral plots to the area of the cadastral district.

Development density (**A.5**)—expressed by the ratio of built-up areas in a cadastral district to the area of that district.

Land-use homogeneity (**A.6**)—expressed by the land-use homogeneity index *SJ<sup>i</sup>* [71]:

$$Sf\_i = \sum\_{z=1}^{\mu} f\_{iz}^2 \tag{1}$$

where: *Jiz* is the proportion of a given land-use type (*z*) in the total area of cadastral district *i* and *u* is the number of land-use types in cadastral district.

Technical infrastructure—the availability of technical infrastructure was evaluated in three categories: roads (**A.7**), power grid (**A.8**), and the water supply network (**A.9**). All networks and grids were evaluated with the same key. Cadastral districts were assessed based on the proportion of plots without access to each of the three types of infrastructure in the total number of plots in that district. The availability of roads was defined as the presence of a road in the plot's immediate vicinity, whereas the availability of the remaining infrastructure was defined as the presence of the relevant utilities at a distance of up to 100 m from the plot. The studied municipality was inventoried based on the above principles, and the results are presented in Figure 3.

**Figure 3.** Division of Purda municipality into cadastral districts (**A**); Cadastral plots without access to: roads (**B**), power grid (**C**), and water supply network (**D**). Source: own elaboration based on data from the County Centre for Geodetic and Cartographic Documentation (PODGiK) data.

Topography (**E.1**)—topographic features were determined based on the maximum terrain curvature in the cadastral district. Terrain curvature was calculated based digital

elevation model data from the Municipal Center for Geodetic and Cartographic Documentation. Digital elevation model data were interpolated (Figure 4A), and terrain curvature was determined at the nodes of a regular grid with a side length of 100 m. The results were used to calculate the range of curvature values in area K in the analyzed cadastral districts. The calculated values were used to evaluate conflict-generating attributes in these districts (Figure 4B).

**Figure 4.** (**A**) Topography of Purda municipality; (**B**) Terrain curvature in Purda municipality. Source: own elaboration based on data from the Municipal Centre for Geodetic and Cartographic Documentation (GODGiK).

Boundaries of areas with natural land cover—this analysis included forests (**E.2**) and surface water bodies (**E.3**). This indicator was expressed by the ratio of the length of boundaries of forests and surface water bodies to the area of forests and water bodies in the analyzed cadastral district.

The results of the evaluation of conflict-generating attributes in the cadastral districts of Purda municipality are presented in Table 1.

#### *2.2. Methodology*

The identified attributes had a varied contribution to the risk of land-use conflict. These attributes were weighed to assess their importance. Shannon's entropy method [72] is widely used to determine the weights of various evaluation criteria. The concept of Shannon's entropy plays an important role in information theory, and it is regarded as a general measure of uncertainty. In transportation models, entropy is a measure of the dispersal of trips between the point of origin and the destination [73]. In physics, entropy represents the state of disorder of a system [74]. Entropy associated with an event is also a measure of the event's degree of randomness. The concept of entropy can also be used to measure fuzziness [75]. This method is relatively objective, and it can be effectively deployed in the decision-making process [76]. The entropy weight method is also widely used to determine the weights of criteria and attributes [77–80]. In the discussed method, relative importance is assigned to the analyzed attributes by measuring the existing differences between sets of data [81].


**Table 1.** Conflict-generating attributes in the cadastral districts of Purda municipality—matrix **M**.

\* all attributes were measured with the use of the described procedure in the analyzed units.

The identified conflict-generating attributes were used to build decision matrix **M** (Table 1), which was normalized with the following equation:

$$m\_{ij} = \frac{m\_{ij}}{\sum\_{i=1}^{k} m\_{ij}} \tag{2}$$

where *mij* is the results of the evaluation of conflict-generating attributes; *i* is the successive districts in the total number of districts *k*; and *j* is the successive attributes in the total number of attributes *z.*

The normalized matrix **N** with elements *nij* was used to assign weights to conflictgenerating attributes (Table 2).


**Table 2.** Elements of matrix **N** in the analyzed districts.


**Table 2.** *Cont.*

The information content of matrix **N** was determined by calculating the entropy value *E<sup>j</sup>* of every conflict-generating attribute with the use of the below formula [82]:

$$E\_j = -K \sum\_{i=1}^k n\_{ij} \ln n\_{ij} \tag{3}$$

where *i* is the successive districts in the total number of districts *k*; *j* is the successive attributes in the total number of attributes *r*; and *K* = 1/ln *k* (for *k* = 33 *K* = 0.29) is the constant value which guarantees that the value of *E* falls within the range of [0;1] (Table 3).




**Table 3.** *Cont.*

The differences in the value of *nij* relative to successive attributes were calculated with the following formula:

$$d\_{\dot{j}} = 1 - E\_{\dot{j}} \tag{4}$$

where *j* is the successive attributes in the total number of attributes *r*. The result is the attribute vector *d<sup>j</sup>* .

$$d\_{\circ} = \begin{bmatrix} 0.25 & 0.09 & 0.13 & 0.11 & 0.18 & 0.02 & 0.02 & 0.05 & 0.04 & 0.01 & 0.38 & 0.05 \end{bmatrix} \tag{5}$$

The value of *d<sup>j</sup>* was used to determine the weights *w<sup>j</sup>* of conflict-generating attributes with the use of the below formula:

$$w\_j = \frac{d\_j}{\sum\_{j=1}^r d\_j} \tag{6}$$

The result is the vector of attribute weights **W**.

**W** = [0.19 0.07 0.10 0.08 0.13 0.01 0.01 0.04 0.03 0.01 0.29 0.03] (7)

The normalized matrix **N** was multiplied by vector *W* to produce matrix **D**. The risk of land-use conflict in the analyzed districts was expressed by the sum of the rows of matrix **D**. The results were represented by indicator *V<sup>i</sup>* for improved readability.

$$V\_i = 100 \times \sum\_{j=1}^{r} (n\_{ij} w\_j) \tag{8}$$

where *i* is the successive districts in the total number of districts *k*.

The value of indicator *V<sup>i</sup>* can range from 0 to 100. Districts with higher values of *V<sup>i</sup>* are characterized by a greater accumulation of attributes that can give rise to spatial conflict.

#### **3. Results and Discussion**

Indicator *V<sup>i</sup>* was calculated for the analyzed districts, and the results are presented in Table 4. In Grabowo district, *V<sup>i</sup>* considerably exceeded the average value for the examined districts (27.60). The above result could be attributed to the very small area of Grabowo relative to other districts, as well as the fact that other attributes in Grabowo also substantially exceeded the average value. Grabowo was thus eliminated from classification. Only districts where the value of *V<sup>i</sup>* ranged from 0.00 to 10.00 were included in the classification.


**Table 4.** Value of *V<sup>i</sup>* in the analyzed districts.

Indicator *V<sup>i</sup>* was used to determine the risk of land-use conflict in the analyzed cadastral districts. Classification results are presented in Figure 5.

The analyzed districts were divided into five classes with the use of the geometric interval classification method. Class 1, with the highest accumulation of conflict-generating attributes, comprised only one district, Ostrzeszewo, where *V<sup>i</sup>* reached 9.90. Class 2, with a high accumulation of the analyzed attributes, contained districts subjected to considerable urbanization and human pressure. Classes 4 and 5, with the lowest accumulation of conflict-generating attributes, were composed of ecological sites (forests and water bodies) in their entirety or in most part. The analysis of land-use types in the Purda municipality indicates that the proposed method generates reliable results. The value of indicator *V<sup>i</sup>* was markedly higher in areas subjected to considerable anthropogenic pressure.

**Figure 5.** Values of indicator *V<sup>j</sup>* in the cadastral districts of Purda municipality.

#### **4. Conclusions**

One of the greatest challenges in land management is to minimize the "pretexts" for spatial conflict. Conflict-generating factors should be eliminated or minimized in all stages of the process, including land-use planning, land protection, and land management. The aim of all land management procedures and decisions should be to improve land-use comfort. Therefore, the identification of potential hotbeds of conflict is a very important consideration that tends to be overlooked in research.

In this study, attempts were made to fill in this knowledge gap. The present research is by no means exhaustive, but the results indicate that geospatial databases can be used effectively to reduce the risk of spatial conflict.

This study proposes a procedure for evaluating the risk of land-use conflict based on the attributes that increase the probability of such risk. Conflict-generating attributes were calculated based on data that are available in municipal centers for geodetic and cartographic documentation. The analysis was conducted with the use of GIS tools in ArcGIS 10.4.1 software.

The proposed procedure was tested in a rural municipality with a varied spatial structure in the Region of Warmia and Mazury. The physical and geodetic attributes of space were identified in considerable detail at the local level. A procedure for weighing and measuring the intensity of attributes that can compromise spatial harmony and contribute to spatial conflict was proposed. The importance of conflict-generating attributes was assessed with Shannon's entropy method, and indicator *V<sup>i</sup>* was calculated as the sum of

attribute weights. The procedure of calculating indicator *V<sup>i</sup>* involved well-known metrics as well as the measurement methods proposed by the authors. The results of the analysis indicate that anthropogenic factors are the main drivers of land-use conflict. Therefore, the developed procedure can be used to identify areas with a higher risk of land-use conflict. Conflict-prone areas should be identified based on analyses of cadastral data, local zoning plans and social participation to optimize spatial planning and improve the quality of life in local communities.

The results of the study demonstrated that the risk of land-use conflict is particularly high in the immediate vicinity of areas that are subjected to considerable anthropogenic pressure. Areas where human activities generate social controversy should be identified to promote sustainable land-use planning and prevent conflict.

The proposed procedure can be modified to account for specific research objectives, the characteristic features of the studied area, and access to spatial data. The developed methodology constitutes a valuable tool which supports planning and monitoring of sustainable land-use practices. The results of the analysis can be used to plan future actions with the aim of enhancing harmonious spatial development and minimizing the risk of land-use conflict.

**Author Contributions:** Conceptualization, I.C. and A.B.; methodology, I.C. and A.B.; software, I.C. and A.B.; validation, I.C. and A.B.; formal analysis I.C. and A.B.; investigation I.C. and A.B.; resources I.C. and A.B.; data curation, I.C. and A.B.; writing—original draft preparation, I.C.; writing—review and editing, A.B.; visualization, I.C.; supervision, I.C. and A.B.; project administration, I.C. and A.B.; and funding acquisition I.C. and A.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Analysis and Evaluation of the Spatial Structure of Cittaslow Towns on the Example of Selected Regions in Central Italy and North-Eastern Poland**

**Marek Zagroba , Katarzyna Pawlewicz and Adam Senetra \***

Department of Socio-Economic Geography, Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, Prawoche ´nskiego 15, 10-720 Olsztyn, Poland; mazag@uwm.edu.pl (M.Z.); katarzyna.pawlewicz@uwm.edu.pl (K.P.)

**\*** Correspondence: adam.senetra@uwm.edu.pl; Tel.: +48-89-5234948

**Abstract:** Cittaslow International promotes harmonious development of small towns based on sustainable relationships between economic growth, protection of local traditions, cultural heritage and the environment, and an improvement in the quality of local life. The aim of this study was to analyze and evaluate the differences and similarities in the spatial structure of Cittaslow towns in the Italian regions of Tuscany and Umbria and the Polish region of Warmia and Mazury. The study examined historical towns which are situated in different parts of Europe and have evolved in different cultural and natural environments. The presented research attempts to determine whether the spatial structure of historical towns established in different European regions promotes the dissemination of the Cittaslow philosophy and the adoption of sustainable development principles. The urban design, architectural features and the composition of urban and architectural factors which are largely responsible for perceptions of multi-dimensional space were evaluated. These goals were achieved with the use of a self-designed research method which supported a subjective evaluation of spatial structure defined by historical urban planning and architectural solutions. The study demonstrated that Medieval urban layouts can be successfully incorporated into the modern urban fabric to promote sustainable development and slow living.

**Keywords:** Slow City; small towns; spatial structure; sustainable development; old market square; historical urban layout

#### **1. Introduction**

The objective of the Cittaslow International network of small towns is to promote harmonious development and slow living. This philosophy offers an alternative to globalization and the fast pace of life in large cities [1–3]. Cittaslow towns implement sustainable development policies by creating a healthy balance between economic growth, protection of local traditions, cultural heritage and the environment, and improving the quality of local life [4–6]. The members of the Cittaslow network promote local and specific values and products, and they initiate active measures to protect the natural environment and the historical urban fabric. These policies build a sense of solidarity among local community members who feel responsible for their place of residence, including its past and future [7,8]. The unique spatial structure of historical towns plays an important role in building harmonious relationships and adapting these towns to contemporary needs and aspirations [9,10].

Small towns are human settlements that occupy a relatively limited area and organize their living space in a similar manner regardless of their location. The scale of urban development, small population, absence of industry and low levels of technical infrastructure relative to large cities contribute to sustainable development and reluctance towards modern trends that rely heavily on globalization and a fast pace of life.

**Citation:** Zagroba, M.; Pawlewicz, K.; Senetra, A. Analysis and Evaluation of the Spatial Structure of Cittaslow Towns on the Example of Selected Regions in Central Italy and North-Eastern Poland. *Land* **2021**, *10*, 780. https:// doi.org/10.3390/land10080780

Academic Editors: Luca Salvati, Iwona Cie´slak and Andrzej Biłozor

Received: 23 June 2021 Accepted: 22 July 2021 Published: 25 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Considerable research has been dedicated to the development and future outlook of urban expansion. Zambon et al. [11] performed a quantitative analysis of urban plans to predict future urban growth patterns. The authors relied on an extensive database of social and economic indicators to examine the consequences of urban expansion in the Spanish region of Catalonia which was divided into local zones for the needs of the study. They found that urbanization had contributed little to the sustainable development of towns in recent decades. The authors concluded that future urban development in Catalonia will not follow a polycentric model, but will fuel the growth of small and medium-sized towns. The historical center of Santiago de Chile was modernized based its architectural and urban heritage. The elements of the city's original urban fabric were recreated and used to propose a new urban development concept. This approach was adopted to bridge the gap between modern requirements and the city's colonial and republican past [12]. In Senigallia, Italy, the open-design approach was implemented to regenerate the town's historical urban heritage through participatory design [13]. The town's historical district was redesigned with the use of Multicriteria Decision Analysis (MCDA). Historical districts pose a challenge for urban designers who have to reconcile conservation with modern welfare and safety standards. The MCDA approach was validated experimentally in Biella, Italy [14].

The aim of this study was to analyze the differences and similarities in the spatial structure of small towns belonging to the Cittaslow network. The evaluated towns are located in the Italian regions of Tuscany and Umbria and the Region of Warmia and Mazury in north-eastern Poland. Based on the aim of the study, the authors made an attempt to answer the following research questions:


#### **2. Cittaslow International**

Current trends in urban and economic development favor measures that strengthen the growth potential of mainly large urban agglomerations. Rapid globalization has contributed to a faster pace of life. The citizens of highly developed countries show a growing appreciation for the slower approach to everyday life in smaller settlement units [15,16]. In small and medium-sized towns, most businesses operate only on the local market. These towns often collaborate and join various organizations to expand their economic potential. One of such organizations is the Cittaslow Movement, an international network of cities that was founded in 1999 to endorse the Slow City concept. Towns can apply for Cittaslow membership if their population does not exceed 50,000, and if they support and implement the organization's goals [17]. Candidates are evaluated in seven areas: energy and environmental policy, infrastructure policies, quality of urban life policies, agricultural, tourism and artisan policies, policies for hospitality, awareness and training, social cohesion and partnerships, based on 72 detailed criteria. Towns that meet at least 51% of these requirements are eligible for membership. They are re-evaluated (re-certified) based on the same criteria after five years [18].

The overreaching goal of the Cittaslow movement is to improve the quality of life, promote economic growth, protect the natural environment and cultural heritage, promote regional products, crafts, traditions and customs [19]. The movement advocates a slower pace of life as an alternative to the hustle and bustle of large cities and metropolises. The organization does not oppose technological progress, and its members are local economic hubs that place a high value on preserving their unique character [8]. Cittaslow members promote their towns as places with a high quality of life in the proximity of nature, highquality products and strong local communities [20].

The goals of the Cittaslow movement coincide with the principles of sustainable development, with the main emphasis on increasing local incomes, improving local safety, fostering a friendly social environment, improving the quality of the natural environment, enhancing spatial order and the functionality of urban spatial structures [21]. Sustainable cities are characterized by a cohesive relationship between the urban and the natural environment. Social and environmental movements play an important role, and they focus on preserving the city's unique character, restoring historical architecture, promoting local products and cuisine. In cities that are popular tourist destinations, considerable emphasis is placed on the promotion of local hospitality traditions [16,22]. Globalization increases the risk of cultural uniformity by destroying local values. The Cittaslow concept stimulates local growth as the core element of sustainable development. Member towns strive to preserve their identity and protect local values, both in the Cittaslow network and in the global context [7]. Sustainable development encompasses the environmental, social and economic aspects of urban growth. Community participation plays an important role in decision-making, and participatory processes further the sustainable development of all Cittaslow members at the local level. Cittaslow towns initiate a variety of events and projects with the involvement of the local authorities and residents, which is why the Cittaslow network is also regarded as a social movement and a local governance model [23].

The spatial layout and the potential of small historical towns are well aligned with the main tenets of the Cittaslow movement which are strongly rooted in sustainability [24]. Towns that boast the snail logo strive to improve the quality of local life and cultivate the culture of good living. The small size of urban structures, the cozy ambience of public spaces (squares, streets, parks) and small population encourage the preservation of local traditions and the search for harmony between a slow-paced life and sustainable development [25,26]. Historical towns are characterized by strong place identity, which promotes social integration and fosters a sense of attachment to one's home [27,28]. In towns with historical urban design and architecture, the residents are more likely to identify with and feel responsible for their place of residence than large city dwellers. These factors contribute to the search for novel urban planning solutions that are consistent with the Cittaslow philosophy of a slow life and eco-friendly development [27,29]. Historical structures in small towns well serve the above goals.

The philosophy and ideological assumptions of Cittaslow movement also fit in the evolution of spatial and urban solutions. Changes in various fields of life (economy, culture, etc.) have influenced the changes in its style. Moreover, they have an impact on the physical side of the anthropogenic space [30]. Globalization and computerization have contributed to the modification of social relations and homogenization of space in all dimensions [31]. The dominant role is played by the flow of information, idea, capital, goods and also people [32]. The quality of life indices for the certification and monitoring of the member cities are the premises of the local councils' actions. The modern ideas of spatial planning allow claiming that the sustainable development and the idea of Slow City are based on similar, or even often the same, assumptions. Duplicating the same norms in sustainable planning and the idea of the movement has become the norm emphasized in the modern debates on cities development. Consequently, the social awareness increase, spatial structures are harmonized, the elements of the environment are respected and the results of intensive urbanization are balanced. As a result, all of the above-mentioned elements become the widely acknowledged rule of good practices in planning and spatial management [30]. Moreover, they have the chance to be more widely implicated in other, more complex and more urbanized spatial arrangements.

The integral element of city development is a constant process of changes in which one of the leading roles is assigned to the processes of revitalization. It is a process that includes comprehensive operations in old towns, old districts neglected in terms of the economy, aesthetics, space, infrastructure, communication and functionality. In numerous points, the process converges with the idea of Slow City and sustainable planning. It leads to the prevention of social exclusion, facilitates socio-economic development and it also leads to

the improvement of the quality of life of the local society. The revitalization process must be conducted according to the principles of sustainable development with the integration of social, spatial, economic, cultural, construction, investment and ecological aspects within the determined aim [33,34]. The quoted scientific and technical discourse fully confirms the necessity for the integration of the idea of Slow City, the sustainable development and technical actions (f.eg. revitalization) in a widely-acknowledged planning and land use of Cittaslow towns.

#### **3. Materials and Methods**

#### *3.1. Study Areas*

The study areas were small towns, which are Cittaslow members, in the Italian regions of Tuscany and Umbria, and the Polish region of Warmia and Mazury (Figure 1).

**Figure 1.** The analyzed regions. Source: own elaboration.

The reason for choosing Cittaslow network for the study is the fact that it is a consistent network of small towns. Moreover, it gives the possibility of conducting the research on various urban organisms. While achieving the objectives, Cittaslow solicits raising the effectiveness of economic growth, it puts emphasis on a high level of education and helps in reducing poverty; it also develops newer and newer innovative economic projects. The objectives fit in the ideas of sustainable development. Among strong points of Cittaslow network, one can list [35]:


Cittaslow network unites mostly historical towns which drew the attention of inter alia the scientists from Massachusetts Institute of Technology City Science (USA). They stated: "We went back in time and looked at towns in which we all would like to be—a lot of them belong to the historical European towns". They think that towns that work the best are towns that had been organized before the car was invented. The towns include districts with all essential everyday life facilities. It is the so-called [36].

The value of small towns is emphasized also in The European Commission studies on the basis of which one can state that [37]:


These regions were selected for the study due to similarities in their spatial layout. The analyzed regions are highly attractive and popular tourist destinations that are widely visited by domestic tourists as well as tourists from other countries in Europe and the world. The popularity of the studied regions can be attributed to their unique environment, landscape, topographic features and cultural heritage, including historical urban fabric and world-class monuments such as castles, churches and parks [38–41]. Moreover, Tuscany is the cradle of the Cittaslow movement, whereas Warmia and Mazury is a region with the highest number of Cittaslow towns in Poland. One should mention that Polish National Cittaslow Network is the second in the terms of the number of town members in the world [35].

Small towns are characterized by similarities and differences in their topographic features, historical factors which influenced the evolution of urban structures, culture, habits, building traditions, as well as climate which shaped the local architecture. The above factors are responsible for the differences and similarities in the spatial structure of small towns, but towns that are members of the Cittaslow network pursue similar goals and strive to fulfill modern needs and aspirations. The analyzed towns differ considerably in population which ranges from 505 to nearly 28,000 in the studied Italian regions, and from 1964 to more than 23,000 in the Polish region.

#### 3.1.1. Italian Towns

Tuscan and Umbrian towns are much older than their Polish counterparts. Some Italian towns were established in Roman times, and they are regarded as the cradles of European civilization [42]. The founding dates of some towns cannot be determined accurately due to the turbulent history of the Italian peninsula. Small settlements and Roman military camps were transformed into towns that were destroyed and reconstructed on numerous occasions over the centuries. Other towns were built as part of the urbanization effort of the Roman Empire [43,44]. Topographic features and other environmental factors significantly influenced the spatial structure of Italian towns, most of which have a highly irregular street pattern [45]. However, these factors have little bearing on the preserved urban fabric which dates back to later periods in history. The present size and population of Italian towns is unrelated to topographic features [46]. The territorial reach of Italian towns continued to expand as their economies grew in successive centuries, but their original layout has been preserved to this day. The study covered 15 towns in Tuscany and 11 towns in Umbria:


#### 3.1.2. Polish Towns

The founding dates of towns in the Polish region of Warmia and Mazury are much easier to determine. Source documents citing charters that granted town rights to settlements in Warmia and Mazury have survived to this day. The relevant dates are also easier to establish because Polish towns were founded later than their Italian counterparts. Warmian and Masurian towns were granted charters because they were established on virgin territory. Their founding dates are indicated in historical documents [47].

The analyzed towns have evolved over the centuries. Their present size and population differ significantly from the original figures, but similarly to Italian towns, their historical layout can still be identified. The study analyzed 22 towns in the Polish Voivodeship of Warmia and Mazury: Barczewo, Bartoszyce, Biskupiec, Bisztynek, Braniewo, Dobre Miasto, Działdowo, Gołdap, Górowo Iławeckie, Jeziorany, Lidzbark Warmi ´nski, Lidzbark, Lubawa, Nidzica, Nowe Miasto Lubawskie, Olsztynek, Orneta, Pasym, Reszel, Ryn, S ˛epopol, and Wydminy.

#### *3.2. Methods*

Multidimensional analyses and evaluations are required to assess the multiperspective functioning of historical towns and the ability of their spatial structures to meet the needs and aspirations of modern communities. The evaluated towns are characterized by similarities as well as differences in this respect. The objective of the applied original research methods was to verify the assumption that sustainable municipal development is closely related with spatial attributes. Some of the applied methods were based on the approaches that are widely described in the literature [13,24,27,28,46–55], and they involved analyses of the current status of historical urban layout. The remaining methods were developed by the authors, and they rely on subjective evaluations of the towns' spatial structure which is defined by the historical urban fabric and architectural features. This approach is largely universal, and it can be applied in towns situated in various regions of the world. The spatial structure of the analyzed towns was assessed with the use of three criteria:


3.2.1. Multi-Criteria Evaluation of the Spatial Structure of Small Towns

The aim of this evaluation was to determine the extent to which a slower pace of life in small cities contributes to sustainable development. Due to the large number of analyzed towns (22 towns in the Polish region of Warmia and Mazury, 15 towns in Tuscany, and 11 towns in Umbria, Italy), a quantitative research method was applied to identify and systematize the key elements that determine the quality of urban spaces. The following elements were taken into consideration:

	- towns that evolved from the existing settlements;
	- towns built around Roman military camps;
	- towns built in cruda radice (from a "raw root"), usually along or at the crossroads of major transportation routes.

Over the centuries, continued economic growth led to the territorial expansion of the studied towns. The towns' original and present area was compared, and the attractiveness of historical urban fabric relative to modern forms of urban development was evaluated with the use of the following indicator:

$$\text{IDA} = \text{PA} / \text{TAF} \tag{1}$$

where: *IDA*—increase in developed area; *PA*—present area; *TAF*—town area at the time of foundation.


Source: own elaboration based on Tołwi ´nski [48].

**Table 2.** Correlations between landform and urban planning solutions.


Source: own elaboration based on Czubiel and Domagała [47].

**Table 3.** Shape and size of market squares in the studied towns.


Source: own elaboration based on Czubiel and Domagała [47].

#### 3.2.2. Evaluation of the Influence of Spatial Attributes on the Townscape

This stage of the research involved cartographic methods as well as field surveys of Polish towns and selected Italian towns. Urban structures were evaluated by directly experiencing the distinctive atmosphere of historical towns. Direct observations were supplemented by analyses of photographs and video footage. Iconographic sources such as maps and satellite images were also used to examine the key elements of the urban structure. These research methods supported assessments of individual spatial attributes which stimulate the senses and enable spectators to experience the unique/distinctive atmosphere of a place (*genius loci*). The following factors were taken into consideration:

	- Italian towns with a free-form urban pattern and an irregular street network, where the junction of the main roads acts as a focal point in the town center;
	- Polish towns with a grid street pattern, rigid compositional axes and a clearly defined central area.

The urban forms in both Polish and Italian towns are aesthetically pleasing, but they evoke completely different perceptions of urban space (Table 4).


**Table 4.** The influence of urban design on perceptions of space.

Source: own elaboration based on Hall [49], Wejchert [51], Gzell [54].


were used to estimate the date of construction, identify the architectural style and assess the authenticity of the analyzed buildings (Table 7).

5. Scale of urban development—the area of the studied towns was analyzed in view of historical changes in their territorial reach relative to their original area. The territorial expansion of Medieval towns was an inevitable consequence of their economic growth. The historical architecture in the center of small towns has to be preserved against the background of modern development to maintain the esthetic composition of the urban structure. This is a necessary prerequisite for preserving the unique identity of historical towns, and it demonstrates that towns are living organisms that evolve over time (Table 8).



Source: own elaboration based on Wejchert [51], Montgomery [53].



Source: own elaboration based on Burgess [28], Norberg-Schulz [52], Radwan [55].

**Table 7.** The influence of urban components on perceptions of the townscape.




Source: own elaboration based on Norberg-Schulz [52], Zagroba and Gawryluk [24].

**Table 8.** Factors responsible for the territorial expansion of the studied towns.


Source: own elaboration based on Alexander [27], Benevolo [46], Rossi et al. [13].

The factors listed in Table 8 were used to evaluate the quality of urban space in historical towns. The quality of the historical urban fabric is determined by perceptions of structural archetypes, distinctive features that contrast with contemporary urban structures outside the historical center, as well as compositional factors such as the scale of urban development and urban forms.

#### 3.2.3. Analysis and Evaluation of the Spatial Structure of the Studied Towns

An intuitive (holistic) research method was applied to determine the extent to which the distinctive ambience (*genius loci*) of historical towns shapes the urban landscape in the evaluated regions and contributes to an improvement in the local standard of living. The influence of urban design elements, including structures, buildings, town blocks, streets and squares, on the quality of urban space was evaluated in Polish and Italian towns. Historical architecture and urban design are manifestations of a region's cultural heritage, and they evoke strong emotional responses. The analysis revealed strong links between the quality of local life and the protection of heritage sites. These relationships have evolved differently in Italian and Polish towns, which can be attributed to differences in culture, climate and history (Table 9).


**Table 9.** Preservation of historical spatial components in the studied towns.

Source: own elaboration based on Rossi [50], Norberg-Schulz [52].

#### **4. Results**

The data collected with the use of the described research method were interpreted based on three criteria for analyzing the spatial structure of the studied towns. The results of the study are presented separately for each stage of the conducted research for greater readability.

#### *4.1. Multi-Criteria Evaluation of the Spatial Structure of Small Towns–Results*

Italian towns differ considerably in founding dates because they were established from Roman times to the Medieval period (Figures 2 and 3).

Most of the analyzed Polish towns were established in same century (Figure 4).

The spatial structure of Italian and Polish towns was determined by topographic features and environmental conditions. All towns were founded in areas with favorable terrain characteristics (hill with steep slopes, river bend, bank of a river or another water body) which acted as natural barriers and protected the inhabitants against enemies (Figure 5).

A centrally located market square has always occupied an important place in the spatial structure of the studied towns. The size and shape of historical market squares differ considerably in the analyzed urban structures, but most towns have rectangular market squares (Figure 6).

The size of urban structures in Medieval towns was determined by the towns' economic status and technical capabilities (Tables 10–12). The average area of Medieval towns within fortified walls was determined at 3.89 ha in Tuscany, 6.04 ha in Umbria, and 5.37 ha in Warmia and Mazury. Over the centuries, economic growth fueled the expansion of the analyzed urban structures outside defensive walls. Tables 10–12 present the developed area of Italian and Polish towns, not the area enclosed by their administrative boundaries (which includes undeveloped areas such as forests, water bodies and agricultural land). Only built-up areas with the accompanying green infrastructure were measured based on orthophotomaps.

The area enclosed by fortified walls was used to calculate the relative area of the market square, which was expressed as the ratio of market square area to developed urban area (Figures 7 and 8). The average relative market square area was determined at 2.15% in Tuscany, 1.91% in Umbria, and 10.92% in Warmia and Mazury.

As demonstrated by Figures 9 and 10, the studied towns continued to expand dynamically throughout history. The greatest territorial expansion was noted in the 19th and 20th centuries. The character and density of urban development also evolved, and this process was accompanied by changes in urban design and architectural style. The average increase in developed area was determined at 19.89% in Tuscan towns, 16.50% in Umbrian towns, and 25.35% in Polish towns in the Region of Warmia and Mazury.

**Figure 2.** Founding dates of Tuscan towns. Source: own elaboration based on the data published by the Italian National Institute of Statistics [61] and Atlante Storico Iconografico Delle Città Toscane [62]. Key: n.e.d—no exact data available.

**Figure 3.** Founding dates of Umbrian towns. Source: own elaboration based on the data published by the Italian National Institute of Statistics [61] and Ross [63]. Key: n.e.d—no exact data available.

**Figure 4.** Founding dates of Polish towns. Source: own elaboration based on Czubiel and Domagała [47].

**Figure 5.** Topographic features in Polish and Italian towns. Source: own elaboration based on cartographic analyses. Key: 1—Polish 9 towns: Bartoszyce, Biskupiec, Braniewo, Lidzbark Warmi ´nski, Nidzica, Nowe Miasto Lubawskie, Olsztynek, Reszel, S ˛epopol; 2—Italian 2 towns: Maradi (T), San Vicenzo (T); Polish 3 towns: Bisztynek, Działdowo, Gołdap; 3—Polish 3 towns: Barczewo, Dobre Miasto, Lubawa; 4—Italian 23 towns: Abetone Cutigliano (T), Anghiari (T), Barga (T), Bucine (T), Capalbio (T), Castelnuovo Berardenga (T), Civitella in val di Chiana (T), Cortona (T), Massa Marittima (T), Pratovecchio (T), San Miniato (T), Suvereto (T), Amelia (U), Citta della Pieve (U), Ficulle (U), Monte Castello di Vibio (U), Montefalco (U), Orvieto (U), Parrano (U), Preci (U), Todi (U), Torgiano (U), Trevi (U); Polish 3 towns: Jeziorany, Lidzbark, Orneta; 5—Polish 4 towns: Górowo Iławeckie, Pasym, Ryn, Wydminy; 6—Italian 1 town: Greve in Chianti (T); (T)—Tuscany, (U)—Umbria.

**Figure 6.** Shape of market squares in Italian and Polish towns. Source: own elaboration. Key: 1—Italian 2 towns: Bucine (T), San Vicenzo (T); 2—Polish 6 towns: Działdowo, Gołdap, Lidzbark Warmi ´nski, Lubawa, Nowe Miasto Lubawskie, Reszel; 3—Italian 7 towns: Capalbio (T), Marradi (T), Suvereto (T), Monte Castello di Vibio (U), Orvieto (U), Todi (U), Torgiano (U); Polish 15 towns: Barczewo, Bartoszyce, Biskupiec, Bisztynek, Braniewo, Dobre Miasto, Górowo Iławeckie, Jeziorany, Lidzbark, Olsztynek, Orneta, Nidzica, Pasym, Ryn, Wydminy; 4—Italian 2 towns: Castelnuovo Berardenga (T), Greve in Chianti (T); 5—Italian 1 town: Montefalco (U); 6—Italian 9 towns: Barga (T), Civitella in val di Chiana (T), Cortona (T), San Miniato (T), Citta della Pieve (U), Preci (U), Ficulle (U), Parrano (U), Trevi (U); Polish 1 town: S ˛epopol; 7—Italian 5 towns: Abetone Cutigliano (T), Anghiari (T), Massa Marittima (T), Pratovecchio (T), Amelia (U).



Key: zero values denote cities without fortified walls; contemporary built-up area—urbanized area (developed area with the accompanying green infrastructure). Source: own elaboration based on Cittaslow Italia [64] and Google Maps [65].


**Table 11.** Umbrian towns—developed area.

Key: contemporary built-up area—urbanized area (developed area with the accompanying green infrastructure). Source: own elaboration based on Cittaslow Italia [64] and Google Maps [65].



Key: zero values denote cities without fortified walls; contemporary built-up area—urbanized area (developed area with the accompanying green infrastructure). Source: own elaboration based on Cittaslow Polska [66] and Google Maps [65].

**Figure 7.** Relative market square area in Italian towns. Source: own elaboration. Key: zero values denote cities without fortified walls.

**Figure 8.** Relative market square area in Polish towns. Source: own elaboration.

**Figure 9.** Increase in the developed area of Italian towns. Source: own elaboration. Key: zero values denote towns where the historical urban fabric was not preserved.

**Figure 10.** Increase in the developed area of Polish towns. Source: own elaboration.

#### *4.2. Evaluation of the Influence of Spatial Attributes on the Townscape—Results*

An analysis based on the adopted criteria (Tables 4–8—urban layout, public urban spaces such as municipal squares, pocket parks and municipal parks, architecture, scale of urban development) revealed that historical solutions considerably influenced urban planning in successive centuries. Historical town centers are characterized by different scale, density and type of development than other built-up areas, and they constitute the focal points in the studied towns (Tables 13–15).


**Table 13.** Preservation of historical spatial components in Tuscan towns.

Source: own elaboration. Key: Preserved Not preserved Partly preserved.

**Table 14.** Preservation of historical spatial components in Umbrian towns.


Source: own elaboration. Key: Preserved Not preserved Partly preserved.

**Table 15.** Preservation of historical spatial components in Polish towns in the Region of Warmia and Mazury.

Source: own elaboration. Key: Preserved Not preserved Partly preserved.

#### *4.3. Analysis and Evaluation of the Spatial Structure of the Studied Towns—Results*

The distinctive atmosphere (*genius loci*) of a town is determined by various components of the urban fabric, including material factors as well as non-material factors which cannot be quantified, but which influence the human senses and are responsible for esthetic perceptions of space. The studied towns differed considerably in subjective perceptions of esthetic appeal (Tables 16–18).

**TUSCANY Criteria for Evaluating the Preservation of Historical Spatial Layout Abetone Cutigliano Anghiari Barga Bucine Capalbio Castelnuovo Berardenga Civitella in val di Chiana Cortona Greve in Chianti Marradi Massa Marittima Pratovecchio San Miniato San Vicenzo Suvereto** *Genius loci* of the urban fabric Source: own elaboration. Key: Preserved Not preserved Partly preserved. **Table 17.** *Genius loci* in Umbrian towns. **UMBRIA Criteria for Evaluating the Preservation of Historical Spatial Layout Amelia Citta della Pieve Ficulle Monte Castello di Vibio Montefalco Orvieto Parrano Preci Todi Torgiano Trevi** *Genius loci* of the urban fabric Source: own elaboration. Key: Preserved Not preserved Partly preserved. **Table 18.** *Genius loci* in Polish towns in the Region of Warmia and Mazury. **WARMIA AND MAZURY Criteria for Evaluating the Preservation of Historical Spatial Layout Barczewo Bartoszyce Biskupiec Bisztynek Braniewo Dobre Miasto Działdowo Gołdap Górowo Iławeckie Jeziorany Lidzbark Warmi ´nski Lidzbark Lubawa Nidzica Nowe Miasto Lubawskie Olsztynek Orneta Pasym Reszel Ryn S ˛epopol Wydminy***Genius loci* of the urban fabric Source: own elaboration. Key: Preserved Not preserved Partly preserved.

**Table 16.** *Genius loci* in Tuscan towns.

#### **5. Discussion**

Cittaslow towns are conjoined with a rich and long history as well as the charm of places ostensibly frozen in time. While analyzing the localization conditions of the Cittaslow network, it is hard to find any regularity. They represent different geographical, historical and most of all spatial types. They are conjoined mainly by the fact that they have historical centers and/or protected areas. Cultural heritage, valuable landscape, local culture and language are appreciated by Cittaslow towns' authorities, inhabitants and tourists. "Slow" cities lay emphasis on all of the values and owing to that they get unique character [67,68].

Historical planning solutions elicit the strongest emotional responses in perceptions of urban space; they contribute to a sense of local identity and create a sense of responsibility for one's place of residence. The protection and promotion of these values constitute the fundamental tenets of the Cittaslow philosophy [69,70]. It should be mentioned that towns that join Cittaslow network commit themselves to keep the cultural heritage connected with the way of living and living conditions [71,72]. Therefore, they achieve the goals of the movement connected with taking care of the historic urban fabric, renovating monuments and towns' aesthetics [73]. It is connected with maintaining and cultivating their historical identity which is not always connected with the functional demands of modern times. Technical and technological development, fire regulations or the civilization progress in general are a challenge in adjusting historical structures to the modern demands. Unlike big cities, small towns have no possibilities for developing industry, building large shopping centers or thoroughfare [74]. The analyzed Polish and Italian towns have many similarities, such as the size of the original urban settlement. However, they differ in other respects, which can be attributed to cultural and climatic differences. Historical urban planning solutions play an important role in this comparison because they testify to the towns' rich cultural heritage which followed European trends in urban design and architecture. The analyzed urban layout is also indicative of differences in local building traditions [75–77]. Qualitative improvement of the built environment should acknowledge local divergence and models as the manifestation of local identity. The greater awareness of key social factors in urban planning and creating architecture influence the higher potential of social sustainability of residential environment [78]. The extent to which urban design components determine the attractiveness of historical towns was evaluated based on the results of field surveys and analyses of iconographic materials. This is a very important consideration in small towns where the physical attributes of space are closely related with the spiritual needs of public space users. It is particularly visible in Slow Cities that apart from rich cultural heritage also have other important endogenous assets: widely underestimated bond and social awareness, and cooperation skills that stem from a deep sense of belonging to the inhabited place [67,79]. Consequently, the inhabitants feel like taking responsibility for their town which is definitely easier to be done in small local societies, where unlike in cities, the bonds are usually really strong [26,74,80]. The applied research methods supported the identification of links between the quality of historical urban space and the local standards of living. These methods were also successfully used to determine why small historical towns uphold the Cittaslow philosophy and whether different urban layouts in Polish and Italian towns create equal opportunities for implementing the principles of the slow living movement.

The analyzed Italian towns do not differ considerably in urban layout despite the fact that they were founded in different periods of time. The free compositional arrangements are dominant in Italian towns, which could have been connected with transforming former settlements, topography, climate and historical conditioning [81]. The factors, regardless of the widely-acknowledged in the Middle Ages rules of town layout, based on the chessboard layout, made the structure of Umbria and Tuscany to exceed the rules of urban composition created centuries ago [82,83]. Regardless of their founding date, the compared urban structures have many similarities, which suggests that climate and local architectural traditions played a key role in the development of urban forms. The layout of Polish towns is completely different than Italian towns' layouts. It corresponds to the general rules for founding a town that were current in the Medieval Times. Polish towns are characterized by more regular layouts which are based on the chessboard layout with a regular market in the middle and a perpendicular grid of streets. [45,48]. Nearly all of the evaluated towns in the Region of Warmia and Mazury have a regular, geometric street pattern with a centrally located market square, a fortified castle and a church.

Towns founded in different historical periods are also characterized by varied urban layouts. A grid street pattern is rarely encountered in Italian towns. In these towns, narrow streets are chaotically distributed, and dense development offers shade and refuge in the hot summer months. In the Middle Ages, dense settlement also increased security because smaller areas were easier to defend against enemies. That feature characterizes towns in the whole Europe [84]. Most of the evaluated towns abound in historical architecture which makes a reference to local building traditions and creates urban forms that are characteristic of the cultural landscape of Tuscany and Umbria. A completely different urban layout was observed in Polish towns. Warmian and Masurian towns were founded on a rectangular street plan, and they were divided into blocks of dense development with a market square in the center [85]. The original urban layout is still legible in most towns. Despite the above, the historical architecture of Polish towns was largely destroyed at the end of World War II. Consequently, the most valuable and the oldest parts of buildings, sometimes the vivid symbols, buildings acknowledged as the elements that shaped the individual landscape of the towns were degraded. Unfortunately, the post-war reconstruction effort, as well as their development, were very often unsuccessful and they often worked the opposite to the historical functional layout, arrangement and the scale of building that had been functioning in historical Polish towns for centuries [33,86]. At that time Poland belonged to the Eastern Block which negatively influenced the maintenance of historical identity of the towns. The post-war reconstruction was done without respect for historical assumptions which consequently led to losing towns' historical character [87].

Polish and Italian towns were established in areas with different landform and natural features. Most Italian towns were founded on steep hills that are ubiquitous in Tuscany and Umbria [88–90]. Hills are local topographic features which acted as natural barriers and protected towns and their inhabitants. In contrast, north-eastern Poland abounds in rivers and lakes which were the main focal points during the establishment of Medieval towns. Many Polish towns were nested in the bend of a river. It was done mainly because a river was indispensable for life. It provided water for people and animals, allowed for gathering food, watered crops, and fertilized soils. Close vicinity to rivers had also served military purposes [91]. Security was of the utmost concern in Medieval times, and towns were founded in areas where local terrain features offered effective protection. Moreover, a river facilitated trade and communication enabled professions that required the presence of water; it allowed draining the rain and urban pollution or using energy resources of river flow. Its close vicinity to a large extent influenced the economic development and it also had an impact on political, demographical and social relations of towns located close to the river [91].

The size of Medieval towns was determined by their ability to satisfy the residents' basic needs, and the complex process of building fortified walls favored dense development. Throughout the centuries, defensive walls limited the territorial expansion of cities whose size was fairly similar and representative of the level of technological advancement in the Middle Ages [92]. The average area of towns enclosed by fortified walls was similar in all studied regions, although it was somewhat larger in Italian towns (Cortona, Amelia and Todi). Polish towns were characterized by similar size, with the exception of Gołdap and Ryn.

Traders sold their goods in centrally located market squares which fueled the economic growth of Medieval towns. The market square was the heart of the town, and its location influenced the urban development pattern in the remaining parts of the town. The size and shape of the market square were determined by the town's area and street pattern [92]. The vast majority of towns in the Region of Warmia and Mazury have rectangular and square markets. Only three market shapes were identified in this region, which could be attributed to the fact that Polish towns were founded in the same historical period. The shape of market squares in Italian towns is more varied. A trapezoidal shape was most frequently encountered, followed by rectangular markets and markets with a complex shape. A total of seven market shapes were identified in the study, but none of the analyzed Italian towns had square-shaped markets. The observed variations are associated with considerable differences in the founding dates of Italian towns.

The functions of market squares have been expanded in the modern times. Market squares are not only the hubs of commercial and economic activity, but they also serve representative and recreational purposes [93,94]. They are historical landmarks that contribute to a sense of local identity and attract tourists and potential investors [95]. The relative area of the market square differs considerably between Italian and Polish towns. The studied towns in the Region of Warmia and Mazury are characterized by significantly higher relative market area which was determined at 10.74% on average and reached up to 20% in Nidzica and Ryn. In Italian towns, relative market square area approximated 10% only in San Miniato (Tuscany). These variations can be attributed to differences in founding dates. Polish towns were established later than their Italian counterparts, and most of them were not built around the existing settlements, but from a "raw root". From the beginning of their existence, Polish towns were adapted to local needs, and the relative proportions of the market square to the remaining forms of urban development could be freely shaped.

Towns are living organisms that continue to evolve, and their territorial reach changes throughout time. The growth and expansion of towns are determined by their regional and supra-regional status, economic performance, quality of space, and standards of living [96–98]. These factors influence the rate of urban expansion in the course of decades and centuries. In Polish towns, built-up area increased most rapidly in the 19th century when small factories (mills, breweries, lumber mills, brick factories) and railway lines were built in the Region of Warmia and Mazury [47]. The expansion of Italian towns was less dynamic due to topographic barriers (closed-form cities established on hills).

Preservation of historical spatial components significantly influences the shaping and perceptions of urban space. The elements, such as urban layouts, i.e., streets, squares, urban interiors and architecture, create the picture of historical changes. The elements to a large extent influence the spatial identity of towns [99]. In the towns of the analyzed areas, the differences are significant. All preservation criteria have been met in Umbrian towns, excluding Torgiano and, partly, Preci. The historical environment of Tuscan towns has also been successfully preserved (excluding Bucine, Civitella in val di Chiana, Pratovecchio, San Vicenzo and, partly, Abetone Cutigliano). The historical urban layout of Polish towns has been partly maintained or lost. Only the first two criteria in Table 15 have been preserved to a satisfactory degree.

The genius loci notion is indispensably connected with the perception of town space. It is not material, the places are special. They are the source of spiritual experience connected with history, the meaning, symbols and aesthetics which are individually perceived. Urban space, individual in every case, is prestigious, "the tokens" of a town with which the inhabitants identify. This relation equals the sense of belonging to a given cultural circle [100]. The significance of the places is unquestionable as it is consolidated in social awareness by particular events, representational buildings or functions [101]. In urban space, it is significant as town planning and architecture enrich the spiritual sphere, they are born in social space which remains as the space full of meanings and symbols, especially in historical towns [102]. For this reason the genius loci is the most subjective and elusive factor in evaluations of historical urban forms. Historical downtown areas differ considerably from the remaining parts of the urban fabric, and they have distinctive features that have been adapted to contemporary needs without a loss of local identity. The results of the study revealed that the preservation of historical spatial components

is directly responsible for the unique ambience of historical towns. This observation is validated by the fact that Torgiano and Preci are the only Umbrian towns without a genius loci. There are no discernible elements that would signify the rich history of the town. The urban layout and architecture do not have hallmarks of historical solutions as space is organized rather in a modern way. In Tuscany, the pervading spirit of a place is also most strongly manifested in towns whose historical environment has been successfully preserved. Polish Medieval towns have been most severely deprived of their historical ambience, and the genius loci has been retained in only six towns (Działdowo, Górowo Iławeckie, Nowe Miasto Lubawskie, Orneta, Pasym, Reszel). In the remaining cases, the spatial solutions do not have any historical features. After World War II their urban layout was changed and the architecture of socialism vividly stood out from the stylistics that had been created for centuries.

To summarize, one can state that towns that ate members of Cittaslow network develop according to the same set of criteria which is at the same time the benchmark of developmental standards. However, it is so profound that it allows for shaping the local specialization in an individual way. Every town has its own unique endogenous potential which under some conditions can contribute to real development and raising its inhabitants' quality of life [67,103], and the same time achieving the goals of functioning of Cittaslow movement.

#### **6. Conclusions**

Small historical towns offer a glimpse into Europe's wealthy cultural and architectural heritage. The original spatial structure of these towns has been preserved to a varied extent. Cittaslow International is a network of small towns which prioritizes the quality of life over the hustle and bustle of modern cities. The article analyzes and evaluates historical spatial forms in Cittaslow towns situated in selected European regions with a different culture and history. The relationships between the urban layout and the local quality of life based on sustainable development and protection of cultural heritage were examined.

The study revealed that the Medieval layout of Polish and Italian towns can be successfully incorporated in the modern urban fabric to promote the Cittaslow philosophy of sustainable development and slow living. Cittaslow towns are characterized by small size and population, small-scale urban development and user-friendly public spaces that promote a sense of local identity and foster responsibility for one's place of residence. Historical spatial layouts of the towns (downtown built-up areas) are not adjusted to the modern functional specificity of a big city and its dynamics. There is no possibility for industrial development, building large-areas shopping centers, organizing public transport or building thoroughfare. The small scale of the area in the town center makes social integration an inevitable element of its functioning.

Moreover, historical towns have distinctive spatial features, including small squares, narrow streets that are closed off to vehicular traffic, and historical architecture that triggers emotional responses. These components facilitate the implementation of the Cittaslow concept that advocates harmonious relations between the past, i.e., the preservation and promotion of local traditions and cultural heritage, and the future, i.e., economic growth and an improvement in living standards. Even though Polish and Italian towns have differences in their layouts and character, they still have a common feature—the cozy space organization which being combined with old but well-functioning architecture corresponds to Cittaslow assumptions.

The analyses demonstrate that the components can be applied for spaces developed in a various way, both Polish and Italian towns. Historical urban fabric favors pedestrian movement, which contributes to the formation of stronger social bonds, unlike in large metropolitan areas where social interactions are scarce. Dense development in small towns eliminates physical barriers to human contact, such as extensive open spaces, wide roads and large plots of land zoned for development. These findings indicate that in order to preserve the historical urban fabric, the structure should not be interfered with. Effective planning policies are required to preserve the scale, unique character and identity of historical towns, and they facilitate the achievement of Cittaslow goals. Historical spatial layout and urban structures promote local arts, crafts and services. Preservation of local traditions and cultural heritage contributes to the development of tourism, and modern technology (such as remote work arrangements) improves the quality of local life.

Historical urban fabric is an element of cultural heritage and one of the pillars of the tourism industry which creates ample opportunities for promoting the values of towns and regions. Therefore, the results of this study can be used by local authorities to establish cooperation networks, promote their towns and regions, and exchange experiences relating to the implementation of local development policies. The proposed research methods are largely universal and can be applied to analyze the spatial structure of small towns in other regions of the world. The study adds to the existing body of knowledge relating to the development of historical urban forms and spatial layout. The possibility for comparing Polish and Italian towns, which differ in many above-mentioned aspects, proves the point. The methodology is therefore universal and can contribute to the comprehension and further development of the Slow City idea in various regions in the world. Moreover, it can be used as a key element in building local urban revitalization programs for historical towns. Such practice is commonly applied in urban planning in numerous European regions. However, during conducting the research one should be aware of the limitations connected with the lack of a unified database for given countries and the limitations of the access to iconographic and cartographic resources.

**Author Contributions:** Conceptualization, M.Z., K.P. and A.S.; methodology, M.Z., K.P. and A.S.; formal analysis, M.Z.; investigation, M.Z. and K.P.; resources, M.Z.; data curation, M.Z. and K.P.; writing—original draft preparation, M.Z., K.P. and A.S.; writing—review and editing, K.P. and A.S.; visualization, M.Z.; supervision, A.S.; project administration, M.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: http://asict.arte.unipi.it/index.html/ (accessed on 18 August 2020), http://www.cittaslow. org/ (accessed on 30 August 2020), www.istat.it (accessed on 30 August 2020), www.cittaslow.it (accessed on 30 August 2020), www.google.com/maps (accessed on 30 August 2020), https://cittaslowpolska.pl/ (accessed on 30 August 2020).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Case Report* **Toward a Dualistic Growth? Population Increase and Land-Use Change in Rome, Italy**

**Leonardo Bianchini <sup>1</sup> , Gianluca Egidi <sup>1</sup> , Ahmed Alhuseen <sup>2</sup> , Adele Sateriano <sup>3</sup> , Sirio Cividino <sup>4</sup> , Matteo Clemente <sup>5</sup> and Vito Imbrenda 6,\***


6

**Abstract:** The spatial mismatch between population growth and settlement expansion is at the base of current models of urban growth. Empirical evidence is increasingly required to inform planning measures promoting urban containment in the context of a stable (or declining) population. In these regards, per-capita indicators of land-use change can be adopted with the aim at evaluating long-term sustainability of urbanization processes. The present study assesses spatial variations in per-capita indicators of land-use change in Rome, Central Italy, at five years (1949, 1974, 1999, 2008, and 2016) with the final objective of quantifying the mismatch between urban expansion and population growth. Originally specialized in agricultural productions, Rome's metropolitan area is a paradigmatic example of dispersed urban expansion in the Mediterranean basin. By considering multiple land-use dynamics, per-capita indicators of landscape change delineated three distinctive waves of growth corresponding with urbanization, suburbanization, and a more mixed stage with counter-urbanization and re-urbanization impulses. By reflecting different socioeconomic contexts on a local scale, urban fabric and forests were identified as the 'winner' classes, expanding homogeneously over time at the expense of cropland. Agricultural landscapes experienced a more heterogeneous trend with arable land and pastures declining systematically and more fragmented land classes (e.g., vineyards and olive groves) displaying stable (or slightly increasing) trends. The continuous reduction of per-capita surface area of cropland that's supports a reduced production base, which is now insufficient to satisfy the rising demand for fresh food at the metropolitan scale, indicates the unsustainability of the current development in Rome and more generally in the whole Mediterranean basin, a region specialized traditionally in (proximity) agricultural productions.

**Keywords:** metropolitan expansion; per-capita urban area; per-capita cropland; land mismatch; population; Italy

#### **1. Introduction**

Land use/land cover changes (LULCCs) are due to a large number of factors encompassing climate change, occurrence of natural risk, regulation/conservation policies, and human disturbance mainly consisting in various forms of urbanization [1–9]. In this perspective, in urban areas, settlement expansion and landscape transformations were intimately associated, involving multiple research dimensions encompassing ecology, planning, economic issues, and social aspects [10]. For a comprehensive study of

**Citation:** Bianchini, L.; Egidi, G.; Alhuseen, A.; Sateriano, A.; Cividino, S.; Clemente, M.; Imbrenda, V. Toward a Dualistic Growth? Population Increase and Land-Use Change in Rome, Italy. *Land* **2021**, *10*, 749. https://doi.org/10.3390/ land10070749

Academic Editors: Luca Salvati, Iwona Cie´sla and Andrzej Biłozor

Received: 30 June 2021 Accepted: 13 July 2021 Published: 17 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

urbanization-driven landscape changes, defining urban growth in terms of density, spatial morphology, socioeconomic implications, and environmental impacts is a difficult task [11–13]. Focusing on agglomeration, contemporary urbanization has been progressively shifted beyond the typical ideal city models in which the density of population, settlements, and economic activities display a significant decline moving away from downtown [14]. Urban sprawl was proposed as a comprehensive definition of this evolution: while sprawl comes across as a matter of degree not easily quantifiable, it is argued that in this case the density gradient of any urban function (population, buildings, and businesses) became less steep over time [15–17]. However, low-density development differs from place to place, thus defining of such a process is a mostly place-specific exercise [18]. Impacts of sprawl on fringe landscapes are also mixed and hardly predictable as land-use change in metropolitan regions is often the result of a complex interplay of socioeconomic factors and land constraints (e.g., accessibility, infrastructural development, land/house prices, and access to credit) [19–22].

The dynamic linkage between landscape transformations and population dynamics in advanced economies became increasingly multifaceted because of the mutual interplay of environmental, social, and planning dimensions that influence the level of sustainable development at different spatial scales from regions to local communities [23,24]. It was demonstrated how population dynamics have influenced sprawl less intensively than what occurred under more compact and dense stages of urban expansion in the past [25]. In fact, the current model of urban development (i.e., low-density settlements scattered on a natural or agricultural matrix) results in spatially articulated settlement morphologies leading to high rates of land consumption [26–28].

Following Couch et al. [2], urban sprawl has been increasingly treated as a process of urban change rather than a pattern of urbanization. In this sense, the definition given by Glaster and other scholars nearly twenty years ago [1] is the one that best allows sprawl to be intended as a process and not merely a spatial pattern. Within this approach, the simplest indicator for measuring the extent of sprawl in a metropolitan area is a ratio of two growth rates: the rate at which land development near the outer suburbs has increased divided by the rate at which the population of the metropolitan area has grown [29]. Measuring urban and suburban expansion in this context, a recent study defined exurban development as a process in which "the spread of development across the landscape far outpaces population growth" [30–32]. Similarly, another study notes that "if land is being consumed at a faster rate than population growth, then a metropolitan area can be characterized as 'sprawling'. If population is growing more rapidly than land is being consumed for urbanization, then a metropolitan area can be characterized as 'densifying'" [3]. Taking this approach, Wiewel and Persky's work on Chicago's suburban sprawl [13] demonstrated that between 1970–1990, urbanized land grew by 46%, while the population increased by only 4% (a ratio of 11.5 to 1). This is only an example of the possible use of per-capita indicators of land-use [33–38].

A large imbalance between a place's spatial expansion and its population change (where the former increases much more rapidly than the latter) is not unusual in advanced economies [39]. Growth of this sort produces a lower density outcome with people and their residential and commercial buildings using more space often at the expense of forests and farmland [40]. Examining landscapes from the other side of the fringe, exurban development has produced fragmented and chaotic natural landscapes that are progressively losing their characteristic traits (e.g., as far as the composition, configuration, and structure are concerned) [41]. While land-use change in metropolitan regions was the subject of earlier studies delineating knock-on effects on the environmental matrix [42–44], the relationship between land-use change and urban cycles has not yet been adequately studied, especially in regions where human pressure has increased more rapidly in recent years [45].

Based on earlier studies [46], a complete urban cycle is usually represented as a sequence of four development stages with different demographic characteristics from urbanization to suburbanization and from counter-urbanization to re-urbanization [47]. The downtown population increased in both the first and the fourth stage; the suburban population increased in the second stage; and the rural population in metropolitan regions increased in the third stage [48]. Per-capita land-use indicators seem to be an appropriate solution to better delineate the temporal outline of different stages of a complete urban cycle, in turn quantifying the incipient mismatch between population growth and urban expansion [49]. This evidence indicates, likely better than other indicators, the shift from compact models to a more dispersed settlements' structure [50]. By linking (urban) form and (ecological) functions in peri-urban areas, a long-term analysis of per-capita land-use indicators provides the necessary informative base to any policy aimed at containing urban expansion [51–54]. In this direction, spatial planning is urgently required to finely tune urban expansion with population increase and growth in economic activities, limiting settlement diffusion (e.g., in shrinking cities) [55].

The drastic spread of official statistics, land-use maps, and geo-spatial databases support a refined landscape analysis investigating per-capita land-use indicators over a sufficiently long-time interval [56]. Per-capita indicators of land-use change represent a suitable tool to test assumptions on the relationship between urban cycles and landscape transformations over different waves of metropolitan expansion [57]. In this direction, exploratory multivariate data analysis contributes to reveal the complex linkage between compositional changes in relict, fringe landscapes, and the increase of resident population, considered an anticipatory indicator of the specific stage of a given city life cycle [58,59].

Grounded on this theory, our study provides a long-term analysis of per-capita land cover changes at the fringe of a metropolitan region in southern Europe. In light of different models of urban expansion from compact and radio-centric urbanization to dispersed expansion of low-density settlements, the present study documents urban and peri-urban changes over nearly 70 years (1949–2016). As a novel contribution to land-use science, the present work delineates the prevalent mode of urban expansion in Rome (Italy) considering both socioeconomic functions (whose changes are reflected in the sequential stages of the city life cycle) and morphological shifts (e.g., compact vs. dispersed). The Rome metropolitan area is a typical semi-compact and dense city in southern Europe experiencing different waves of settlement expansion and represents an example of metropolitan transformations in the Mediterranean [60–62]. The empirical results of this study allow for the discussion of the (supposed) unsustainability of current urban expansion compared with past settlement structures as far as land fragmentation and loss of relict habitats and traditional crops at the fringe are concerned.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The investigated area covers a large part of Rome's province (Latium region, Central Italy) encompassing the municipalities of Rome and Fiumicino (1500 km<sup>2</sup> , see Figure 1). The study area is mostly flat and corresponds with the so called 'Agro Romano', a cultivated district with traditional rural landmarks, biodiversity, and cultural heritage surrounding the historical city of Rome along the alluvial plain of the Tiber river [60]. Climate regime in Rome is typically Mediterranean with rainfalls concentrated in the fall and spring and mild temperatures in the winter. Average annual rainfalls and mean daily temperatures were nearly 700 mm and 16 ◦C in the period between 1960–1990, although in the last decades the study area is becoming drier and warmer as a consequence of reduced precipitation and increased temperatures.

During the investigated period (1949–2016), this area has been subjected to considerable transformations due to a mix of drivers. Among them, Common Agricultural Policy, local market conditions, demographic trends, and conservation measures (e.g., Natura 2000 network) have stimulated on the one hand a strong intensification and simplification of the agricultural productions; on the other, an expansion of land abandonment phenomena, all contributing to an increased environmental fragility of these areas [63,64]. A pivotal role has been played by urban growth impacting agricultural lands and compromising

their distinctive socioecological features. The low-density discontinuous settlement that developed on the edges of Rome (Figure 2) penetrates into the Agro Romano, fragmenting its agro-forestry matrix. The negative effects of this expansion can be cumulative and can increasingly affect large areas as shown by the regional distribution of per-capita land consumption computed at the municipal scale. [63,64]. A pivotal role has been played by urban growth impacting agricultural lands and compromising their distinctive socioecological features. The low-density discontinuous settlement that developed on the edges of Rome (Figure 2) penetrates into the Agro Romano, fragmenting its agro-forestry matrix. The negative effects of this expansion can be cumulative and can increasingly affect large areas as shown by the regional distribu-

During the investigated period (1949–2016), this area has been subjected to considerable transformations due to a mix of drivers. Among them, Common Agricultural Policy, local market conditions, demographic trends, and conservation measures (e.g., Natura 2000 network) have stimulated on the one hand a strong intensification and simplification of the agricultural productions; on the other, an expansion of land abandonment phenomena, all contributing to an increased environmental fragility of these areas

*Land* **2021**, *10*, x FOR PEER REVIEW 4 of 14

In particular, despite the fact that Rome exhibits intermediate values for the extent of urban areas on the total municipal area and for per-capita built-up areas partially due to a huge municipal area, the neighboring municipalities exhibit a sprawling urbanization with variable settlement densities that are frequently higher than those observed in the central municipality. Settlements traditionally organized on radial axes around the consolidated city are characterized by variable sealing rates with very heterogeneous percentages in the regional area, particularly high around the county seats (not only in Rome but also in the other Latium provinces), representing sub-centers in expansion. Simultaneously, the original settlement structure in the rural districts of the Agro Romano has been transformed into a medium and low-rise fabric spatially decomposed as a result of urban de-concentration processes only partially devised with consequent spatial colonization of areas located at 20–40 km from the central city. tion of per-capita land consumption computed at the municipal scale. In particular, despite the fact that Rome exhibits intermediate values for the extent of urban areas on the total municipal area and for per-capita built-up areas partially due to a huge municipal area, the neighboring municipalities exhibit a sprawling urbanization with variable settlement densities that are frequently higher than those observed in the central municipality. Settlements traditionally organized on radial axes around the consolidated city are characterized by variable sealing rates with very heterogeneous percentages in the regional area, particularly high around the county seats (not only in Rome but also in the other Latium provinces), representing sub-centers in expansion. Simultaneously, the original settlement structure in the rural districts of the Agro Romano has been transformed into a medium and low-rise fabric spatially decomposed as a result of urban de-concentration processes only partially devised with consequent spa-

Urban settlements in Rome can be divided into 2 main districts: (1) the consolidated urban fabric hosting 63.4% of the population (as the percentage share in the total population) with a population density of 4873 inhabitants/km<sup>2</sup> and (2) the suburbs hosting the 36.6% of the population (as the percentage share in the total population) with a population density of 1037 inhabitants/km<sup>2</sup> (2011 census). tial colonization of areas located at 20–40 km from the central city. Urban settlements in Rome can be divided into 2 main districts: (1) the consolidated urban fabric hosting 63.4% of the population (as the percentage share in the total population) with a population density of 4873 inhabitants/km2 and (2) the suburbs hosting the 36.6% of the population (as the percentage share in the total population) with a population density of 1037 inhabitants/km<sup>2</sup> (2011 census).

**Figure 1.** A map indicating the position of the study area (Rome and Fiumicino municipality) in Central Italy (a) and two maps illustrating the spatial distribution of basic land-use classes (black: urban settlements; grey: woodlands; and white: agricultural areas) in Rome 1949 (b); 2016 (c). Urban settlements include impervious land and urban parks; agricultural areas include arable land, **Figure 1.** A map indicating the position of the study area (Rome and Fiumicino municipality) in Central Italy (**a**) and two maps illustrating the spatial distribution of basic land-use classes (black: urban settlements; grey: woodlands; and white: agricultural areas) in Rome 1949 (**b**); 2016 (**c**). Urban settlements include impervious land and urban parks; agricultural areas include arable land, crop mosaic, vineyards, and olive groves; and woodlands include forests, pastures, and water bodies.

bodies.

crop mosaic, vineyards, and olive groves; and woodlands include forests, pastures, and water

‐ ‐ ‐ **Figure 2.** Boundaries of the Latium region located at the heart of Italy. Within the circle the municipalities of Rome and Fiumicino are shown with their corresponding urban areas (1960–2009, sub-figure (**a**)) and 2016 artificial surface per-capita (sub-figure (**b**)). These figures are taken from the annual report (2017) that ISPRA (Italian National Institute for Environmental Protection and Research) draws up on soil consumption, territorial dynamics and ecosystem services [65].

#### *2.2. Land-Use Maps*

*‐* ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ Following earlier studies [26], a database reporting the surface area of several land-use classes was derived from elaboration of 3 compatible maps with a land nomenclature based on a simplified Corine Land Cover classification system [57–59,61,62]: (i) the Italian Istituto Geografico Militare topographic map (1:25,000 scale) produced in 1949, (ii) a land-use map (1:25,000) produced by the Cartographical Service of the Rome's province authority derived from field surveys and in-house photo-interpretation of digital ortho-photographs taken for cadastral purposes, and (iii) land-use maps (1:25,000) realized by the Cartographical Service of Latium regional authority from photo-interpretation of digital ortho-images released from the Italian National Geoportal. In addition, a first map was originally produced for 1999 and subsequently updated for 2008 and 2016 with new images referring to the same years. Eight homogeneous classes with a minimum mapping unit of 1 hectare have been considered: (i) arable land, (ii) crop mosaic, (iii) vineyards, (iv) olive groves, (v) woodlands, (vi) pastures, (vii) water bodies and wetlands, (viii) impervious land, and (ix) urban parks.

‐ ‐

#### *2.3. Land-Use Indicators*

*‐* ‐ ‐ ‐ In this work, we used per-capita indicators as suggested by the United Nations [66] for the achievement of the Sustainable Development Goals (SDGs). In particular, the 2030 Agenda strongly encourages the adoption of the indicator "relationship between land consumption and population growth". In addition, Italy has adopted this indicator in the annual report of soil consumption drawn up by ISPRA (Italian National Institute for Environmental Protection and Research, see Reference [65]). For each investigated year (1949, 1974, 1999, 2008, and 2016), a per-capita indicator of surface area by land-use class was constructed considering (i) the total extension (ha) of land devoted to a given class and (ii) the total population residing in the study area (municipalities of Rome and Fiumicino). Total population was derived from integration of population census data (conducted every 10 years in Italy on behalf of the National Statistical Institute, Istat) and results from the national population registry available for each country's municipality. Annual rates of change over time were also calculated separately for each time interval (1949–1974, 1974–1999, 1999–2008, and 2008–2016) and land-use class.

To summarize long-term land-use patterns and trends in the study area, a Principal Component Analysis (PCA) was run on a matrix composed of two indicators' sets: (i) the relative proportion of the 9 classes (see above) by year (i.e., class area in total landscape, labelled with 'c') and (ii) per-capita surface area (ha) of each land-use class by year (labelled with 'p'). PCA is a multivariate exploratory technique aimed at analyzing two-way data matrices, inspecting the latent relationship among variables organized by rows and columns together. In our case, PCA provides delineates a dynamic representation of landuse change in the study area considering year-by-year class transformations by evaluating both landscape composition (class area) and land-use intensity (per-capita class area). The input matrix was constituted of 9 land-use classes (columns) and 2 indicators (percent class share in the total landscape and per-capita surface area by class) quantified at five years each (rows) and occupying ten rows. Components were extracted on the base of the absolute eigenvalue. Components with eigenvalue > 1 were regarded as significant and analyzed further for loadings and scores' structure. A biplot was realized with the aim of illustrating the statistical distribution of component loadings (land-use classes) and scores (indicators and years), evidencing the apparent linkages between land-use composition and landscape structure. This plot provided a refined and summary outlook of similarities and differences in landscape transformations across the study area.

#### **3. Results**

The study area has experienced land-use changes over time (Figure 3). Urban growth with sequential acceleration and deceleration waves has been associated with a moderate increase in the forest area. Landscape transformations have negatively impacted the traditional agricultural matrix that has contracted significantly. Urban fabric, initially compact and concentrated around the central city, has gradually expanded into rural areas occupying more than 30% of the surface area administered by Rome's municipality in 2016 and developing a spatially heterogeneous shape. Based on the landscape structure in 1949, the agricultural matrix formed a natural interface between urban areas and forests. In 2016, it was extremely fragmented, no longer acting as a buffer zone between settlements and woods, and now placed in close contact in most cases. These changes have transformed Rome's morphology, initially dense and organized mono-centrically around the historical town. During the study period, urban development progressively lost its radio-centric and additive character. In 2016, Rome's morphology was found more dispersed and spatially discontinuous. Taken together, these preliminary results highlight a transition from a compact form to a more dispersed and fragmented morphology common with other Mediterranean cities.

The long-term transformation of Rome's landscape (1949–2016) coincided with intense population growth in the study area (Figure 3). However, combining urban expansion and population growth rates, different rates of change were observed in the four sub-periods of investigation. The first period (1949–1974) was characterized by a sustained rate of urban expansion (about 4% per year) associated with an equally high rate of population growth (about 3% per year) and their ratio is about 1.5. This period reflects compact, dense, and radio-centric urbanization leveraged by demographic growth. The second time interval (1974–1999) delineated a massive urban expansion (about 3% per year) that did not correspond to a similar demographic trend, though, as the population grew very weakly with a ratio reaching the tremendous value of about 188. This period coincides with a phase of suburbanization, the result of which was the suburban expansion of low-density and spatially dispersed settlements in the metropolitan area. The largest mismatch between urban expansion and population growth was observed at this stage. The two subsequent periods (1999–2008 and 2008–2016) were rather similar, characterized by moderate urban growth in the face of modest demographic dynamics. In relative terms,

the mismatch between the two growth rates remained high with ratio values of about 6 and 3.5, respectively.

‐

**Figure 3.** Percent annual rate of growth (population: blue; settlements: orange) by time interval in Rome. Urban expansion includes impervious land and parks/gardens within the city.

‐ ‐ ‐ ‐ ‐ ‐ Considering the availability of different land-uses per-capita as an indicator of longterm transformations in urban, agricultural, and forest landscapes (Table 1), a substantial reduction in per-capita arable land was observed over time. Impervious lands (urban settlements) exhibit opposite dynamics similar to what has been observed for urban parks and gardens, a class physically associated with settlements and therefore dependent on their dynamics over time.


‐ ‐ ‐ **Table 1.** Per-capita surface area (hectares) by land-use class in Rome, 1949–2016.

Following a significant decrease in the first time interval (1949–1974), forests have exhibited slight increases in more recent periods. Despite the significant contraction of agricultural areas, the cropland mosaic has undergone an important spatial reorganization. Some agricultural systems exhibited a decrease (vineyards and pastures), while other classes exhibited a slight growth (olive groves). In every case, trends over time have been non-linear, highlighting a substantial heterogeneity that corresponds to very different

dynamics over space according to the different phases of urban expansion as better demonstrated in the following analysis. The empirical analysis of per-capita land-use change in the four phases of metropolitan development outlined above highlights different dynamics in the first two time periods, corresponding with two sequential stages of the urban cycle in Europe (urbanization–suburbanization). By contrast, land-use change rates are fairly homogeneous in the two most recent periods (Table 2). These periods represent a rather heterogeneous phase of the urban cycle that combines elements of counter-urbanization (slow but constant land consumption at progressively greater distances from urban centers) and re-urbanization (slow recovery of the population even in central areas). More specifically, urbanization in Rome (1949–1974) coincided with a significant contraction of the per-capita surfaces of almost all agricultural and forest land-use (except olive groves, pastures, and crop mosaics) in favor of urban settlements. In the context of suburbanization (1974–1999), a slower (per-capita) contraction of all agricultural land-use was observed in favor of both urban settlements and forests. In the face of a continuous growth of urban settlements, the natural landscape responded in a very different way in the most recent phases. Arable lands continued to decline significantly. This extensive use of agricultural land characteristic of the study area has constituted a stock of buildable area over the entire study period considering the modest value added.

**Table 2.** Percent change over time (annual rate) in the per-capita surface area by land-use class in Rome, 1949–2016.


The agricultural mosaic together with vineyards and olive groves demonstrated evident signs of recovery. This result is in line with a generalized fragmentation of rural spaces at the fringe. Expansion of tree crops coincided with the consolidation of agricultural mosaics, a highly fragmented landscape that retains important elements of naturalness. The future challenge for fragmented landscapes will be to preserve this type of land-use from further isolation, fragmentation, and degradation resulting from urban growth. On the contrary, pastures have undergone a slow but steady decrease in line with what has been observed for arable lands. In the Mediterranean, fringe pastures have traditionally represented a reservoir of land potentially buildable or convertible to other (economically more profitable) uses. From this perspective, the expansion of pastures in 1949–1974 confirms the massive urban pressure typical of that period when pastures, often representing a human-driven evolution of arable lands, were frequently regarded as a 'temporary' land use pending 'speculative' conversion to more profitable uses. In the subsequent periods, pastures mostly acted as the largest stock of land to be converted, sometimes along with arable land.

Based on the previous results, a PCA was developed to summarize the main transformations in fringe landscapes and compare the latent trends in terms of the total area and area per-capita for each land-use class (Figure 4). The PCA extracted two axes cumulatively explaining more than 90% of the total variance. Component 1 (71.9%) clearly discriminates the total dynamics (negative scores) from the per-capita land-use indicators (positive scores), differentially depicting trends in the agricultural landscape. In fact, arable lands are considered in opposition to other agricultural land uses. This result highlights how

the greatest transformations of the agricultural landscape in the study area concerned the arable lands, which have undergone a strong downsizing in per-capita terms. In contrast, all other uses of agricultural land have experienced much more modest changes. Component 2 (18.7%) demonstrates a marked similarity between urban settlements and forest areas, the two land uses that exhibited increases (more or less strong) during the survey period and to the detriment of agricultural areas. Component 2 also discriminates between earlier periods (p49 and p74) and more recent times (p99, p08, and p16), suggesting a substantial diversification in the three phases of urban development previously outlined (urbanization, suburbanization, and a mixed stage between counter-urbanization and latent re-urbanization). ‐ ‐ ‐ ‐ ‐

‐ ‐ ‐

‐ ‐ ‐

‐ ‐ ‐ **Figure 4.** Results of a Principal Component Analysis run on the matrix including percent rates of change in land-use and per-capita land-use in Rome, 1949–2016.

#### **4. Discussion**

‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ The analysis of landscape transformations conducted through the use of per-capita surface indicators by land use class provided an integrated view of environmental and socioeconomic nature. As far as built settlements are concerned, per-capita land-use indicators quantified the mismatch between urban expansion and population growth, outlining distinctive phases of the metropolitan cycle [67]. In the study area, this indicator has assumed profoundly different values in correspondence with (i) compact urbanization and (ii) low-density suburbanization, assuming intermediate values in the most recent period characterized by the coexistence of re-urbanization and counter-urbanization impulses. With regard to non-urban land-use, per-capita indicators provide an even more significant view; in the case of agricultural landscapes, the negative evolution of per-capita cropland indicated the impossibility to satisfy the growing (local) demand for fresh food. These contexts, however, were known for centuries for their specialization in primary production, which experienced a significant decline in the most recent decades [68]. Therefore, a recent analysis [69] argues that there is a need to focus more on the form and quality of urbanization processes rather than simply on the volume and speed of urbanization. Thus, the measure of the amounts of land in urban use and their spatial configuration remains crucial to provide useful information on anticipating future needs and to ensure an

adequate supply of public goods such as infrastructure, open spaces, and common facilities for urban expansion [70].

The multivariate analysis clearly indicates the long-term competition between landuse, highlighting the relationship between 'predators' (urban settlements and more recently forests) and 'prey' (arable land and more recently pastures). This interpretation, in line with earlier studies developed in the Mediterranean basin, highlights the substantial dichotomy between 'predators' with a competitive advantage (urban settlements) and more marginal actors (forests) that opportunistically occupy landscape niches left free from abandonment of agricultural land as a result of real estate speculation [70]. This dynamic is visible in marginal areas, while being occasionally observed at the fringe, for example in contexts where physical (e.g., steepness and accessibility) and/or regulatory (planning/zoning) constraints to building are more pronounced [71–73]. Simultaneously, our analysis establishes how agricultural landscapes do not experience homogeneous dynamics [74–76]. In the face of the originally dominant arable lands that systematically acted as a 'prey' between 1949–2016, other land-uses have consolidated their spatial distribution with a significant increase in per-capita surface area. Among these, per-capita increase in olive groves, a 'synanthropic' crop in the Mediterranean basin, appears very significant, having occupied peri-urban voids generated by a fragmented growth of low-density settlements [77,78]. The rapid suburbanization process during the 1960s, 1970s, and the following period became a dominant form of urban growth and mainly contributed to the spatial dispersion of urban centers towards the urban fringes. However, remarkably, the dispersal of the employment was slower than that of the city during this period, therefore the commuters' distances have increased. During 1980s and 1990s, settlement accesses have grown along road infrastructures, encouraging the emergence of new commercial, industrial, and leisure areas. The expansion of urban towards peripheries preying on more arable lands was fueled by the increase of car ownership, the expansion of transport infrastructure, and the emergence of new commercial and industrial areas that have served as new suburban job hubs [79]. The local context was at the base of landscape transformations, having land speculation, second homes, tourism development, and migration as characteristic roles in landscape changes at the fringe [80]. In these regards, socioeconomic factors have demonstrated to influence metropolitan growth in Rome. Assuming the relevance of a comparative analysis of land-use change over a sufficiently long term, a joint monitoring of landscape transformations and population supports sustainable development policies in metropolitan regions [79]. In comparison with northern, central, and western counterparts, southern European metropolises have experienced landscape fragmentation in a context of increasing ecological fragility. Urban sprawl has demonstrated to influence fringe landscapes with changes in the intimate structure of both agricultural and forest mosaics [81]. The intrinsic relationship between landscape transformations and different waves of urban expansion was also illustrated, distinguishing at least three developmental waves, namely urbanization, suburbanization, and a more mixed phase with impulses of counter-urbanization and re-urbanization. Per-capita indicators of land-use change clearly documented the complete unsustainability of recent urban expansion. The intrinsic mismatch between population growth and urban expansion enlarged in recent times, particularly evident during both suburbanization and the most recent developmental wave.

#### **5. Conclusions**

Comparative analysis delineating latent patterns and trends in land-use has recently benefited from indicators that assess the intrinsic mismatch between landscape transformations and population growth. Rising availability of high-resolution datasets supports such investigation, allowing for the construction of new indicators and refining empirical approaches to land-use science. An extensive use of per-capita land-use indicators will contribute to monitor the effectiveness of adopted policy measures and forecast possible future land-use changes in a spatial planning perspective. When evaluating long-term transformations of fringe landscapes, the empirical results of our study indicate the in-

creasing mismatch between settlement expansion and population growth that can be easily captured through the ratio between the two growth rates. This ratio goes from the value of just 1.5 for the period of 1949–1974 to the enormous peak of 188 in the time frame of 1974–1999, and then reaches the values of about 6 and 3.5 in the subsequent periods (1999– 2008 and 2008–2016, respectively). In combination, this developmental mode reveals all its traits of unsustainability, leading to landscape fragmentation and abnormal consumption of soil resources. Spatial planning (and primarily town master plans) should envisage practical action to contain the unavoidable effects of recent urban growth on the capacity of ecosystems to maintain a complete provision of services and goods in metropolitan region.

**Author Contributions:** Supervision, M.C.; project administration and funding acquisition, M.C.; conceptualization and writing—original draft preparation, A.S. and A.A.; writing—review and editing, S.C. and L.B.; software and data curation, S.C.; conceptualization, methodology, visualization, and supervision, G.E. and L.B.; writing—review and editing, V.I. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## **Forest Transition and Metropolitan Transformations in Developed Countries: Interpreting Apparent and Latent Dynamics with Local Regression Models**

**Leonardo Bianchini <sup>1</sup> , Rosanna Salvia <sup>2</sup> , Giovanni Quaranta <sup>2</sup> , Gianluca Egidi <sup>1</sup> , Luca Salvati 3,\* and Alvaro Marucci <sup>1</sup>**


**Abstract:** Metropolitan fringes in Southern Europe preserve, under different territorial contexts, natural habitats, relict woodlands, and mixed agro-forest systems acting as a sink of biodiversity and ecosystem services in ecologically vulnerable landscapes. Clarifying territorial and socioeconomic processes that underlie land-use change in metropolitan regions is relevant for forest conservation policies. At the same time, long-term dynamics of fringe forests in the northern Mediterranean basin have been demonstrated to be rather mixed, with deforestation up to the 1950s and a subsequent recovery more evident in recent decades. The present study makes use of Forest Transition Theory (FTT) to examine spatial processes of forest loss and expansion in metropolitan Rome, Central Italy, through local regressions elaborating two diachronic land-use maps that span more than 80 years (1936–2018) representative of different socioeconomic and ecological conditions. Our study evaluates the turnaround from net forest area loss to net forest area gain, considering together the predictions of the FTT and those of the City Life Cycle (CLC) theory that provides a classical description of the functioning of metropolitan cycles. The empirical findings of our study document a moderate increase in forest cover depending on the forestation of previously abandoned cropland as a consequence of tighter levels of land protection. Natural and human-driven expansion of small and isolated forest nuclei along fringe land was demonstrated to fuel a polycentric expansion of woodlands. The results of a Geographically Weighted Regression (GWR) reveal the importance of metropolitan growth in long-term forest expansion. Forest–urban dynamics reflect together settlement sprawl and increased forest disturbance. The contemporary expansion of fringe residential settlements and peri-urban forests into relict agricultural landscapes claims for a renewed land management that may reconnect town planning, reducing the intrinsic risks associated with fringe woodlands (e.g., wildfires) with environmental policies preserving the ecological functionality of diversified agro-forest systems.

**Keywords:** land-use change; metropolitan gradient; spatial econometrics; agricultural mechanization; Mediterranean

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

Providing a global interpretation of socio-environmental changes across a development gradient, Forest Transition Theory (FTT) predicts the inherent shift from net forest area loss, typical of emerging economies, to net forest area expansion [1]. This shift is characteristic of advanced economies and is coupled with a generalized recovery of mixed agricultural–wildland ecosystems [2–4]. This process has occurred in recent times via natural regeneration [5], active planting [6–8], or a combination of the two [9]. From this perspective, the notion of 'forest transition' is associated intimately with underlying socioeconomic forces [10], such as urbanization, late industrialization, tourism growth,

**Citation:** Bianchini, L.; Salvia, R.; Quaranta, G.; Egidi, G.; Salvati, L.; Marucci, A. Forest Transition and Metropolitan Transformations in Developed Countries: Interpreting Apparent and Latent Dynamics with Local Regression Models. *Land* **2022**, *11*, 12. https://doi.org/10.3390/ land11010012

Academic Editor: Alexandru-Ionu¸t Petri¸sor

Received: 30 November 2021 Accepted: 17 December 2021 Published: 22 December 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

and infrastructural development [11]. The different processes leading to forest transitions clearly depend on the local (territorial) context [12]. Although some generic processes can be identified for affluent countries (Figure 1), regions and districts do not necessarily experience a regular pattern of forest cover change over time, and the causes and effects of forest transitions may vary largely over space [12–14]. For instance, urbanization can determine a forest decline from direct clearcutting or wildfires [15], basically answering to increased housing demand (e.g., because of population growth) or real estate speculation, especially in contexts with less rigid planning rules or adopting new liberal urbanism schemes [16].

**Figure 1.** A framework illustrating the Forest Transition Theory (FTT) in advanced economies (Source: authors' elaboration from Rudel et al., 2005).

The predictions of the FTT have been tested empirically at various spatial scales [17–21]. Although the main forces of deforestation act locally, forest transitions in developed countries are far less investigated at that scale, especially in metropolitan regions [22–24]. As a result of urbanization, these regions represent a challenging opportunity to investigate the response of agro-forest landscapes to ecological disturbance and socioeconomic shocks [15,25–27]. Continuous urbanization has reflected the long-term interactions between nature and humans characteristic of the Mediterranean region [7,15,20,25–29]. Following urbanization, the abandonment of cropland, clearcutting, but also wood recolonization in some cases, may result in sequential stages of forest decline and recovery, depending on the local context [30–32]. Wildland–Urban Interfaces (WUIs) in Southern Europe frequently mix low-density settlements with natural habitats [17]. As a result, fringe landscapes feature irregular boundaries with discontinuous, low-density settlements that reflect suburbanization (i.e., population relocation to suburbs) and the delocalization of economic activities in peripheral areas [33]. These fragmented and mixed landscapes derive from a latent (and quite recent) process of peri-urban forest expansion into cropland, shrubland, and pastures [34], which has followed a significant decline in fringe forest cover following compact urban expansion [35]. As a matter of fact, economic development was demonstrated to fuel, in Italy as in Spain, and in Portugal as in Greece, compact urban growth in the 1950s and the 1960s and the uneven expansion of residential, low-density settlements in the 1970s and the 1980s [36–39]. From this perspective, FTT may appropriately predict cyclical trends in forest decline and expansion in complex peri-urban landscapes including relict agro-forest systems [40]. At the same time, predictions of the FTT were compared with those delineated by the City Life Cycle (CLC) theory. This theory provided a classical description of metropolitan cycles in advanced economies [41], explaining—at least indirectly—the socioeconomic dynamics at the base of peri-urban forest decline and

expansion [38]. CLC indicates different, sequential stages of urban growth forming a cycle of urbanization, suburbanization, counter-urbanization, and re-urbanization [33]. The first two stages, urbanization and suburbanization, have been particularly well studied in Mediterranean Europe [16], and they respectively reflect a compact-dense/mono-centric growth and a low-density, spatially heterogeneous expansion of residential settlements [42].

For the first time, to the best of our knowledge, the present study mixes these two dynamic theories (CLC and FTT) with the aims of (i) improving forest assessment in Mediterranean fringe districts and (ii) informing dedicated planning strategies for the sustainable management of WUIs [2,18,43,44]. Mediterranean forests are widely recognized as an invaluable sink of biodiversity [45], forming green infrastructures that may (indirectly) contain urbanization and the negative externalities of economic activity [34]. However, earlier studies have demonstrated that built-up areas around forests expanded in parallel with a decrease in cropland. In other words, residential settlements and forests act as competing land uses with negative impacts on agricultural systems [5].

Going beyond a model of forest expansion from small nuclei of pristine, high-quality woodlands (concentrated in peripheral districts), recent land-use dynamics reflect a more complex path with the growth of scattered and fragmented forest nuclei embedded in a consolidated network of relict peri-urban and rural woodlands [31,46,47]. The shift from a traditional model of forestation irradiating from a few peripheral locations (hereafter defined as 'mono-centric') to a 'polycentric' structure of forest growth (irradiating from multiple locations even close to cities), may be seen at the base of FTT in Mediterranean contexts [48]. This pattern may inform a refined interpretation of forest transitions in metropolitan regions [39]. This 'polycentric' network of forests is also at the base of green infrastructures, assuring the efficient protection of natural habitats in fringe districts [48].

The present study introduces a refined analysis of long-term changes in forest cover as a result of urban expansion in a large metropolitan area (Rome, Central Italy) representative of compact, mono-centric cities in Southern Europe [33]. Reflecting socio-ecological dynamics that impact ecologically fragile Mediterranean environments, land-use complexity warrants further investigation of forest trends (1936–2018) in light of the FTT and the parallel urban cycle. A particularly long time interval was investigated in this study covering a productive cycle of forests and a period that encompassed various stages of metropolitan growth including urbanization and suburbanization [35]. Peri-urban landscapes surrounding Rome (Central Italy) have been identified as the most dynamic across the country because of city size and human pressure [34], resulting in sequential waves of forest decline and recovery [47]. While 55 Natura 2000 sites involving 22% of the study area have been recently established in Rome [48], natural land converted to settlements grew by 0.4% per year during the last half-century, endangering fragile coastal ecosystems more than mountain systems [49].

To check the validity of the polycentric model applied to forest expansion, local econometric estimations based on a Geographically Weighted Regression (GWR) contributed to ascertain the relationship between settlement expansion and changes over time in the composition and morphology of fringe forest landscapes [45]. Being deliberately simplified, our approach tests the shift from a mono-centric model of forest distribution over space toward a more polycentric structure, as hypothesized by Colantoni et al. (2015) [35]. The empirical verification of this hypothesis benefits from a spatially explicit analysis of a dependent variable (percent share of forest cover in total landscape area) and of a predictor, the distance from a central location, which is assumed to represent a metropolitan gradient from central to peripheral locations typical of mono-centric regions [41]. To account for the residual variability that the predictor is unable to explain [16], space was explicitly considered in the econometric specification. The empirical results of this study may inform policy and planning strategies preserving relict agro-forest systems in ecologically sensitive contexts typical of Mediterranean metropolises.

#### **2. Methodology**

#### *2.1. Study Area*

The investigated area is the metropolitan region of Rome (5355 km<sup>2</sup> ), which is an administrative partition of the Latium region (Central Italy) encompassing the previously established province of Rome (NUTS-3 level of the European Nomenclature of Territorial Statistics). The area includes 15% flat land (<100 m at the sea level) and 20% mountainous land (>600 m at the sea level). The Simbruini mountains (highest elevation: 1820 m above sea level), which belong to the Apennine district, are the most important relief in the area (Figure 2, left); the alluvial flat area of the Tiber river is made up of lowlands (the so-called "Agro Romano" district; Figure 2, right). The climate is typically Mediterranean, with rainfalls concentrated in autumn and spring, and mild winter temperatures [35]. The average annual rainfall and mean daily temperature in Rome (1971–2000) were nearly 700 mm and 17 ◦C, respectively [47].

**Figure 2. Left**: elevation of the study area (meters on the sea level) overlapped with municipalities and urban districts; the insert map represents the position of Rome in Italy; **right**: a zenith photograph of inner Rome with peri-urban forests (green areas) concentrating on the left side of the landscape scene, courtesy of Google Earth.

The total population almost doubled during the study period from 2 million to 4 million inhabitants [5]. The percent share of the population living in inner Rome in the total population of the study area increased from 20% to 40% between 1936 and 2020 [39]. Even though urban settlements are becoming prevalent in the study area, the majority of the province's land is still comprised of forests, pastures, and cultivated fields [45]. Summer wildfires and human pressure, because of Rome expansion, have degraded the pristine forests, but relict woodlands have been sometimes preserved in both flat and hilly areas [48]. Based on official statistics [49], forest composition was changed significantly in the study area. Chestnuts and conifers increased considerably between the mid-1930s and late 2010s (respectively from 13.8% to 18.5% and from 1.7% to 3.0% of total forest stock); beech experienced a moderate decline (from 10.1% to 9.8% of total forest stock). Cropland abandonment around the central city was the base of natural afforestation uplands [47]; urbanization determined, at the same time, a progressive fragmentation of relict forests around the city, especially along the coastal rim [45]. Industrial areas were traditionally located in the eastern part of the 'Agro Romano' district, while traditional rural areas were (and still are) relatively abundant in the Western side of the study area [49].

Land-use maps were reproduced in Figure 3 to illustrate the mono-centric organization of Rome's metropolitan region, with compact settlements located downtown and a moderate spread of low-density settlements around central locations [34]. The shift toward dispersed urbanization in 2018 was not altering the polarization in central places (downtown Rome) and peripheral districts West and East of Rome [16]. Overall, the study area is administered by 122 municipalities, including the municipality of Rome that encompasses

the historical city ('within the Aurelian Walls') and the surrounding area (1285 km<sup>2</sup> ) within and outside the main Ring Road ('Grande Raccordo Anulare', GRA). In this study, the central municipality of Rome was further partitioned in 115 local districts (a sort of formal neighborhood known as 'suddivisioni toponomastiche' in Italian, and it is mainly used for statistical reporting at a sub-municipal scale). Following earlier studies [33], the present study made use of a specific (territorial) partition of the area into 236 elementary spatial units that derive from the union (i.e., spatial merging) of 121 municipalities (excluding Rome) and 115 local districts in Rome.

**Figure 3.** The spatial distribution of the compact and dispersed urban fabric in the study area in the early 1960s (**left**) and late 2010s (**right**), based on the elaboration on a local land cover map for 1960 and Corine Land Cover database for 2018.

#### *2.2. Forest Maps*

Two forest maps spanning the time frame between 1936 and 2018 and presenting a comparable spatial resolution and forest definition were used here. The former source is the Forest Map realized at 1:100,000 scale by 'Milizia Forestale', the Italian Forest Service during the Fascist period, as part of a forest inventory dated 1936 and recently disseminated in digital format (shapefile). The latter source is a Corine Land Cover (CLC) digital map dated 2018 and adopting a legend of 44 land-use classes at a 1:100,000 scale. Polygons classified as CLC 3.1 type ('forests') were extracted and elaborated (Figure 4). The information accuracy of the two maps and the reliability of the forest cover measured for both 1936 and 2018 were internally checked considering additional data that provided estimates of forest area in Rome: (i) the long-term annual forest survey carried out by Italian National Institute of Statistics (Istat), (ii) the two national forest inventories run in 1985 and 2003, (iii) a 25,000 topographic map produced by Italian Military Geographic Institute (Florence) and referring to 1949, (iv) a land map produced by National Research Council (CNR) at 1:200,000 scale and referring to the early 1960s [50], (v) four CLC maps dated 1990, 2000, 2006, and 2012, and (vi) the 1:25,000 land-use maps of Latium produced for 1999 and 2016 by the Cartographic Service of the Regional Authority of Latium region through the interpretation of digital ortho-photographs.

**Figure 4.** The spatial distribution of forest cover in the study area in 1936 (**left**) and in 2018 (**right**), based on elaboration on the forest maps described above.

#### *2.3. Logical Framework*

To test the inherent shift toward a 'polycentric' model of forest expansion (Colantoni et al. 2015), the percent of forest land area in the total landscape (i.e., municipal/district area) was derived, for each elementary analysis' unit, from the spatial overlay of a shapefile map illustrating the boundaries of the selected domains (n = 236 municipalities/local districts, see above) separately with each forest map (1936 and 2018). The average distance of each spatial unit (polygon) from downtown Rome was computed as the linear distance of the polygon centroid from a central place in the historical city—considered the heart of political, social, economic, touristic, and cultural life [16]. Adopting administrative boundaries as the elementary unit of investigation gave room to reliable comparisons with official statistics and constitutes a relevant domain for planning purposes [34]. In this study, we assumed Rome's metropolitan region as a laboratory of forest transitions [47], e.g., hypothesizing a classical ('mono-centric') model of forest decline around settlements and forest expansion in peripheral districts, and testing a possible shift toward a more 'polycentric' model of forest expansion around suburban settlements [48]. To verify such assumptions, changes over time in the spatial distribution of forest land along the urban gradient were analyzed comparing the results of spatially implicit and spatially explicit regressions that test the predictions of the FTT at a local scale [5]. More specifically, global and local regressions assessed the empirical relationship between percent forest area (dependent variable) and the distance from downtown Rome (predictor) at two times (1936 and 2018). We assumed a linear and spatially homogeneous relationship between the two variables in a 'mono-centric' model of forest expansion (i.e., a low percentage of forest area close to the inner city that increases with the distance from the city itself). With 'poly-centric' forest expansion [35], the relationship between the two variables is expected to be less polarized along the metropolitan gradient, with the predictor's impact varying largely across space [33]. The highest impact can be reflective of a local context (directly or indirectly) supporting forest expansion [5].

#### *2.4. Statistical Analysis*

Following the logical framework exposed above, both spatially implicit and explicit models, estimated separately for two time points (1936 and 2018) have been adopted to interpret long-term forest expansion/decline and the underlying territorial dynamics along the metropolitan gradient in Rome. A preliminary check of the linear or non-linear relationship between forest cover and distance from downtown Rome was performed using parametric (Pearson) and non-parametric (Spearman) pair-wise correlation coefficients, testing for significance at *p* < 0.05. A detailed description of the regression techniques

subsequently adopted to test the polycentric expansion of forests has been provided in the following two paragraphs, distinguishing global from local approaches.

#### 2.4.1. Global Approaches

Spatially implicit global approaches adopted in this study included Ordinary Least Square (OLS) regressions using linear and non-linear specifications, namely first (linear), second-order (squared), and third-order (cubic) forms. More specifically, the mono-centric structure was first tested adopting a linear specification [33] estimated through logarithmic transformations of both dependent and predictor variables as follows:

$$\mathbf{F} = \mathbf{a} + \mathbf{b}(\mathbf{D}) + \mathbf{e} \tag{1}$$

where F is the percent share of forest cover in total landscape, D is the distance of each spatial unit from downtown Rome (km, log-transformed), a and b are the regression coefficients, and **e** is the regression error. In this case, forest cover increases (or decreases) linearly along the metropolitan gradient, satisfying the assumption of the mono-centric model [33]. To verify the possible impact of the varying size (i.e., surface area) of each spatial partition adopted as the elementary analysis' unit in this study (i.e., municipalities/urban districts), we run OLS linear estimations of Equation (1) both un-weighted and weighted with the surface area of each elementary unit (km<sup>2</sup> ). District area was the result of the application of an ArcGIS 'calculate area' tool on the shapefile depicting municipalities/urban districts provided by the National Institute of Statistics (ISTAT) and statistical office of Rome municipality (Figure 2, left).

A squared specification was also tested with the aim at defining a quadratic relationship of the metropolitan gradient on forest cover:

$$\mathbf{F} = \mathbf{a} + \mathbf{b}(\mathbf{D}) + \mathbf{c}(\mathbf{D}^2) + \mathbf{e} \tag{2}$$

where F is the percent share of forest cover in the total landscape, D is the distance of each spatial unit from downtown Rome (km, log-transformed), a, b, and c are the regression coefficients, and **e** is the regression error. More complex patterns based on a cubic specification were tested to highlight place-specific responses of forest cover as the distance from downtown Rome increases [48], as follows:

$$\mathbf{F} = \mathbf{a} + \mathbf{b}(\mathbf{D}) + \mathbf{c}(\mathbf{D}^2) + \mathbf{d}(\mathbf{D}^3) + \mathbf{e} \tag{3}$$

where F is the percent share of forest cover in the total landscape, D is the distance of each spatial unit from downtown Rome (km, log-transformed), a, b, c, and d are the regression coefficients, and **e** is the regression error. Significant specifications were assessed using standard diagnostics, namely the adjusted R<sup>2</sup> .

#### 2.4.2. Local Approaches

We adopted a spatially explicit approach referring to the Geographically Weighted Regression (GWR) introduced by Fotheringham et al. (2002) to interpret complex spatial patterns of environmental and socioeconomic processes along a given geographical gradient. A refined understanding of the spatial variability related to the mono-centric expansion of metropolitan regions contributes to identify significant, local deviations from global patterns [48], informing spatial policies better adapted to specific territorial conditions. The use of administrative districts, such as municipalities, taken as policy-relevant spatial domains, allows a comprehensive investigation of context-based interactions that are frequently demised in 'centralized' (spatially implicit) interpretative models [51]. Based on these premises, GWRs estimate local regression models for each spatial domain in a given area, accounting for spatial dependence and heterogeneity [38], i.e., controlling for spatial structures that characterize the relationship between the dependent variable and the

predictor (Ali et al., 2007). We adopted a standard GWR specification for a given location s = 1 to n, as follows:

$$\mathbf{F(s) = B(s)D(s) + e(s)}\tag{4}$$

where F(s) and D(s) are, respectively, the dependent variable (percent forest cover) and the predictor (distance from downtown Rome) illustrated in Equation (1) above, which were both measured at each location s; B(s) means the column vector of the regression coefficients at location s, and **e**(s) is the random error at location s. Regression parameters were estimated separately for 1936 and 2018 at each location by weighted least squares and are thus spatially explicit, i.e., a function of s. Spatial coordinates of each elementary unit considered in this study were determined based on a shapefile map of administrative boundaries (see Figure 2, left) managed in ArcGIS (ESRI Inc., Redwoods, CA, USA) environment. A Kernel spline function was adopted to calculate weights for the estimation of local regressions [38]. Limitations regarding the use of GWR only arise when drawing conclusions based on a reduced number of sample observations, which is not the case of this study. GWRs were run separately for each time interval. For each regression, the model's results included (i) a global measure of goodness-of-fit (adjusted R<sup>2</sup> ) that was compared with the same index obtained from the respective OLS regression (both weighted and un-weighted for the surface area of each spatial partition, see above) and (ii) a spatial distribution of local coefficients (R<sup>2</sup> , slope coefficient) illustrated through maps (Salvati et al. 2016). The GWR results clarify the role of spatial heterogeneity as a novel contribution to the empirical test of the mono-centric model [33].

The intrinsic shift from a classical 'mono-centric' model of forest expansion toward a more 'poly-centric' structure was reflected in a different spatial distribution of both slope and R<sup>2</sup> regression coefficients [16]. In fact, we expect (i) a more fragmented/heterogeneous distribution of R<sup>2</sup> coefficients (possibly indicating the increase in sparse nuclei of forest expansion) and (ii) an expansion of the spatial domains with high slope coefficients, reflecting a latent, but generalized, increase in forest cover [35]. With this perspective in mind, comparing the intensity and direction of the relationship between percent forest cover and the distance from downtown Rome for different econometric models and specifications (e.g., global vs. local, spatially implicit vs. spatially explicit) provides an intrinsic measure of the role of territorial factors [48]. Such contextual aspects should be more explicitly considered in any environmental policy targeting the mitigation of (and adaptation to) urbanization processes [49].

#### **3. Results**

Forest cover in Rome increased from 18.3% in 1936 to 19.9% in 2018. The spatial distribution of forest area (Figure 5) reflects the intense landscape transformations between 1936 and 2018, with forest expansion in relict croplands East of Rome and moderate decline in flat districts surrounding the 'Agro Romano' and along the sea coast. These districts included, up to now, the main remnants of the forest cover extending along the Tyrrhenian Sea coast from Tuscany to Campania in ancient times. Accelerated rates of forest recovery have been observed in rural, upland districts west and north of Rome, which were originally devoted to shrublands and olive groves (e.g., 'Sabina' district). Forest cover was relatively stable in the 'Castelli Romani' district, a peri-urban, upland area with high human pressure (population density around 500 inhabitants/km<sup>2</sup> ) and mostly devoted to chestnut production.

#### *3.1. Global Approaches*

All statistical models testing changes in the spatial relationship between forest cover and the distance from downtown Rome (Table 1) indicated how the correlation between the two variables intensified over time. Non-parametric Spearman rank coefficients delineate a positive and strong correlation between forest cover and distance from a central place in Rome (r<sup>s</sup> = 0.76 and 0.86, respectively for 1936 and 2018). However, while the first-order (linear) Ordinary Least Square (OLS) model performed rather low for 1936 and 2018, secondand third-order (i.e., squared and cubic) OLS regressions performed better for both years (adjusted R<sup>2</sup> = 0.58 for 2018). With global R<sup>2</sup> coefficients increasing from 0.65 in 1936 to 0.75 in 2018, local regressions outperformed OLS (linear and non-linear) models, documenting the importance of the spatial dimension in long-term forest transitions characteristic of the study area. Moran's spatial autocorrelation indexes demonstrate that the percent share of forest cover in the total municipal/district area was not randomly distributed over space (1936: I(*z*) = 4.29, *p* < 0.05; 2018: I(*z*) = 3.69, *p* < 0.05). Spatial autocorrelation justifies the use of a local regression approach when assessing the relationship between percent share of forest cover in the total municipal/district area as the dependent variable and the distance from downtown Rome as an intrinsic test of the mono-centric (or poly-centric) forest structure.

**Figure 5.** The spatial distribution of percent share of forest cover in the total municipal (or district) area in Rome (**left**: 1936; **right**: 2018).

**Table 1.** Correlation coefficients and best-fit R<sup>2</sup> of global econometric models (Ordinary Least Squares (OLS) with linear and non-linear specifications; Geographically Weighted Regression (GWR) estimating the percentage of forest area in each spatial domain (dependent variable) from the distance to downtown Rome (predictor) by year.


All models are significant at *p* < 0.05. † weighted by surface area of each spatial domain (i.e., municipalities/urban districts).

#### *3.2. Local Approaches*

The satisfactory result of local regressions suggests how territorial contexts distinctive of municipalities and urban districts in the study area shape forest dynamics at different distances from downtown Rome. Local regressions modeled the spatial dimension of both 1936 and 2018 data by incorporating a location distance matrix that allowed the local estimation of both slope (measuring the predictor's impact) and R<sup>2</sup> (measuring the model's goodness-of-fit) regression coefficients for each spatial domain. Vector maps were used to illustrate the spatial distribution of local regression slope and local R<sup>2</sup> coefficients. The results of local regression models for forest cover as the dependent variable and the distance from downtown Rome as the predictor were illustrated in maps considering separately the spatial distribution of regression slopes and local R<sup>2</sup> goodness-of-fit coefficients (Figure 6). Slope coefficients depicted the (more or less intense) contribution of specific areas to a mono-centric spatial structure.

**Figure 6.** Results of a Geographically Weighted Regression assessing the relationship between the percent share of forest cover in total landscape and the distance from downtown Rome by year (**left**: 1936; **right**: 2018; **upper** panel: local slope (regression) coefficients; **lower** panel: local R<sup>2</sup> goodness-of-fit coefficients).

The empirical results of local regressions indicate a distinctive spatial distribution of forest cover for 1936 and 2018, in turn reflecting a substantially different relationship with the distance from a central place in Rome. Comparing the models' goodness-of-fit, the regression for the last observation year (2018) was performing slightly better than the model for the first observation year (1936). Spatial units (municipalities/districts) with local R <sup>2</sup> > 0.5 (regarded as a satisfactory model's fit at that spatial scale) increased during the study period. The spatial distribution of local R<sup>2</sup> coefficients in 1936 delineated particularly high values in economically peripheral municipalities of the Apennine mountain districts ('Lucretili' and 'Lepini' districts), as well as in rural, remote municipalities in the northwest of Rome ('Tolfa-Bracciano' district). In 2018, the highest R<sup>2</sup> coefficients were found either in upland/mountainous municipalities East of Rome ('Lucretili', 'Simbruini' and 'Lepini' districts); the same pattern was observed in sparse coastal/lowland municipalities featuring

human disturbance and low ecological quality ('Anzio-Nettuno' coastal district South of Rome; 'Bracciano Lake' district). In both cases, spatial units with high R<sup>2</sup> were found closer to Rome in 2018 than in 1936. As far as the impact of the distance from downtown, the highest regression slopes for 1936 were recorded in remote districts East of Rome and in more sparse flat areas close to the city. High slopes were also recorded in a rural, remote district West of Rome, and they decreased in intensity along the sea coast and in flat districts around the city. Eighty years later, while peripheral municipalities in the Apennine district still had the highest slope coefficients, more heterogeneous, peri-urban districts both west and east of Rome displayed high (or very high) slope coefficients. Taken together, local slope and R<sup>2</sup> coefficients reflect the polarization in districts with forest recovery and decline, which is likely because of wildfires, clearcutting, or other human/ecological disturbances. The gradual disappearance of coastal woodlands because of progressive clearcutting made the spatial distribution of forests more heterogeneous, in turn contributing to a less polarized landscape in urban and rural areas. At the same time, urban sprawl and the rising human pressure driven by sprawled urbanization has consolidated some smaller nuclei of forest expansion, acting as an 'endogenous source' of forest transitions (i.e., fueling the shift from net decline to net recovery) at the local scale.

#### **4. Discussion**

During the last century, a significant decrease in cropland and a contemporary increase in residential settlements and forests has been observed in Italy [50] and, more generally, in Southern Europe [23]. Land-use change was responsible for the expansion of 'contact areas' between developed (i.e., residential and/or industrial settlements) and natural/seminatural landscapes in Mediterranean Europe [24,25,52,53]. By confirming the predictions of the Forest Transition Theory, our study documents the relevance of a comprehensive landscape analysis along urban–forest interfaces to inform policies containing urbanization and mitigating the negative impact of human disturbance on the surrounding natural habitats [7]. Barbati et al. (2013) found that the spatial distribution of natural landscapes in recent times became more polarized along the metropolitan continuum [5]. A thorough understanding of the intrinsic mechanisms of urban growth and the consequent dynamics of natural landscapes—mainly forests—along this gradient provides an integral vision and future perspective to landscape research [48]. From this perspective, our study contributes to regional science suggesting how the notion of mono-centric (or poly-centric) urban expansion can be (more or less directly) extended to dynamics related to other land use, namely forests, underlying socioeconomic mechanisms and territorial processes that are governed (and are in turn influenced by) the intrinsic linkage between humans and nature at the base of regional landscape dynamics [8]. A global analysis failed in assessing these complex dynamics, suggesting how spatially explicit approaches are appropriate tools to investigate the importance of heterogeneous local contexts [33], especially when they assume a stronger role than regional drivers of change and local socioeconomic pressures [9].

The results of local regressions presented in our study provided evidence on the importance of a mono-centric structure of urban growth in Rome, which is in line with the empirical findings of earlier studies [5,16,42]—as plastically illustrated in Figure 2 earlier in this paper with the parallel consolidation of a moderately polycentric model of forest expansion [35]. This complex spatial pattern is assumed to be the joint impact of two processes: (i) intense (natural) forestation in mountainous and economically depressed areas and (ii) moderate (and mostly human-induced) forest recovery in flat and coastal areas, depending on the intensity of conflicts over alternative land-use, i.e., on the basis of the 'economic strength' of the agricultural sector [31,38,45,47]. As a matter of fact, cropland abandonment typical of economically marginal districts was observed also in some periurban areas originally devoted to agriculture because of particularly fertile soils [51]. In such areas, forest expansion occurred more likely than in other socioeconomic contexts of the same geographical region [5,39,48].

Being representative of more general dynamics at the regional scale, distinctive transformations of peri-urban landscapes were described in the study area considering together the empirical results of this study and earlier literature: (i) economic decline of remote districts resulting in re-forestation [28,50,54], (ii) crop intensification in more accessible rural districts [5,29,34], and (iii) urban sprawl, agricultural land consumption, and forest fragmentation in flat districts [35,55,56]. As a counter-intuitive result, forest increase was intrinsically associated with reduced proximity between settlements and natural land [51], as a result of forestation of abandoned cropland in accessible districts indirectly fueled by urban expansion [37–39]. From a socioeconomic standpoint, such transformations fueled the heterogeneous expansion of forest land cover [25], with ecosystem dynamics remaining essentially under-investigated [30].

From the spatial planning perspective, the results of our study highlight the need to adopt a multi-target strategy for the conservation of natural and semi-natural areas in peri-urban contexts [35]. This strategy should recognize forest expansion in peri-urban areas together as a positive process of greening [31] and a potentially negative phenomenon, as the increase in forest cover as well as in the length of the margin directly in contact with residential settlements causes an increased risk of wildfires in already vulnerable areas [47]. Future studies should better interpret long-term changes in metropolitan landscapes [37], clearly distinguishing the impact of socioeconomic drivers and planning/biophysical constraints to building, in turn clarifying the possible role of different forest and agroforest types in that change [5,45,48]. More generally, the analysis of changes in the forest landscape in peri-urban areas [46] cannot be separated from an accurate investigation of the causes of expansion and decline of agriculture in the facing rural contexts.

#### **5. Conclusions**

The present study refers to long-term changes in forest area that encompass a sufficiently long time period covering the productive cycle of a high forest. Long-term changes in forest cover include both natural and human-induced transformations and may account for a truly comprehensive picture of landscape changes. In this perspective, we tried to estimate the synergic impact of both drivers. The complex results of this study (i.e., the emergence of a polycentric model of forest expansion in a context of mono-centric expansion of urban settlements) outlines the urgent need for policy strategies managing urban–forest interactions along the fringe. Effective strategies governing peri-urban landscape dynamics should include (i) urban containment policies reducing the environmental impact of sprawl on fringe land and (ii) measures controlling the unwanted expansion of peri-urban woodlands (starting from the multiplication of a sort of 'hotspots' indirectly acting as nuclei of forest spread) at the expense of abandoned cropland.

Based on the empirical findings of this study and a pertinent review of the recent literature, these actions should target the integral preservation of the traditional diversity of agro-forest mosaics in Southern European fringe lands. Our study highlights the importance of monitoring techniques for the quantification of urban–forest ecosystem services, starting from a recent field experience in the Mediterranean context. New logical approaches and information technologies for the 'holistic' study of peri-urban forests are requested to integrate operational tools, methodologies, and analysis' techniques, with increasing, comparative evidence from case studies. Assessing the quality and health of peri-urban forests implies a reduction of monitoring costs, the improvement of sampling efficiency and accuracy, and a more effective integration of the existing measurement networks in a long-term vision that refers to the paradigms of ecological resilience and socioeconomic sustainability.

**Author Contributions:** Conceptualization, R.S. and G.Q.; methodology, L.S.; software, A.M.; validation, L.B. and G.E.; formal analysis, L.S.; investigation, R.S.; resources, G.Q.; data curation, G.E.; writing—original draft preparation, R.S.; writing—review and editing, L.S. and A.M.; visualization, L.B.; supervision, L.B.; project administration, A.M.; funding acquisition, G.Q. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** Official statistics derived from public websites were used in this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Research on Green Innovation of the Great Changsha-Zhuzhou-Xiangtan City Group Based on Network**

**Lu Wang <sup>1</sup> , Wenzhong Ye <sup>1</sup> and Lingming Chen 1,2,\***


**Abstract:** This article aims to promote the high-quality development of the Great Changsha-Zhuzhou-Xiangtan City Group and improve the green innovation efficiency of urban agglomeration. This article takes green innovation in networked urban agglomerations as its research subject. Furthermore, it analyzes the impact of network structure characteristics such as network scale and network structure hole on green innovation in urban agglomerations. Moreover, this study uses the unexpected output SBM model to measure green innovation efficiency of the eight prefecture-level cities in the Great Changsha-Zhuzhou-Xiangtan City Group from 2012 to 2018 and analyzes its influencing factors using the panel Tobit model. The results show that the overall green innovation efficiency of the Great Changsha-Zhuzhou-Xiangtan City Group is stable. The distribution of urban green innovation efficiency in the region is characterized by urban gradient and mid-stream drive. In the process of networked innovation, economic development, which has a positive impact on green innovation, promotes the overall effectiveness of the network structure. The low efficiency of urban educational resources, which has a negative impact on green innovation, leads to the redundancy of a network scale. The unapparent advantage of industrial structures, which have a negative impact on the development of green innovation, leads to the insufficient depth and breadth of network openness. Lastly, government support and the level of infrastructure have no impact on green innovation.

**Keywords:** Great Changsha-Zhuzhou-Xiangtan City Group; green innovation; network structure; unexpected output SBM model

#### **1. Introduction**

The Fifth Plenary Session of the 19th Central Committee of the Communist Party of China put forward a new goal of promoting green development and the harmonious coexistence between humans and nature. To launch the new development policy, the power institution proposed that the regional economic development takes green innovation as the new driving force, protects the environment, and considers the overall economic development situation to achieve sustainable economic growth. Due to the economic growth resulting from the traditional extensive economic development model, China is facing problems related to resource shortage, environmental pollution, and ecological destruction. The realization of sustainable economic and social development has become the focus of scholars. In the traditional economic and social development model, the model of high energy consumption and high pollution in exchange for economic benefits has caused damage to the ecological environment. Ignoring the environmental benefits generated by innovation behavior is not conducive to sustainable development. It is important to overcome the barriers of the traditional development model, take green innovation as the core driving force, and realize the leap of economic development and innovation. Green innovation is also known as environmental innovation, ecological innovation, or sustainable innovation. Compared with traditional innovation, green innovation aims at reducing environmental impact, achieving economic development while reducing resource

**Citation:** Wang, L.; Ye, W.; Chen, L. Research on Green Innovation of the Great Changsha-Zhuzhou-Xiangtan City Group Based on Network. *Land* **2021**, *10*, 1198. https://doi.org/ 10.3390/land10111198

Academic Editors: Luca Salvati, Iwona Cie´slak and Andrzej Biłozor

Received: 5 October 2021 Accepted: 3 November 2021 Published: 5 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

consumption and environmental costs, which is beneficial to building an efficient and green economic and social development environment.

Urban agglomerations are the main form of the global division of labor, new urbanization, and rural revitalization. In the context of China entering a new era, urban agglomerations face problems such as the continued expansion of population and infrastructure construction scale, prominent contradictions in the economy, resources, environment, and increased pressure on urban bearing. To fully implement the *Outline of Changsha-Zhuzhou-Xiangtan Regional Integrated Development Planning*, we argue for providing full play to the driving role of Changsha-Zhuzhou-Xiangtan in implementing the strategy of "Three High and Four New", improving the speed of urban development, and striving to build a modern city with harmonious coexistence between humans and nature. At the same time, we should improve the linkage mechanism of ecological environment co-protection, jointly solve environmental pollution problems, jointly protect the Changsha-Zhuzhou-Xiangtan ecology, and strive for the Changsha-Zhuzhou-Xiangtan national ecological civilization pilot zone. The construction of the Changsha-Zhuzhou-Xiangtan metropolitan area is a significant achievement contributing to the rise of central China and the development of the Yangtze River Economic Belt. In September 2020, General Secretary Xi Jinping visited Hunan and emphasized that the integrated development of Changsha, Zhuzhou, and Xiangtan should continue achieving exceptional results and insisted on leading innovation and green growth. Thus, we intend to research green innovation and its influencing factors in the Great Changsha-Zhuzhou-Xiangtan City Group.

The article is arranged as follows: Section 2 presents the literature review; Section 3 introduces the theoretical mechanism, including the effect of network structure characteristics on green innovation in the urban agglomeration, and the influencing mechanism of green innovation in the Great Changsha-Zhuzhou-Xiangtan City Group based on a network of green innovation in the urban agglomeration. Section 4 introduces the model methodology and variable selection, including the index system and the selection of the index. Section 5 uses the unexpected output SBM model to measure the efficiency of green innovation. Section 6 uses data from the Great Changsha-Zhuzhou-Xiangtan City Group (from 2012 to 2018) to empirically analyze the impact of network structure on green innovation, and test its theoretical mechanism. Section 7 summarizes the conclusions and makes recommendations, and proposes future research directions.

#### **2. Literature Review**

An urban agglomeration is a vital innovation space and carrier. Some scholars have constructed the innovation cooperation network model among urban agglomerations to optimize the innovation strategy of urban agglomerations [1–3]. At present, many scholars are investigating the relationship between urban agglomeration and innovation performance of the network structure. For example, Sheng Yanwen et al. [4] used social network analysis and the DEA model to analyze the impact of central position, intermediary position, and structural holes of urban agglomeration innovation network on innovation efficiency. Ren [5] constructed a regional innovation network system to explore the impact of network size, network openness, structural holes, strong ties, and weak ties on innovation capability. Other scholars, based on stochastic frontier analysis [6], data envelopment analysis [7], or canonical correlation analysis [8], analyzed the impact of network size, network openness, network structure holes, and network density on regional innovation.

Research on green innovation primarily includes the concept definition, index measurement, and driving factors. So far, scholars in various countries have gained a profound understanding of the concept of green innovation [9,10]. For example, Kemp [11] argued that green innovation is an activity directed toward reducing environmental pollution and resource waste. The methods commonly used for green innovation efficiency are generally divided into parametric methods and non-parametric methods, such as stochastic frontier analysis and data envelopment analysis (DEA) [12,13]. Meanwhile, there are two standard methods for green innovation evaluation: One is to construct the evaluation index of green

innovation [14] and the other is to use data envelopment analysis (DEA). For example, Tone [15] proposed the non-angle and non-radial DEA-SBM model, which solves the slack variables problem that the traditional model does not consider. Teng [16] verified the spatial correlation of green innovation development among cities in the Yangtze River Delta by combining the Super-SBM model with the GML index. The drivers of green innovation are mainly economic development [17], government support [18], and policy system considerations [19].

Green innovation research of the Changsha-Zhuzhou-Xiangtan urban agglomeration is mainly from green ecology and development, including: to evaluate the safety of the ecosystem of Changsha-Zhuzhou-Xiangtan urban agglomeration [20], to protect and construct green heart ecology [21], and to research the relationship between the construction of a two-oriented society ' comprehensive reform area and promote regional green development [22].

Compared with simply studying the green innovation of urban agglomerations, it is more appropriate to study the green innovation effect of the Changsha-Zhuzhou-Xiangtan City Group. In general, most of the existing literature suggests that green innovation can influence regional innovation development; however, conclusions based on the green innovation effect are not uniform. Moreover, the literature ignores the internal mechanism of the green innovation effect of urban agglomerations from the perspective of the network and the mechanism of the effect of other economic and social factors on the network structure in the process of green innovation. In terms of research samples, most of the literature is limited to provincial or national data, with macro sample size and coverage of the survey area or relatively mature urban agglomerations area, which has certain geographical achievements that ignore the micro aspects. Compared with the previous literature, the main contributions of this article are as follows: First, the Great Changsha-Zhuzhou-Xiangtan City Group is a micro-level region with vast space for innovation and progress regarding the economic development period, innovation period, and green environment construction period. In this regard, this article uses data from the Great Changsha-Zhuzhou-Xiangtan City Group from 2012 to 2018. The research selected eight prefecture-level cities in the Great Changsha-Zhuzhou-Xiangtan City Group specific to a certain degree of representativeness. Second, this article investigates the impact of network structure on green innovation using the unexpected output SBM model to measure the green innovation efficiency and the panel Tobit regression method of the re-centered influence function, which directly examines the marginal impact from the perspective of the network.

#### **3. Theoretical Mechanism Analysis**

#### *3.1. Analysis of the Effect of Network Structure Characteristics on Green Innovation in Urban Agglomeration*

The urban agglomeration network is the main form of urban spatial structure. For the development of urban agglomeration, the region is necessary to establish a network structure system. The network development of urban agglomeration is not only determined by internal attributes but also dominated by external dynamic factors. The dynamic mechanism of urban agglomeration network development focuses on green innovation. The key to researching networked green innovation rests within the high frequency of learning and communication between regional innovation subjects to promote the free flow of knowledge, technology, talents, and other innovation resources. By placing focus on the synergistic effect among innovation subjects in the urban agglomeration, this effect can improve the service of scientific and technological innovation and the level of achievement transformation to reduce the negative impacts on the environment and achieve green innovation. On the measurement of network characteristics for further characterization, domestic and foreign research mainly selects network scale, network openness, network structure hole, network density (network connection strength) [23]. Hence, the structural characteristics will be analyzed from four aspects: network scale, network openness, network structure hole, and network density. Its functions are as follows.

#### 3.1.1. Network Scale

Network scale in the network is the number of innovation subjects in urban agglomeration. The larger the number of creative subjects, the larger the network scale. The more substantial the heterogeneity between subjects, the richer the innovation resources. Heterogeneous network members have a high degree of complementary knowledge, which is conducive to reducing transaction costs and achieving results pertaining to the transformation of innovation.

#### 3.1.2. Network Openness

Network openness is the capacity of network entities to absorb external information, related technologies, and other innovative resources. The higher the degree of openness, the stronger the ability to acquire external information, knowledge, technology, and other creative resources conducive to innovation.

#### 3.1.3. Network Structure Hole

The more structural holes, the more social relations carry resources, providing opportunities for different innovation subjects to communicate and interact. The junction in the hub position occupies a structural hole and connects the creative subjects that lack direct contact to build a bridge of communication and cooperation, reduce information asymmetry effectively, and stimulate the realization of innovative achievements.

#### 3.1.4. Network Density

Network density reflects the connection degree of innovation nodes. The closer is the connection between nodes in the network, the stronger the intention of innovation subjects to carry out innovation cooperation, and the more conducive to exchange and collaboration. High network density provides an effective platform for building and maintaining trust and cooperation among innovation subjects, conducive to obtaining external innovation resources within the innovation network, realizing the transformation of innovation achievements.

#### *3.2. Influencing Mechanism of Green Innovation in the Great Changsha-Zhuzhou-Xiangtan City Group Based on Network*

Regional knowledge production and technology transformation achieve development through innovation networks. Typically, innovation motivation stems from the connection between innovation subjects. Economic development provides a conducive environment for the flow of regional knowledge, technology, and other elements; government funding significantly contributes to the smooth running of innovative activities; improving the information infrastructure is a prerequisite for the flow of innovative talents among regions; the tertiary industry enables innovation subjects to have a competitive advantage, expanding the scale of innovation; urban education to promote regional innovation activities to carry out the study provides a platform for exchange. Hence, based on network structure characteristics, this paper arranges the influencing mechanisms of green innovation in the Great Changzhou-Zhuzhou-Xiangtan area, as shown in Figure 1.

#### 3.2.1. Economic Development Level

Economic development is the strategic basis for building an innovative city and carrying out creative activities. Usually, the size of the regional economy and resources constrain innovation elements and innovation activities, and innovation communities are jointly built by innovation subjects and governments within the region, creating an implementable innovation network structure [24].

#### 3.2.2. Government Support

The results of government support for innovation activities are affected by the intensity of investment. The reasons are as follows: First, the increase in government input

stimulates enterprises and other innovation subjects to carry out scientific and technological innovation collaborations; second, the tendency of government investment, which crowds out private investment, leads to the crowding-out effect caused by the excessive accumulation of social capital. The government provides fiscal and tax subsidies for enterprises to build innovation network platforms. Enterprises in the innovation network rely on the support of the government to integrate innovation resources effectively and promote the development of innovative networks to enhance competitive advantages for regional innovation cooperation. Excessive capital tilt, which is not conducive to the transformation of research results, triggers inefficient capital utilization, inhibits the effect of large-scale innovation, imbalances the distribution of innovation elements, and hinders the flow of talents, knowledge, technology, and other elements in the innovation network.

**Figure 1.** Influencing mechanisms of green innovation.

#### 3.2.3. Information Infrastructure

Well-developed infrastructure is the primary condition for a region to attract innovative talent. A well-developed information network promotes knowledge flow and technology diffusion among innovation subjects. Moreover, convenient transportation and information networks provide essential conditions for expanding the scale of regional networks and enabling enterprises to engage in innovative activities.

#### 3.2.4. Industry Structure

The industrial structure embedded in the innovation network is the reintegration and redistribution of resources. Innovative enterprises use innovation networks to enhance their competitiveness and optimize the structures of emerging industries. Based on undertaking traditional industries, urban agglomerations integrate into innovation networks, thus forming professional industrial layouts. The long-term, knowledge spillover in the innovation network leads to a nonlinear process from inferiority to continuous optimization of the industrial structure in innovation activities. Furthermore, with the improvement of resource utilization, innovation achievements are successfully transformed.

#### 3.2.5. Urban Education Level

Urban education aims at cultivating high-quality and heterogeneous innovative talent. Importantly, only in the innovation network can heterogeneous talent realize resource complementarity and technology sharing, thus generating a synergistic effect among innovation subjects [25]. The embeddedness of network structure has an essential impact on innovation activities. Heterogeneous human capital, which is located in the hole of network structure, can obtain information resources by building relations with key innovation subjects and occupying information advantages. The more educated network members, the larger the network scale. Meanwhile, the more information resources embedded in the

network, the more frequent communication and interaction conducive to constructing a complete network system.

#### **4. Model Methodology and Variable Selection**

#### *4.1. Research Methodology*

#### 4.1.1. SBM Model with Non-Expected Output

Scholars mainly used the non-parametric DEA method in early studies on the measurement of green innovation efficiency. Since the traditional DEA model assumes that the output is the desired output, ignoring the influence of slack variables will significantly increase the measured efficiency values. At the same time, the indicators used in the experimental production process will produce lower efficiency. Therefore, the SBM model proposed by Tone [15] can effectively avoid the measurement bias caused by traditional DEA and measure the green innovation efficiency of the cities within the Great Changsha-Zhuzhou-Xiangtan City Group. The following is the constructed evaluation model of green innovation efficiency.

$$\begin{aligned} \mathbf{Min}\rho &= \frac{1 - \frac{1}{m} \sum\_{i=1}^{n} \frac{s\_i^-}{s\_i^+}}{1 - \frac{1}{q\_1 + q\_2} \left( \sum\_{r=k}^n \frac{s\_r^+}{r\_{rk}^+} + \sum\_{i=1}^n \frac{s\_i^-}{r\_{rk}^-} \right)} \\ &\text{ } \begin{cases} \sum\_{i=1}^n x\_{ij}\lambda\_j - s\_i^- \le x\_{ik} \\\ j = 1 \\\ j \ne k \\\ \sum\_{\begin{subarray}{c} i \in \mathcal{I} \\ j \ne k \end{subarray}} y\_{ij}\lambda\_j - s\_i^+ \le y\_{\ i\}} \\\ j \ne k \\\ \sum\_{\begin{subarray}{c} z\_{ij} \ne 0 \\ j \ne k \end{subarray}} z\_{ij}\lambda\_j - s\_i^{z^-} \le z\_{\ rk} \\\ j \ne k \\\ \lambda\_r s^-, s^+ \ge 0 \end{aligned} \end{aligned} \tag{1}$$

In the formula, there are *n* decision units (DUM), which include the input, expected output, and unexpected output. The number of types is represented by *m*, *q*1, *q*2, respectively. The slack variables of input, expected output, and unexpected output are represented by *s* −, *s* <sup>+</sup>, *s* z − , respectively. In general, *λ* is the weight of the decision unit and *ρ* is the efficiency value of DMU. As known, the value range is between 0 and 1. Therefore, effective DUM is obtained through the SBM model of unexpected output.

#### 4.1.2. Panel Tobit Model

The Tobit regression model, also known as a truncated model, was proposed by Tobit. The limited Tobit regression model can avoid the deviation of ordinary least square estimation results. The Tobit regression model is used to ensure unbiased and valid estimation results. After the measurement of green innovation efficiency is completed, the value range of GIE is between 0 and 1, and the basic model is as follows:

$$\begin{cases} Y\_{\text{it}}^{\*} = \alpha\_{0} + X\_{\text{it}} \beta + \varepsilon\_{i} \\ Y\_{\text{it}} = Y\_{\text{it}}^{\*} \text{ if } 1 \ge Y\_{\text{it}}^{\*} \ge 0 \\ Y\_{\text{it}} = 0 \text{ if } \ Y\_{\text{it}}^{\*} \le 0 \\ Y\_{\text{it}} = 1 \text{ if } \ Y\_{\text{it}}^{\*} \ge 1 \end{cases} \tag{2}$$

In the formula, *Y* ∗ *it* the explanatory variable vector, *Y<sup>i</sup>* is the value of the observed dependent variable, *β* is the regression parameter vector, and *ε<sup>i</sup>* represents the stochastic perturbation term. The panel Tobit regression model constructed based on influencing factors is as follows.

*G IE* = *α*<sup>0</sup> + *β*<sup>1</sup> ln *GDPit* + *β*<sup>2</sup> ln *GOVit* + *β*<sup>3</sup> ln *INFit* + *β*<sup>4</sup> ln *INSit* + *β*<sup>5</sup> ln *EDUit* + *εit* (3)

*4.2. Variables and Data* 4.2.1. Variable Selection Input Variables

Green innovation efficiency is a complex system with multiple inputs and outputs. The Great Changsha-Zhuzhou-Xiangtan City Group constitutes a networked innovation system. Selecting corresponding indicators is based on green innovation input and green innovation output to estimate the green innovation efficiency of eight cities in the Great Changsha-Zhuzhou-Xiangtan City Group. According to the status quo of green innovation in the Great Changsha-Zhuzhou-Xiangtan City Group and the data availability, we constructed the evaluation index system of green innovation efficiency. The measurement of green innovation efficiency of cities in the Great Changsha-Zhuzhou-Xiangtan City Group should reflect the theme "Green" and the network effect of urban agglomeration innovation. Therefore, this paper integrates the green concept into the input-output factors and comprehensively considers the environmental pollution situation. Based on the network characteristics of urban agglomeration, the input indexes mainly include: First, using the number of universities, research institutions, and enterprises to represent network scale; second, network openness measures the ability of urban agglomeration to absorb external knowledge and information by foreign direct investment; third, the network structure hole selects the technology market transaction amount as the bridge of knowledge flow and technical cooperation among innovation subjects in the innovation network; fourth, the network density is represented by the external expenditure of Research and Development (R&D) expenditure in universities [23].

#### Output Variables

Output can be divided into expected output and unexpected output. Expected outputs mainly include patent applications and per capita GDP. Patent data can reflect the results of urban innovation output comprehensively; therefore, the number of patent applications is selected to represent. Per capita GDP demonstrates the level of regional economic growth. Generally, as unexpected outputs, the comprehensive environmental index is used to represent the environmental benefit output of the undesired output, and the environmental pollution comprehensive index is calculated by the entropy weight method of the three industrial wastes, which are the industrial wastewater discharge, the industrial sulfur dioxide discharge, and the industrial smoke emission respect, respectively. Specific indicators are shown in Table 1 below.


**Table 1.** Evaluation index system of green innovation efficiency.

Theoretically, in urban innovation activities, any factors related to innovation activities will impact innovation efficiency. According to the current situation of urban green development, economic development requires a high level of technical innovation. On the contrary, the economic development level is also bound to increase innovation input, forming a positive feedback effect. Therefore, the influencing factors of green innovation efficiency should include the level of economic development (*GDP*), which is represented by per capita GDP. Since communication infrastructure plays the most important role in innovation among the four infrastructures of the highway, railway, communication, and energy, it is expressed as the proportion of regional postal and telecommunication services in GDP (*INF*) [26]. Government support (*GOV*) is represented by the proportion of regional science and technology financial expenditure in local financial expenditure. Industrial structure (*INS*) uses the proportion of the added value of the tertiary industry in the gross regional product, which reflects the ratio of the tertiary industry in the national economy to measure. Urban education can provide high-quality human capital for innovation and become a significant factor influencing the regional innovation system. Therefore, this study selected urban education level (*EDU*) as the influencing factor and represented by the number of university students [27] (see Table 2 for details).

**Table 2.** Influencing factors and variables of green innovation efficiency.


#### *4.3. Data Sources*

We included eight prefecture-level cities in the Great Changsha-Zhuzhou-Xiangtan City Group from 2012 to 2018 as the research objects, namely, Changsha, Zhuzhou, Xiangtan, Hengyang, Yueyang, Changde, Yiyang, and Loudi. The original data of the indicators in this paper came predominantly from the statistical bulletins of the corresponding prefecture-level cities in Hunan Statistical Yearbook, Hunan Science and Technology Statistical Yearbook, and China Urban Statistical Yearbook (2012–2018). Some missing values are complemented by linear interpolation.

#### **5. Measurement and Characteristics of Green Innovation Efficiency of Great Changsha-Zhuzhou-Xiangtan City Group Based on Network**

*5.1. Measurement Results of Green Innovation Efficiency of the Great Changsha-Zhuzhou-Xiangtan City Group Based on Network*

Using the constructed SBM model of unexpected output to calculate the green innovation efficiency of cities in the Great Changsha-Zhuzhou-Xiangtan City Group from 2012 to 2018, the results are shown in Table 3. Meanwhile, cloud model and system cluster analysis were used to analyze the normal distribution and segmentation of green innovation efficiency in the Great Changsha-Zhuzhou-Xiangtan City Group. The results are shown in Figures 2 and 3, respectively.

**Table 3.** Green innovation efficiency of urban agglomeration.


**Figure 2.** Distribution trend map of green innovation efficiency in the urban agglomeration.

**Figure 3.** Normal cloud-membership degree distribution of green innovation efficiency in the Great Changsha-Zhuzhou-Xiangtan City Group.

#### *5.2. Characteristics Analysis of Green Innovation Efficiency of the Great Changsha-Zhuzhou-Xiangtan City Group Based on Network*

The green innovation efficiency of the Great Changsha-Zhuzhou-Xiangtan City Group was generally stable. In terms of the overall time series, the mean green innovation efficiency values of the Great Changsha-Zhuzhou-Xiangtan City Group from 2012 to 2018 were 0.7030, 0.7108, 0.8184, 0.7951, 0.8929, 0.8031, and 0.7729, respectively. The overall mean values of green innovation efficiency fluctuated at a medium level and showed a stable trend. This may be because Hunan province has focused on constructing a two-oriented society in the core area of urban agglomeration, improving the overall green development of the Great Changsha-Zhuzhou-Xiangtan City Group to a certain extent in recent years. At the same time, influenced by the international environment, China's economy has slowed down; with tremendous downward pressure, the transformation of technological innovation achievements into practical productivity in the Great Changsha-Zhuzhou-Xiangtan City Group is insufficient. In the meantime, rational allocation and utilization of innovative resources are hindered, so the overall efficiency is low. The distribution trend of green efficiency in urban agglomeration is shown in Figure 2. Therefore, the green innovation development of urban agglomeration cannot be achieved without a favorable innovation environment.

The green innovation efficiency of the Great Changsha-Zhuzhou-Xiangtan City Group presents the characteristics of "urban gradient" development. As shown in Figure 3, Changsha and Yueyang rank high in green innovation efficiency and have a maximum advantage for green innovation development. On the one hand, Changsha has an extensive network

scale and high network openness. As a provincial capital city, Changsha is inextricably linked to its position in the network structure hole in the innovation system. As a network structure hole for strengthening regional cooperation, Yueyang is the "main battlefield" of Hunan's growth pole construction and an essential fulcrum for docking the Yangtze River Economic Belt construction. Other cities in the urban agglomeration—Zhuzhou and Xiangtan—have dominant industries concentrated in secondary industries, such as iron and steel industries. Therefore, extensive development may cause environmental pollution; their green innovation efficiency, therefore, is slightly behind Changsha. Yiyang has continuously lagged with its green innovation efficiency, which has restricted the balanced development of green innovation in the Great Changsha-Zhuzhou-Xiangtan City Group. According to the echo theory, the development of green innovation is limited by its economic scale, resulting in a lack of endogenous motivation and issues with green development [28]. Therefore, we can conclude that it can be divided into three gradients: (1) Changsha and Yueyang, (2) Yiyang, and (3) other cities.

The "mid-stream drive" difference of green innovation efficiency in the Great Changsha-Zhuzhou-Xiangtan City Group is noticeable. Figure 3 shows the apparent differences between cities such as Changsha and Yiyang and other cities in the urban agglomeration; however, other cities need further analysis based on Figures 4 and 5. We can see from Figure 4 that Xiangtan, Changde, and Hengyang are classified into one category, and the green innovation efficiency value is between 0.6 and 0.7. Combined with Figure 5, Xiangtan moves up by one place, Changde moves up by one place, and Hengyang moves up by two. The reasons are as follows. First, Xiangtan encourages independent innovation and strengthens R&D and project promotion by the government bonus policy. For example, the *Implementation Measures for Encouraging Scientific and Technological Innovation* in Xiangtan High-tech Zone, issued in 2016, enhanced the guiding role of government expenditure on green innovation and promoted the innovation efficiency of the region to maintain an excellent level. The Changde Office of Science and Technology issued some awards in *Changde Science and Technology Awards* in 2017 to stimulate the initiative of innovation subjects, accelerate the transformation of innovation achievements, and improve the innovation system. Hengyang proposed *Measures for The Administration of Subsidies for Science and Technology Services (trial)* for innovative services subsidies, enhancing the innovation capacity of relevant enterprises. Second, Zhuzhou and Loudi are the second types, with a green innovation efficiency ranging from 0.85 to 0.90. Zhuzhou moves up by one place, while Loudi moves down by four places. The reason may be that the limited economic scale is the main factor in developing green innovation. The small scale of a regional innovation network and insufficient innovation investment seriously hinder the improvement of innovation efficiency.

**Figure 4.** Cluster tree of green innovation efficiency in the Great Changsha-Zhuzhou-Xiangtan City Group.

**Figure 5.** Trend of green innovation efficiency in the Great Changsha-Zhuzhou-Xiangtan City Group.

#### **6. Research on The Influencing Factors of Green Innovation in the Great Changsha-Zhuzhou-Xiangtan City Group Based on Network**

Based on the theoretical mechanism of the network and the green innovation efficiency measured by the unexpected output SBM model, the empirical analysis of influencing factors is conducted. Unit root tests were carried out prior to regression result analysis to ensure the rigor and correctness of regression results.

#### *6.1. Unit Root Test*

Combined with the SBM model of unexpected output, this paper calculates the green innovation efficiency data of the Great Changsha-Zhuzhou-Xiangtan City Group from 2012 to 2018. It conducts regression analysis with green innovation efficiency as the dependent variable. Firstly, the unit root test of panel data is carried out to enable a more rigorous and reliable empirical process and avoid the pseudo-regression caused by the non-stationary variables to ensure the effectiveness of the estimation results. The stationary unit root test mainly uses LLC, IPS, ADF-Fisher, and PP-Fisher methods to test the variables. The formula of the unit root test is as follows:

$$
\chi\_{it} = \rho\_i \chi\_{i, t-1} + \varepsilon\_{it} \tag{4}
$$

The unit root test can be divided into two categories: one category is the LLC test, which is the same regression coefficient. Additionally, it changes according to different cross-sections, namely, IPS test, ADF-Fisher test and PP-Fisher test. The first test results show that the sequence of a variable is unstable, so take the logarithm to carry on the stability test. As shown in Table 4, the results obtain the overall rejection of the original hypothesis of 'existence of unit root', and then carry out the panel Tobit random effect regression; the results are shown in Table 5.

#### *6.2. Analysis of Regression Results of Influencing Factors*

According to the regression results, the relevant influencing factors are as follows:

First: the economic development level. The economic development level regression results show a significantly positive impact on green innovation efficiency, and the impact coefficient is 0.9745. The level of regional economic development promotes the improvement of green innovation efficiency. A higher GDP encourages individuals to pursue green and low-carbon products and facilitates the optimal allocation of green innovation resources [29]. Moreover, it effectively avoids the redundancy of network scale, enables the effectiveness of the network structure as a whole, and improves the conversion rate of innovation achievements.


**Table 4.** Unit root test results.

Note: \*\* represents significance at the 5% level, and \*\*\* represents significance at the 1% level.

**Table 5.** Regression results of influencing factors of green innovation in the Great Changsha-Zhuzhou-Xiangtan City Group based on network.


Note: \*\*\* represents significance at the 1% level.

Second: government support. The regression results show that the efficiency of green innovation has a negative impact and is not statistically significant. The reasons are as follows. First, in government support for green innovation investment, excessive government involvement has caused innovation resource wastage. Second, excessive government expenditure leads to the crowding-out effect in the innovation activities of enterprises and scientific research institutions. Therefore, the government should invest a reasonable range of fiscal expenditure and rationalize innovation resources.

Third: the level of information infrastructure. Regression results show that the efficiency of green innovation has a positive impact, but not through the significance test. Results show that the current level of information infrastructure of the Great Changsha-Zhuzhou-Xiangtan City Group shows an Internet trend, which has a 'crowding out effect' on postal and telecommunication services. The Internet is introduced into the innovation network as a carrier to enable the innovation synergy relationship informatization and reduce information transaction costs. Therefore, the level of networked information infrastructure has higher innovation efficiency. This conclusion is similar to the research in Zhongchao Wu [23].

Fourth: industrial structure. The empirical results show that industrial institutions have a negative impact on green innovation and pass the significance test. Possible reasons are as follows: first, in the process of embedding the tertiary industry into the innovation

network, enterprises and other innovation subjects fail to make full use of innovation resources, impeding the process of knowledge spillover; the competitive advantage is not apparent, leading to the failure of the tertiary industry to significantly promote the improvement of green innovation efficiency [30]. Second, the depth and breadth of network openness are inadequate, which is not conducive to the flow and reorganization of industrial elements. Therefore, penetrating the barriers of the closed innovation system, building a high-level open innovation platform, and optimizing resource allocation are the keys to the development of green innovation.

Fifth: the urban education level. The regression results of the urban education level show that it negatively influences green innovation efficiency, and the influence coefficient is 0.1358. This indicates that the current education level has no significant promoting effect on improving green innovation efficiency in the Changsha-Zhuzhou-Xiangtan City Group. Possible reasons are as follows: school education fails to achieve innovation-driven teaching, and innovative education resources in universities fail to achieve innovation integration with scientific research institutions and related enterprises. Moreover, low-efficiency allocation of innovation resources leads to redundancy of network scale; heterogeneous human capital located in the hole of network structure cannot obtain relevant information resources through connecting innovation subjects. Therefore, there is no significant improvement in innovation efficiency, similar to the research result of Yuwen Peng [31].

#### **7. Conclusions and Recommendations**

Urban agglomeration can be regarded as a network innovation system with 'input– output.' Based on a heterogeneous network perspective, this paper constructs an unexpected SBM model including R&D investment and analyzes the green innovation efficiency of eight cities in the Great Changsha-Zhuzhou-Xiangtan City Group from 2012 to 2018. On this basis, the panel Tobit model is used to analyze the influencing factors of green innovation efficiency to provide feasible suggestions for accelerating the innovation development of the Great Changsha-Zhuzhou-Xiangtan City Group and realizing energy conservation and emission reduction. The following conclusions are drawn:

First, the overall mean value of green innovation efficiency in the Great Changsha-Zhuzhou-Xiangtan City Group fluctuates between 0.70 and 0.85. The overall efficiency value is at the medium level, showing a stable trend. The green innovation efficiency has particular room for improvement. Specifically for cities, the distribution of urban green innovation efficiency within the region has the characteristics of gradient development and mid-stream drive apparent differences.

Second, in the process of networked innovation, economic development to promote the overall effectiveness of the network structure has a positive influence on green innovation. The low efficiency of urban educational resources utilization and the weak advantage of industrial structure renders the depth and breadth of network openness unsuitable for the development of green innovation; the level of government support and infrastructure has no significant effect on it.

Based on the empirical analysis, this paper proposes the following recommendations:

First, the high-quality development of Hunan province must promote regional collaborative innovation and green development. Hunan province should implement the strategy of "Three High and Four New", giving full play to the radiation role of the core area of the Great Changsha-Zhuzhou-Xiangtan City Group and linking the cities surrounding the Great Changsha-Zhuzhou-Xiangtan City Group. The cities Changsha and Yueyang, with improved green development, should set an example for the radiation range of green innovation. We are encouraging enterprises to independently innovate and play leading roles as enterprises, universities, and research institutions in innovation and create an open and efficient platform for the transformation of innovation achievements. Zhuzhou, Xiangtan, Hengyang, and Changde cities should continuously maintain their advantage of being efficient cities—overcoming regional boundaries to achieve regional cooperation. For cities with a small economic scale, such as Loudi, we should strengthen regional cooperation development, draw lessons from the experience of efficient city development, build an innovation cooperation center system, increase the scale of network construction, and gradually realize the transformation of innovation achievements. For cities with insufficient green innovation and development capacity in Yiyang, we should actively learn from the green development experience of Changsha, improve the construction of collaborative mechanisms for urban green innovation, and encourage technological innovation to benefit innovative development.

Second, it is essential to form an innovation network system with enterprises at its core, including higher-education institutions, research institutions, and other innovation subjects; expand the scale of the innovation network, strengthen the innovation links between subjects, further enhance the enthusiasm and initiative of independent innovation, and effectively improve the efficiency of green innovation.

Third, economic development mainly depends on technological innovation, accelerating technological innovation research and development. The transformation of the economic development model is essential in order to save resources, protect the environment, focus efforts on green innovation efficiency, actively learn from the developed areas of green innovation development, optimal green innovation resources allocation, and enhance the efficiency of green innovation.

Fourth, it is vital to vigorously improve the construction of education service systems and invest reasonably in education resources. We will foster outstanding innovative talent, guide creative and talented university graduates to form enterprises and realize the diffusion of technological knowledge. The purpose is to ensure the rational utilization of regional innovation resources, promote the upgrading of industrial institutions, and realize the development of green innovation.

**Author Contributions:** Conceptualization, L.W. and L.C.; methodology, L.W. and L.C.; software, L.W. and L.C.; formal analysis, L.W. and L.C.; resources, L.W. and L.C.; data curation, L.W. and L.C.; writing—original draft preparation, L.W.; writing—review and editing, L.W. and L.C.; supervision, L.W., W.Y. and L.C.; project administration, W.Y. and L.C.; funding acquisition, W.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was funded by The National Social Science Fund of China (grant number 20BGL299).

**Data Availability Statement:** We are taking eight prefecture-level cities in the Great Changsha-Zhuzhou-Xiangtan City Group from 2012 to 2018 as the research objects, including Changsha, Zhuzhou, Xiangtan, Hengyang, Yueyang, Changde, Yiyang, and Loudi. The original data of the indicators in this paper mainly come from the statistical bulletins of the corresponding prefecturelevel cities in *Hunan Statistical Yearbook, Hunan Science and Technology Statistical Yearbook, and China Urban Statistical Yearbook (2012–2018)*.

**Acknowledgments:** We would like to express our gratitude to all those who helped us during the writing of this article. Our deepest gratitude goes first and foremost to Yaping Hu, who are from the Hunan University of Science and Technology in China, for their constant encouragement and guidance.

**Conflicts of Interest:** The authors declare no conflict of interest.

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## *Article* **Combining Traffic Microsimulation Modeling and Multi-Criteria Analysis for Sustainable Spatial-Traffic Planning**

**Irena Ištoka Otkovi´c 1,\* , Barbara Karleuša <sup>2</sup> , Aleksandra Deluka-Tibljaš <sup>2</sup> , Sanja Šurdonja <sup>2</sup> and Mario Maruši´c <sup>1</sup>**


**Abstract:** Spatial and traffic planning is important in order to achieve a quality, safe, functional, and integrated urban environment. Different tools and expert models were developed that are aimed at a more objective view of the consequences of reconstruction in different spatial and temporal ranges while respecting selection criteria. In this paper we analyze the application of the multi-criteria analysis method when choosing sustainable traffic solutions in the center of a small town, in this case Beliš´ce, Croatia. The goal of this paper is to examine the possibility of improving the methodology for selecting an optimal spatial–traffic solution by combining the quantifiable results of the traffic microsimulation and the method of multi-criteria optimization. Socially sensitive design should include psychological and social evaluation criteria that are included in this paper as qualitative spatial–urban criteria. In the optimization process, different stakeholder groups (experts, students, and citizens) were actively involved in evaluating the importance of selected criteria. The analysis of stakeholders' survey results showed statistically significant differences in criteria preference among three groups. The AHP (Analytic Hierarchy Process) multi-criteria analysis method was used; a total of five criteria groups (functional, safety, economic, environmental, and spatial–urban) were developed, which contain 21 criteria and 7 sub-criteria; and the weights of criteria groups were varied based on stakeholders' preferences. The application of the developed methodology enabled the selection of an optimal solution for the improvement of traffic conditions in a small city with the potential to also be applied to other types of traffic–spatial problems and assure sustainable traffic planning.

**Keywords:** sustainable spatial–traffic planning; microsimulation traffic modeling; AHP; multi-criteria analysis; sensitivity analysis; stakeholders' preferences; public participation

#### **1. Introduction**

Reconstruction of segments of the existing traffic network according to the principles of sustainable urban mobility includes multidisciplinarity and consideration of spatial interventions through holistic criteria. Sustainable traffic and spatial planning should equally meet urban mobility development goals and the needs of traffic users, as well as understand and preserve the natural environment [1].

The European Commission has adopted a Sustainable Urban Mobility Plan (SUMP), a document that sets the framework for planning and managing urban mobility with the goal of improving the urban quality of life by creating a safe, reliable, integrated, multimodal, efficient, and environmentally friendly traffic system [2].

In order to positively influence the choice of movement modalities and encourage active and more environmentally friendly forms of urban mobility, it is necessary to provide accessible, safe, comfortable, and attractive traffic areas intended for pedestrians and cyclists, and various forms of micromobility and, at the same time, respect necessary

**Citation:** Ištoka Otkovi´c, I.; Karleuša, B.; Deluka-Tibljaš, A.; Šurdonja, S.; Maruši´c, M. Combining Traffic Microsimulation Modeling and Multi-Criteria Analysis for Sustainable Spatial-Traffic Planning. *Land* **2021**, *10*, 666. https:// doi.org/10.3390/land10070666

Academic Editors: Iwona Cie´slak, Andrzej Biłozor and Luca Salvati

Received: 14 June 2021 Accepted: 21 June 2021 Published: 24 June 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

requirements for motorized traffic. Planning and construction of traffic infrastructure was traditionally aimed at meeting traffic demand, but the analyses clearly show that the planning approach is changing and that the design of traffic infrastructure is effectively used to generate the desired forms of mobility [3]. The central parts of cities were spatially defined in the distant past, and the continuous increase of the degree of motorization and of the size of the population impose the need to meet numerous and often conflicting objectives during reconstruction, and the choice of an optimal solution becomes a question of multidisciplinary cooperation and the application of a multi-criteria optimization [4].

Microsimulations are a frequently used tool in assessing the functional (traffic) characteristics of alternative transport infrastructure solutions [5,6]. The assessment of alternative solutions based on traffic and cost criteria does not provide a sufficiently broad approach for sustainable and socially sensitive spatial planning, so it is necessary to include other aspects of potential solutions.

The aim of this paper is to give a methodological contribution to the evaluation of alternative solutions of transport infrastructure, including cost, microsimulations as quantitative indicators of traffic conditions, and spatial–urban criteria as qualitative criteria in a broader context that allows for the application of multi-criteria analysis methods. The methodology for evaluating alternative solutions applied in this research is to combine the multi-criteria analysis methods, traffic micro-simulations, and a socially sensitive spatial planning approach to develop a model that unites a conventional approach to traffic planning based on traffic demand of all modes of traffic and a sustainable approach in planning based on ensuring accessibility of space with active public participation in the assessment of alternative solutions and selection of the optimal solution.

In this paper, the Analytic Hierarchy Process (AHP) [7] multi-criteria analysis method is applied to a case study, selecting the optimal solution for the reconstruction of traffic areas at the level of a conceptual solution for part of the traffic network in the center of the town of Beliš´ce in Croatia. The local inhabitants, as well as the professional and non-professional public, were involved in the process of choosing the optimal solution through several steps in the procedure, and the traffic microsimulation technique was used to verify future traffic needs.

After the introduction, the Section 2 of this paper provides an overview of the literature from the research topic. Section 3, entitled "Study Area," describes the spatial and traffic characteristics of the selected case study, the central zone of the town of Beliš´ce in Croatia. Section 4 explains in detail the methodological steps and their application in the selected case study—a combination of traffic microsimulation modeling and MCA. In the Section 5, the results of the application of the analyzed methodology are presented and discussed. The results of quantitative and qualitative criteria are analyzed, as well as different stakeholder preferences. Section 6 presents the concluding considerations arising from the application of the proposed innovative methodology for the assessment of alternative solutions for the reconstruction of transport infrastructure and selection of the optimal solution.

#### **2. Literature Review**

The reconstruction of traffic infrastructure in built and spatially defined urban zones according to the principles of sustainable urban mobility is a complex task. On the one hand, we have different transport users who have different mobility needs and modalities that include vehicle traffic demand, and on the other hand, there are traffic safety requirements, socially sensitive spatial design, environmental impact, and users and community costs that need to be reconciled in the limited available space. Active forms of mobility that are environmentally friendly have additional positive effects because they take up less space and are economically acceptable, so they are often the focus of designers when the reconstruction of transport infrastructure is planned in city centers.

Urban planners and analysts draw particular attention to the concept of urban "walkability", which connects urban design and traffic infrastructure with wider objectives such as public health, ecological and economic objectives, and social equality. The very notion of

walkability is complex and is used to describe quite different kinds of phenomena. Pikora et al. [8] presented the development of a framework of potential environmental influences on walking and cycling. The framework includes four features: functional, safety, aesthetic, and destination. Ewing and Handy [9] highlighted the five criteria for the evaluation of traffic infrastructure intended for pedestrian movement: imageability, enclosure, human scale, transparency, and complexity. Forsyth [10] analyzed walkability from three different points of view: conditions (walkable environments, infrastructure, quality, safety), outcomes (making places lively and sociable), and better urban places (walkability provides a holistic solution to a variety of urban problems). According to Ruiz-Padillo et al. [11], the concept of walkability refers to the extent to which a neighborhood is walking-friendly and defines three key influential factors: public security, traffic safety, and pavement quality.

The traffic safety of pedestrians is an important influential factor in selecting the modality of movement, and numerous studies have focused on the analysis of pedestrian behavior, particularly in the risky segments of the traffic network, such as zones of potential conflict between pedestrians and vehicle–pedestrian crossings. Various studies have been performed and models developed for predicting the behavior of pedestrians [12–14] and vulnerable groups such as children [15] and the elderly [16], with the aim of identifying the influential parameters on pedestrian behavior in the traffic safety analysis. The results of different studies show that pedestrian movement is under significant influence of the cultural heritage of a specific environment and traffic and spatial conditions; therefore, such models are not universally applicable [14,17].

The presence and optimal placement of functionally mixed urban contents in walking distance are the prerequisite for the choice of the pedestrian movement modality. Dovey and Pafka [18] analyzed density, functional mix, and access networks as key factors of urban pedestrian mobility, but highlighted a key distinction between walkability and walking. These factors ensure spatial prerequisites that can be motivating or demotivating, but do not directly cause walking.

Su et al. [19] identified the most common indicators in existing indicator classification frameworks—connectivity, accessibility, suitability, serviceability, and perceptibility—and established an indicator classification system through expert panel evaluation for auditing street walkability in China. Bleˇci´c et al. [20] presented a survey of operational methods for walkability analysis and evaluation that are potentially useful as tools for sustainabilityoriented urban design.

Zuniga-Teran et al. [21] identified gaps and strengths in the Leadership in Energy and Environmental Design for Neighborhood Development (LEED-ND) certification system, through the lens of the nine categories of walkability—connectivity, land use, density, traffic safety, surveillance, parking, experience, greenspace, and community. Within this evaluation system, parking is mentioned as an important element in the evaluation of spatial potentials and shows a clear link between parking and pedestrian infrastructure planning.

A personal car spends around 5% of the total time in movement [22], so the planning approach to stationary traffic for personal vehicles, which adapts to the needs of users, was proven to be ineffective in the time perspective. Traffic infrastructure intended for stationary traffic is efficiently used as an influential factor when choosing movement modalities [23], and in the lack of space in urban-defined central parts of cities, urban areas should be ceded to more efficient traffic modalities.

Zahabi et al. [24] analyzed the impact of surface purpose, public urban transport offers, parking capacities, and parking prices on the choice of urban mobility modalities. The results of the study showed that a USD 1 increase in the hourly parking price would imply an increase of 5% in the probability of using public transport and a 10% increase in the public transport fare would represent a 10% average reduction in the probability of using public transport.

Available and quality transport infrastructure is a prerequisite, but not necessarily a decisive factor in choosing the modality of urban mobility, as shown by the comparison between mobility habits in different cities [25]. Motivational factors for the choice of mobility

modalities include environmental [1], economic level, climate, available public transport services [24], etc. A deeper analysis of the complex psychological and sociological factors influencing mobility motivation is explored within the sociology of mobility [26]. Mobility can be analyzed as capital that has significant social implications. The mobility capital (motility) affects social integration and can be considered an analytical tool that allows the analysis of the relationship between the dynamics of spatial and social mobility [27].

In small towns (up to 10,000 inhabitants) [28], active traffic is traditionally present; however, the construction of traffic areas intended for motor traffic, particularly for fast traffic, jeopardizes the safety of such a manner of movement while the areas intended for stationary traffic are often organized in very attractive urban locations. During spatial and traffic planning and reconstruction, small towns should be observed through spatial particularities of such urban spaces [29]; however, the potential for preserving and revitalizing healthy and environmentally friendly movement lies exactly in the smaller urban areas.

The first step of analysis when planning road infrastructure reconstruction is to identify existing traffic demand; however, it is mandatory to look at the reconstruction solutions through the criteria of future traffic demand. The application of microsimulation traffic modeling enables the analysis of alternative reconstruction solutions in different time and space ranges [30], simulating the behavior of all traffic participants. The advantage of microsimulation modeling is that it enables the analysis of a significant number of traffic and spatial indicators of alternative solutions in the planning phase, as well as the possibility to analyze the reconstruction impacts on a wider network coverage and with future traffic demand [31]. Microsimulations allow for the comparison of variable solutions through numerically expressed functional parameters—driving time, delays, queue parameters, etc., and dynamic parameters—speed of each individual vehicle, average speed, and other indicators related to traffic flow. Microsimulations have proven to be a valuable tool in traffic infrastructure analysis [5,6], but their potential has not been sufficiently exploited in a broader spatial–traffic analysis, as enabled by multi-criteria analysis methods.

Considering the numerous criteria based on which an optimal spatial and traffic solution should be selected, in this paper we analyze the possibility of using multi-criteria analysis combined with the use of VISSIM for traffic microsimulations in the procedure of preliminary analysis of possible developments of the traffic system. Multi-criteria analysis was applied to select the best alternative for the restoration of areas and the reconstruction of systems.

During spatial planning and design, the multi-criteria analysis method is used to optimize the selection of a solution for different types of problems, and the application to big-scale problems has shown its effectiveness [32,33]. Although a significant aspect of scientific interest is developing in the direction of selecting the appropriate optimization method for a particular observed problem, taking into account different MCA methods (including the fuzzy approach), and comparing their adequacy regarding the specifics of the problem analyzed (as suggested by [34,35]), when it comes to planning and designing traffic infrastructure in urban areas, the most commonly used procedure is the Analytic Hierarchy Process (AHP) multi-criteria analysis method [4,36]. Based on the review of multi-criteria analysis method applications in decision-making about transport infrastructure from 2003 to 2012 done by Deluka-Tibljaš et al. [4], in 70% of analyzed scientific papers the AHP method was applied. Other applied methods, in a much lower percentage, were ELECTRE (Elimination and (Et) Choice Translating Reality), PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations), SAW (Simple Additive Weighting), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), etc.

Broniewicz and Ogrodnik [37], in their review of global literature from 2010 to 2019 conducted on the subject of MCDM/MCDA (Multi-Criteria Decision Making/Multi-Criteria Decision Analysis) methods used in transportation infrastructure, proved that the most popular method used to solve multi-criteria decision problems in the field of transport is the AHP method, followed by TOPSIS, DEMATEL (Decision Making Trial and Evaluation Laboratory), and PROMETHEE and ELECTRE.

The AHP method enables the optimization of solutions already in the planning phase when alternative solutions, in general, are not elaborated in detail, because besides the usual multi-criteria assessment of every alternative regarding each criterion separately, it also provides the possibility of pair-wise comparison of alternatives in relation to a specific criterion [38].

Alemdar et al. [39] used microsimulation and MCA (AHP and TOPSIS) to select the optimal corridor design and analyzed different solutions for three intersections on the Vatan Street corridor in ˙Istanbul, Turkey. The evaluation criteria in the study were vehicle delay, queue length, stopped delay, stops, travel time, vehicle safety, CO emission, fuel consumption, and construction cost. In the study done by Bayrak and Bayata [40], three intersection types were modeled and tested using VISSIM software in line with parameters: capacity and level of service, travel time, average delay, queue length, fuel consumption, and vehicle exhaust emissions. Safety and construction costs were added. A new model was then generated using the VISSIM results and the AHP results. Du et al. [41] used a combination of VISSIM microsimulation and AHP methods to create a model to evaluate access management. Alemdar et al. [42] applied AHP and VIKOR methods to optimize the position of pedestrian crossings on the road corridor in a case study in Erzurum, Turkey. Microsimulations in VISSIM were applied in the assessment of the impact of individual scenarios of pedestrian movements on the traffic conditions of the observed coverage.

A review of the literature shows that in recent times the results of microsimulations have been used as input data of MCA methods (most often AHP methods). In doing so, functional indicators are predominantly used, with the addition of cost and safety parameters.

Functional indicators obtained by applying microsimulations represent one of the five groups of criteria on the basis of which alternative solutions are evaluated in this paper. Each group of criteria brings one specific aspect of the evaluation of the solution, and their importance was analyzed by different stakeholders. The addition of spatial–urban criteria introduces a set of qualitative parameters that are psychologically and socially oriented and focused on the subjective experience of different stakeholders. Involving stakeholders in the evaluation of alternative solutions is not a completely new approach, but, like the application of microsimulations, it is insufficiently used in the broader context of the evaluation of alternative solutions. In this paper, the MCA (AHP method) and microsimulation methods are combined in order to achieve sustainable traffic goals in making decisions for a small city area, respecting relevant criteria for socially sensitive design but also local conditions by including local inhabitants as relevant stakeholders.

#### **3. Study Area**

The town of Beliš´ce is an industrial town with around 10,000 inhabitants, is one of seven towns in Osijek-Baranja County, and covers a surface area of 68.75 km<sup>2</sup> . The geographical and traffic position of Beliš´ce makes this town the intersection of important regional road corridors.

The town of Beliš´ce is associated with a 130-year-long industrial tradition of the production and processing of packaging paper, which plays an important role in the economic development of the region. Since 1998, a hydraulic press factory for the production of car tires has been operating in the inner city center of Beliš´ce. The factory is predominantly export oriented and represents an important social and traffic pull factor. Industrial zones located near the town center (Figure 1) generate several major problems in the urban traffic network of the town of Beliš´ce. The insufficient hierarchical structure of the urban network brings freight traffic to the very periphery of the secondary network, which is a significant functional and traffic safety problem.

In addition, increased traffic of trucks and heavy freight vehicles cause economic problems by means of accelerated deterioration of the pavement structure and environmental problems such as noise and air pollution. The observed problem should be seen in the context that Beliš´ce does not offer public city passenger transport, except for a taxi

service, and the choice of modalities is reduced to a private car or taxi car and active forms of mobility.

**Figure 1.** Area use layout—industrial zones (blue) and town center (green) [43].

The reconstruction zone is a central city zone, which, with 86 existing parking spaces in the observed coverage, cannot meet the requirements of stationary traffic. The city center is an administrative center with a number of public facilities (post office, police, health center, pharmacy, museum) that generate the need for a greater dynamic of stationary traffic (stopping and short-term parking). Within the considered segment of the traffic network, there is also a residential zone with long-term parking requirements. The vicinity of the industrial zones puts an additional pressure to the infrastructure intended for stationary traffic and a large number of workers who come to work cannot park their cars near the factories (Figure 2).

**Figure 2.** Stationary traffic problems in Beliš´ce town center.

The wider analyzed coverage of the secondary network is shown in Figure 3 (left). The subject of reconstruction are the traffic areas of the secondary network, parking lots, and pedestrian paths shown in Figure 3 (right). The intersection on the access road marked in red is, according to the traffic count results, a critical point of the narrower secondary network reconstruction area, which is included in the model and analysis of functional characteristics of the alternative solutions.

**Figure 3.** Observed secondary network coverage (**left**) and narrower area of reconstruction (**right**).

Traffic was recorded and counted several times in May 2019 under standard conditions— Tuesdays and Wednesdays at peak hour, from 2 to 3 pm, and the mean value of the total monitored motor traffic network coverage expressed in the equivalent units of a personal car (PCU) distributed on the traffic routes is shown in Table 1. The access points (A1–A4) for both intersections are marked in Figure 3 for the intersection located at the periphery of the reconstruction zone (I1) and for the intersection within the reconstruction zone (I2). In the mixed flow with motor vehicles, there were cyclists who were not counted in equivalent units, to highlight the frequency of this form of mobility (Table 1). The same table contains the existing traffic demand for pedestrian movements.


#### **4. Materials and Methods**

To select the optimal traffic solution for Beliš´ce town center, the multi-criteria analysis (MCA) method was used in combination with the traffic micro-simulation method for preliminary screening of traffic development alternatives based on the expected traffic demand in the project period.

The MCA is applicable if a choice must be made between several solutions based on a larger number of criteria and different, both quantitative and qualitative, measures [44,45]. Although the problem of decision-making related to different infrastructure in urban areas is based largely on common principles, there are certain specifics for different types of infrastructure planning, so for a more detailed analysis, each infrastructure should be observed separately with the definition of specific criteria.

The selection of criteria in the multi-criteria analysis is a sensitive step in particular when quantitative and qualitative indicators are combined. A large number of criteria can be confusing for decision-makers and a small number of criteria does not provide an analysis of all relevant information [46]. The application of quantitative indicators contributes to the objectification of multi-criteria analysis, but the problem is that the selection of the optimal solution is done at the early project stages, when there is not enough detailed data, so the quantification of indicators is a challenging task. The criteria groups, criteria, and sub-criteria for the subject case study are explained in more detail in Section 4.1.

Significant support for decision-makers is provided by the tools of traffic modeling traffic micro-simulations, which are used to analyze and compare alternative reconstruction solutions. Modeling results are functional traffic indicators such as travel time, delays, length of the vehicle queue, number of stops, delays related to stopping, level of service, dynamic characteristics, etc. Within the modeling results, it is possible to obtain environmental indicators (emissions of harmful gases, etc.), as well as economic indicators expressed through fuel consumption. Keeping in mind that this is a microsimulation model, said traffic indicators can be obtained for each specific entity within the simulation (personal vehicle, public transport vehicle, truck, cyclist, pedestrian) and also as mean values of indicators for individual traffic flows or in total for the observed network coverage. At each second of the simulation, the model calculates the spatial positions and dynamic characteristics of each entity and their interactions defined through the input parameters of traffic regulation, priority rules, and characteristics of the behavior of traffic participants. The modeling results are quantitative indicators, and the advantage of the model lies in its high-quality graphic interpretation, so it is possible to make a movie in 3D mode on the operationalization of the observed spatial solution, which enables a realistic insight of the spatial solution for decision-makers and the general public.

Due to the requirements of the analysis of alternative solutions (alternatives), the mean values of traffic indicators were analyzed, which provided a sufficiently realistic insight into the quality of the alternatives for the purposes of their comparison and evaluation. The calibration of the VISSIM model was not done specifically for Beliš´ce, but the application of the VISSIM model on roundabouts in the city of Osijek, Croatia, made a difference between modeled and measured data in situ: around 10% for the uncalibrated and less than 5% for the calibrated model [47]. The town of Beliš´ce is in the immediate vicinity of the city of Osijek, so the input parameters of the VISSIM model associated with driver behavior were shown to be applicable. The selected functional indicators for this analysis level were total delays, maximum queue length, number of stops, delays caused by stopping, and level of service (LOS). Functional indicators were analyzed for the existing traffic demand (determined by the traffic counting) and for the various scenarios of future (presumed) traffic demand, which includes an increase in the traffic load of vehicles, cyclists, and pedestrians. A larger offer of parking spaces, cycling paths, and a well-maintained pedestrian promenade are the pull-factors of the traffic load that were taken into account when evaluating the alternatives of reconstruction.

Traffic is stochastic by nature, so in order to get the modeling results to be as realistic as possible, 10 different driving scenarios of vehicle arrivals were analyzed, and the considered traffic indicators are the average values of functional indicators of each analyzed traffic scenario. The initial value of the random number generator (random seed) was a default value and the set increment was 10. The same 10 traffic scenarios were analyzed for the current state and for each alternative solution of reconstruction in order to ensure comparable simulation results.

Within this study, the microsimulation traffic models for the secondary road network for the existing situation (solution zero—A0) and for alternative reconstruction solutions (A1, A2, A3) were developed in VISSIM. The aim of the application of the microsimulation traffic models was to analyze alternative reconstruction solutions and to evaluate them

based on the available simulation results according to the selected functional criteria, which were incorporated into the procedure of the multi-criteria analysis of alternatives.

In addition to functional indicators, the simulation results provided information regarding an environmental criterion—the emissions of harmful gases, and an economic criterion—user costs through fuel consumption, which was included in the evaluation through the defined criteria. The results of the microsimulation were sufficiently accurate as relative indicators for the purpose of comparing alternatives, but we could not use them as absolute values, so more sensitive models with more spatial input data [48] should be used to model air pollution.

Multi-criteria optimization methods are applied to ill-structured problems, including those related to planning and designing traffic infrastructure. Ill-structured problems are those with very complex objectives, often vaguely formulated, with many uncertainties, and the nature of the observed problem gradually changes during the process of problem solving [49]. The results of ill-structured problems are different dimensions' criteria for the evaluation of solutions and variable constraints.

The alternative solutions in the case study were analyzed using the AHP multi-criteria analysis methodology [7,44,46,49].

AHP is a priority method applicable to problems that can be represented by a hierarchical structure [7]. The top of the hierarchy is represented with the goal, one level lower are criteria, with more levels of (sub)criteria possible, while the lowest level is represented by alternatives.

The basic methodological steps applied in this research are shown in Figure 4.

The AHP method is based on estimating relative priorities (weights) of criteria and alternatives on which a pair-wise comparison matrix for criteria and pair-wise comparison matrices for alternatives (one matrix for each criterion) are generated. The pair-wise comparison of alternatives regarding their importance with respect to each criterion or pair-wise comparison of criteria respect to the goal is done using the pair-wise comparison scale shown in Table 2.


**Table 2.** AHP pair-wise comparison scale [7].

The result of the AHP method is the overall priority vector that defines the priority (weight/importance) of each alternative with respect to the goal so the ranking of alternatives can be made.

The advantage of this multi-criteria method is that it can be used when just the pairwise comparison of alternatives according to each criterion and the pair-wise comparison of criteria towards the goal are known, but also when the alternatives are exactly valued in regard to each criterion separately—that is, if the importance of every single criterion is exactly defined, but it also gives the possibility to combine these two approaches in the same analysis. This was applied in our research.

In this case study, the application of the AHP methodology was based on a total of 5 criteria groups: functional criteria, traffic safety criteria, economic criteria, environmental criteria, and spatial–urban criteria. Each alternative was evaluated according to each criterion and sub-criterion defined within the specified criterion group. Within each of these groups, part of the (sub)criteria were defined by measurable values except in a group of spatial–urban criteria assessed based on the collected subjective evaluations

of criteria given by different categories of stakeholders—local population, experts, and students—whereas economic criteria of construction and maintenance costs were analyzed by alternative pair-wise comparison.

**Figure 4.** The flow diagram of the basic methodology steps.

#### *4.1. Problem Definition*

In the process of spatial and traffic planning, the definition of solutions starts with a clear identification and definition of the problem by defining the basic elements needed for the implementation of the procedure: expected outputs—goals, required input data, definition of expected limitations, and criteria according to which the alternative solutions will be evaluated.

The goal of the project was to meet the traffic demand of different traffic users in Beliš´ce town center, according to the principles of sustainable urban mobility. The data collected from field research and from the existing documentation related to the coverage area served as a basis for the development of alternative solutions [50].

The selected criteria for the assessment of the alternatives using MCA are briefly explained below:

#### 4.1.1. Functional Criteria—F

The functional criteria were used to evaluate the extent to which a particular alternative solution meets the traffic multimodal requirements of certain categories of users, as visible from the selected criteria and sub-criteria. The requirements and quality of traffic flow needed to be analyzed for the current and future traffic load, which, by analyzing different scenarios for increasing traffic load, would be evaluated as critical. Traffic requirements of cycling and pedestrian traffic as well as of stationary traffic were analyzed. The traffic conditions and the interaction of multimodal traffic in conflict zones was evaluated through five of the functional criteria for future traffic demand.

F1—functional traffic criteria of motor vehicles and integrated flows and traffic interactions for a critical traffic scenario of future traffic demand, obtained as a result of the application of traffic microsimulations in VISSIM.

F11—the maximum queue length (m) is the longest line that appears within the traffic simulation and the traffic conditions of the peak load are simulated for 3600 s, i.e., 1 h. F12—total mean delays per vehicle (sec/veh) are time losses caused by all influential parameters, such as traffic load, traffic structure, type of conflict flows, traffic regulation, reaction time of traffic participants, dynamic conditions of each entity (driving speed, acceleration, deceleration, pedestrian speed), safety clearance, the influence of infrastructure elements, etc.

F13—the average number of stops of each vehicle (number) in the traffic flow caused by traffic conditions, traffic regulation, conflict flows, parking/unparking, etc.

F14—the average delays caused by stopping per vehicle (sec/veh) are a measure of the complexity of individual traffic situations and interactions, because there may be traffic scenarios in which there are more short stops, or traffic scenarios in which there are fewer stops, but traffic circumstances are complex and stops last longer.

F15—the level of service (LOS) demonstrated categorically from A to F is a qualitative indicator of traffic conditions and is ranked in six levels, where the conditions of traffic flow of level A are the best and consistent with the movement of vehicles in free flow, and level F practically means standing or very slow forced movement in a line of vehicles. The basis for evaluation of the level of service is the user-oriented parameter expressed through the mean delays, unlike the previously used theoretical criteria of reserve capacity.

F2—functional traffic criterion of bicycle traffic expressed through the length of bicycle paths (m).

F3—functional traffic criterion of pedestrian traffic expressed through the length of pedestrian infrastructure (m).

F4—functional traffic criterion for stationary traffic expressed through the number of parking spaces.

F2, F3, and F4 are defined in the project for alternative solutions (Section 4.2).

#### 4.1.2. Safety Criteria—S

Direct traffic safety indicators such as the number of traffic accidents and the number of severe traffic accidents (with a dead or seriously injured person) could be analyzed only for the existing traffic solution (A0), so the safety criteria were analyzed through indirect indicators.

S1—speed (km/h) is correlated with the number of traffic accidents and is highly correlated with outcomes, i.e., the severity of traffic accidents, especially in the vehicle–pedestrian interaction. The increase in speed from 30 km/h to 50 km/h increases the likelihood of fatal and severe outcomes for pedestrians from the range of 5–22% to the range of 45–85% [51]. The mean speed is obtained by applying the traffic microsimulations in VISSIM.

S2—the degree of segregation (expressed through the number of separated traffic flows) is an indicator of how many traffic flows have separate areas for movement. The pedestrian flows are the last to be integrated into the common traffic area, and this must be hierarchi-

cally (secondary network, access street), safety-wise (vehicle speeds adjusted to pedestrian walking speed), and functionally (low traffic load) justified.

S3—the number of potential conflict points (number) of opposing vehicle–vehicle traffic flows.

S4—the number of potential conflict points (number) of opposing vehicle–pedestrian traffic flows.

S2, S3, and S4 are assessed from the project for alternative solutions (Section 4.2).

#### 4.1.3. Economic Criteria—EC

The economic criteria are usually assessed through the cost of construction, maintenance, the value of the facility at the beginning and at the end of the planned period, the direct and indirect costs of users, etc. Due to the level of project documentation, the cost of construction and maintenance costs could not be expressed in numerical terms, but were analyzed by comparing the solution pairs as one of the options, i.e., benefits of the AHP methodology.

EC1—construction cost–pair-wise comparison.

EC11—reconstructed area in m<sup>2</sup> .

EC12—use of modern technologies (camera/displays with data about the number of available parking spaces)

EC2—maintenance cost–pair-wise comparison.

EC3—fuel consumption (US gal lqd) for a critical traffic scenario of future demand, obtained as a result of micro-simulations in VISSIM.

#### 4.1.4. Environmental Criteria—EN

Environmental criteria were assessed through the quantity of exhaust gases in grams for a critical scenario of future traffic demand, and were derived from the results of microsimulations in VISSIM. The reported numerical indicators could not be used as an absolute, but rather as relative indicators for analysis and comparison of alternative solutions.

EN1—carbon monoxide (CO) emission in grams. EN2—nitrogen oxide emission (NOx) in grams.

EN3—volatile organic compounds (VOC) in grams.

4.1.5. Spatial–Urban Criteria—SU

The spatial–urban criteria are qualitative indicators expressed through the selected criteria describing the attractiveness of the solution and the spatial potential. These qualitative indicators include a subjective experience, and in this study, we analyzed subjective evaluations of performance of alternative solutions according to the criteria expressed in surveys by different target groups.

SU1—walkability potential and spatial motivation for pedestrian movement.

SU2—potential and spatial motivation for cycling.

SU3—attractiveness.

SU4—potential for social interactions.

SU5—assessment of a sense of comfort.

SU6—assessment of a sense of safety for the most vulnerable traffic groups.

SU7—parking policy—adequate attitude toward a stationary traffic solution (how much space we agree to spend on parking lots).

The importance of the criteria for evaluation of alternative solutions, i.e., the weight coefficients of individual criteria, reflect the preferences of the decision-makers and have an impact on the optimization process and on the final outcome of the alternative solution selection. Within this study, three groups of stakeholders were involved in the survey. They analyzed the relevance of criteria and gave their evaluation of alternative solutions in

relation to qualitative criteria (SU). The similarities and differences of the relevance of the criteria between different stakeholder groups were statistically analyzed.

#### *4.2. Case Study Description—Alternative Reconstruction Solutions/Alternatives*

As input parameters for the design of a possible manner of reconstruction of the traffic network, data on movement for all types of traffic and data on existing traffic infrastructure as well as on all land uses in the wider coverage zone (Section 3) were collected.

Three alternative solutions (A1, A2, A3) for the reconstruction of traffic areas in the center of Beliš´ce were developed and further analyzed, and were conceptually different according to the solution of all relevant elements of traffic demand—pedestrian traffic, cycling traffic, motor vehicle traffic, and stationary traffic. All three alternatives were designed to preserve the three large chestnut trees that are a symbol of the town and are within the reconstruction coverage. The comparison of alternatives [50] is given in Table 3.

**Table 3.** Comparison of proposed alternatives.

#### Alternative A1: PEDESTRIAN STREET

Construction of a parking lot and of a pedestrian promenade and repurposing of the road into access to the parking lot using modern technological solutions—cameras in parking lots and a display with the number of free parking spaces on each access road in the wider coverage of the secondary network. Cycling traffic is in the mixed flow together with vehicles, but due to low speeds and elimination of the vehicles that are entering the parking zone inefficiently, the traffic conditions are better.

Reconstructed area: 1630 m<sup>2</sup> Parking places: 109 (20 new) New pedestrian paths: 130 m New bicycle paths: 0 m Intersection: three-leg

#### Alternative A2: SHARED SPACE

Concept with full integration of traffic flows on a common surface designed to meet the needs of pedestrian and cycling movements, with fewer parking spaces than the existing solution, in order to influence the selection of active modalities of urban mobility and demotivate the choice of personal cars as the primary modality. An addition to the solution is the construction of a network of bicycle paths in the coverage area that provides greater safety to bicycle flows.

#### **Table 3.** *Cont.*

Reconstructed area: 3880 m<sup>2</sup> Parking places: 48 (38 fewer) New pedestrian paths: 160 m New bicycle paths: 535 m Intersection: four-leg

#### Alternative A3: TRAFFIC-CALMING ZONE

Reconstruction of the existing collector road leading to the inner city center, in a reduced speed zone ("30 zone"), with 31 new longitudinal parking spaces. The existing traffic areas for pedestrians and cyclists, along with the areas intended for stationary traffic (82 parking spaces), remain the same.

Reconstructed area: 1150 m<sup>2</sup> Parking places: 116 (31 new) New pedestrian paths: 145 m New bicycle paths: 210 m Intersection: four-leg

#### *4.3. Formation of Traffic Models*

For the analysis of functional indicators, microsimulation traffic models for the existing situation (A0) and for the considered alternative solutions (A1, A2, A3) were formed. Traffic load and traffic distribution data for existing traffic demand were entered into the models, after which a number of what-if scenarios of traffic load increase were analyzed in order to verify whether alternative reconstruction solutions could meet functional traffic demand requirements in the future. The input data of the model are the dynamic characteristics of the vehicles, speed, acceleration, deceleration, type, and engine power of the individual vehicles, as well as characteristics associated with driver behavior, such as reaction time, frequency, and length of scanning traffic situations, etc. The model approaches the stochastic nature of the traffic flow by using a random number generator and distributions, and the entered data, for example for the speed of individual vehicle categories, are used in the model as the median of the speed distribution. Of the 10 different traffic scenarios of the arrival of vehicles within the same traffic load, the mean values of functional and other traffic indicators were analyzed.

#### **5. Results and Discussion**

#### *5.1. Microsimulation Results*

The formed microsimulation traffic models for the existing traffic solution and all alternative reconstruction solutions enabled the analysis of different traffic loads and traffic structures. The existing traffic requirements were the first scenario analyzed, but the future traffic requirements were adopted as a relevant traffic load. The projection of the future traffic load is based on economic growth and development of industrial production, for which there is a significant market demand and which would also generate an increase in traffic demand. Scenarios for increasing traffic demand of 50, 75, and 100% for all modes of traffic and in combination were analyzed and a critical scenario was found to be a 100% increase in motor vehicle traffic, without a significant impact on functional indicators of different increases in other modes of traffic. A comparison of functional indicators for a critical traffic scenario for the future traffic load is shown in the diagram in Figure 5. The maximum queue length is expressed in meters; the total delays of vehicles are reported as the mean value in seconds per vehicle. The average number of stops is expressed as the number of stops per vehicle and the delays caused by the stops are expressed in seconds per vehicle. Level of service is a qualitative indicator of the operating conditions of the traffic that is user-oriented and related to delays.

**Figure 5.** Comparison of functional indicators for critical traffic load. A0—existing solution, A1–A3 alternative reconstruction solutions.

The simulation results show that all alternative solutions could meet the presumed increase in traffic with a satisfactory level 3 (or C) of service, with the exception of A3, which showed a greater sensitivity to traffic increase due to frequent parking and unparking maneuvers causing higher time losses and level 4 (or D) of service). Service level D is considered an acceptable level in the conditions of the secondary urban traffic network. The alternative solution with shared space (A2) proved to be sensitive to an increase in traffic according to the criterion of the maximum queue, but this solution is designed to demotivate the use of personal vehicles as the primary form of mobility, as per the spatial concept and the number of available parking spaces. However, in order for the alternative solution to be comparable, it was analyzed for the same increase in traffic and pointless entry into the share-space zone, without the possibility of parking the vehicle.

Figure 6 shows the simulation results of carbon monoxide (CO), nitrogen oxides (NOx), and volatile organic compounds (VOC) for the existing traffic load Figure 6a and for future traffic demand in a critical scenario Figure 6b.

The results clearly show the difference between the alternative reconstruction solutions in conditions of future demand, which justifies the use of microsimulation traffic modeling in the evaluation of alternative solutions.

#### *5.2. Results of the Analysis of Qualitative Spatial–Urban Criteria*

An online survey involving 120 participants formed of different groups of stakeholders was used to assess the quantitative spatial–urban criteria. They were presented with the current condition of the traffic areas of the town of Beliš´ce in the coverage area of a secondary traffic network and three alternative reconstruction solutions. Three groups of 40 participants were surveyed, including experts, students, and citizens. The experts were mostly members of the academic community, and the students included in the survey were equally divided among students in their final years of study in civil engineering and architecture and urbanism. Citizens who participated in the survey were selected by a random sample of residents of Beliš´ce. All respondents were over 20 years of age, and all surveyed students were between 20 and 30 years of age. The gender and age structure of the respondents is shown in Table 4.

**Table 4.** Gender and age structure of respondents.


Seven spatial–urban criteria were selected that are applicable to a specific problem and arose from the analysis of research and urban studies [8,9,20].

Respondents subjectively evaluated the existing situation and each reconstruction solution with scores from 1 to 5 according to seven spatial–urban criteria (Table 5). Score 1means that the solution does not meet the spatial criteria at all, and score 5 means that the solution fully meets the spatial criteria.

The subjective evaluation of all groups of respondents was that according to the spatial– urban criterion, the existing solution is the worst, and the best solution is the alternative with a pedestrian promenade and separate parking (A1) according to all criteria except the cycling potential. The shared space alternative (A2) received high scores for cycling potential, walkability, attractiveness, and social interactions, but experts and students gave it a poor score for parking policy. What is interesting is that the citizens gave this solution a good score for the parking policy and recognized the potential of this solution in promoting

active forms of mobility while limiting parking in the zone. Experts were skeptical about this solution, because the successful application of such a solution is related to a shift in the paradigm of personal mobility in people's minds, and the experience shows that it is a longer process than the implementation of the solution. Ultimately, it often happens that such solutions in the local environment do not contribute to the motivation of active forms of mobility to the expected extent, but to the relocation of stationary traffic problems to another, close location. It is interesting that the solution of longitudinal parking in the collector street (A3), which offers the largest increase in the number of parking spaces, was not the best rated solution according to the criteria of parking policy in any group of respondents. The fact that alternative A3 was the worst rated solution according to the criterion of feeling of traffic safety in all three groups of respondents speaks in favor of the fact that respondents rated longitudinal parking in the road profile as a functionally and safety-wise worse solution than the others. Alternative solution A3 was also rated the worst by the criterion of social interactions, because it does not enrich the space with areas that people would use for mutual encounters, although it effectively increases the number of parking spaces in the coverage area.


**Table 5.** Subjective evaluation of the alternative solution according to spatial–urban criteria.

#### *5.3. Application of the AHP Method*

Table 6 shows criteria groups, criteria, and sub-criteria (explained in detail in Section 4.1) with numerical values derived from design solutions or obtained as a result of microsimulation traffic modeling, and for spatial–urban criteria they are obtained as a result of the subjective evaluation of respondents, with scores from 1 to 5.


#### **Table 6.** Criteria and sub-criteria.

A0—existing solution, A1–A3—alternative reconstruction solutions. <sup>1</sup> Potential and motivation for walking. <sup>2</sup> Potential and motivation for cycling. <sup>3</sup> Does not apply to the number of parking spaces, but rather to adequate/efficient planning of space intended for stationary traffic.

> Economic criteria, construction, and maintenance costs due to the level of project documentation were analyzed by alternatives pair-wise comparison based on the reconstruction area and the application of advanced technological solutions according to the data from Table 6.

> The pair-wise comparison of alternative solutions in regard to economic criteria, construction, and maintenance cost is shown in Table 7 using the AHP pair-wise comparison scale shown in Table 2.

**Table 7.** Pair-wise comparison for construction and maintenance costs.


The consistency of pair-wise comparison matrices of alternatives, criteria, and also of overall priority matrix should be analyzed by calculating the inconsistency index. The inconsistency index should be lower than 0.1, in which case the evaluations are consistent [7]. This was the case of pair-wise comparison of alternatives for EC11, EC12, and EC2 (Table 7).

The hierarchy of criteria groups, criteria, and sub-criteria is shown in Figure 7.

**Figure 7.** The AHP hierarchy of the problem (top left is the goal; on the bottom right are the alternatives).

The Analysis of Preferences

The evaluation of the importance of five selected criteria groups applied in the AHP method was included in a survey in which three groups of respondents participated (experts, students, citizens), and the total database contains the results of the evaluation of 120 respondents. Table 8 shows the criteria scores as evaluated by individual groups of respondents, but also the scores obtained on the entire database. Criteria scores ranked from 1 to 10, with score 1 meaning the criteria are not at all relevant, score 5 meaning the criteria are neither relevant nor irrelevant, and score 10 meaning the criteria are very relevant.


**Table 8.** Mean values of marks and rank of criteria (criteria groups).

Table 8 shows that none of the criteria were evaluated as irrelevant, and all mean scores of criteria groups had values greater than 7. By analyzing the mean values of each criteria group, the highest mean score is assigned to the safety criteria in total and by the citizens. Experts and students gave a slightly higher score to the functional criteria, but the citizens put the functional criteria in third place. Economic criteria were assigned with the lowest scores by all groups, and the lowest mean score was assigned to them by the citizens. The environmental criteria were evaluated by experts and students as third by relevance of the criteria, but citizens thought they were in fourth place. Spatial–urban criteria were the second most important to citizens, after the safety criteria, and other groups rated these criteria as the fourth most relevant.

The basic statistical indicators of the database for each group of respondents are shown in Table 9.


**Table 9.** Basic statistical indicators.

According to the results from Table 9, the evaluations of economic and environmental criteria had the largest standard deviations and the largest range.

According to the Anderson–Darling test for all groups of respondents and all evaluation criteria, the data did not follow a normal distribution. Non-parametric Bonett and Levene tests were used to evaluate the preferences of individual groups of respondents and to analyze the relationship between variance and standard deviations [52,53]. The null hypothesis is that there is no statistically significant difference between groups of respondents (σ(G1)/σ(G2) = 1) and set significance level α = 0.05. The results are shown in Table 10.

**Table 10.** Comparison of preferences of individual groups of respondents—statistical analysis.


Statistically significant differences between the preferences of individual groups of respondents are given in Table 10 (bold). There were statistically significant differences in preferences for functional criteria group between experts and citizens and students and citizens, but there was no statistically significant difference in evaluations between experts and students, which was the expected result. The result clearly shows that experts

considered functional criteria to be as equally important as safety criteria, whereas citizens assigned them a slightly lower score. The economic criteria were best evaluated by students and their score was statistically significantly different from the other groups of respondents. There was no statistically significant difference between experts and citizens in the ranking of the economic criteria group. All groups of respondents gave high scores to the safety criteria. There was no statistically significant difference in preferences between individual groups of respondents for safety and environmental criteria. For spatial–urban criteria, there was a statistically significant difference among preferences between experts and citizens, and a comparison of evaluations of other groups did not show statistical significance in the difference in scores. This result clearly shows that the spatial potential of the solution that contains social and psychological components of the evaluation was important to the citizens and that they gave it more importance than the profession that preferred functional criteria.

The inclusion of preferences in the AHP method was done through the analysis of five different scenarios with different weighting coefficients, thus leading to a sensitivity analysis. The weights were assigned only to criteria groups, not to lower levels of criteria. A description of individual scenarios is shown in Table 11.

**Table 11.** Assignment of different preferences—analysis of 5 scenarios.


The ranking results according to the above scenarios are shown in Table 12.


**Table 12.** The results of ranking according to the analyzed scenarios.

Results of the AHP analysis for Scenarios 1 and 2 are shown in Figures 8 and 9.

**Figure 8.** The AHP analysis for Scenario 1.

**Figure 9.** The AHP analysis for Scenario 2.

The results of the AHP analysis for the preferences of individual groups of respondents are shown in Figure 10a for experts, Figure 10b for students, and Figure 10c for citizens of Beliš´ce.

**Figure 10.** The AHP analysis for Scenarios 3 (**a**), Scenarios 4 (**b**) and Scenarios 5 (**c**).

Regardless of the selected scenario and preferences, A1 stands out as the best alternative solution for reconstruction, followed by A3, A0, and A2 as the worst. The overall priority vector values for alternatives and selected scenarios ranged for A1 from 0.325 to 0.330, for A0 from 0.217 to 0.227, and for A2 from 0.125 to 0.181, while for A3 the value was the same at 0.273. It can be noticed that the introduction of preferences (weights of criteria groups) in Scenario 2 resulted in minor changes in the overall priority vector values for A0 and A2, whereas for A1 this difference was extremely small. The difference between Scenarios 2, 3, and 4 was also negligible, whereas for Scenario 5 A1 had the highest overall

priority vector values, and the difference between the overall priority vector values for A0 and A2 was the lowest.

In order to avoid the rank reversal phenomenon, the "ideal mode" AHP was used to rank alternatives according to all scenarios [54]. Then the ranking results were tested by decomposing the initial problem into sub-problems. First the alternatives were compared two at a time and then based on removing one alternative at a time from the whole group [55,56]. The results show that in all cases the ranking remained the same.

All groups pointed out functional criteria (experts, students) or traffic safety criteria (citizens) as the most important two groups of criteria, which, taking into account different scenarios, is best reconciled by alternative A1. Alternative A1 is based on the well-known concept of the pedestrian zone, which contributes to the quality of pedestrian traffic and space in general. At the same time, the solution for motor traffic (including stationary) does not jeopardize the standard of motor traffic because it offers even more parking spaces than those that already exist. A more innovative concept of the A2 alternative—shared space, is, perhaps as expected, a less desirable solution because it raises the issue of traffic safety in the area shared by all road users, and also significantly reduces the number of parking spaces in the zone. This is something to which the tenants living in this zone were particularly sensitive. Alternative A3—calm traffic, as estimated, does not make visible improvements to existing spatial–traffic conditions, as it continues to favor motor traffic by increasing the total number of parking spaces in the road profile. Longitudinal parking in the road profile was rated by all groups of respondents as worse in terms of the feeling of safety, and even worse than the shared space (A2) alternative solution.

#### **6. Conclusions**

Key decisions about traffic infrastructure reconstruction, such as choosing an alternative solution that has direct implications on the quality of the planned reconstruction, are made in the early design stages when the level of project detail is such that decisions are traditionally based on the evaluation and experience of the designers. In order to prevent subjectivity in selecting the optimal solution, the application of microsimulation traffic modeling is a logical choice, as it allows a numerically based analysis of traffic conditions for planned alternative reconstruction solutions for different spatial and timerelated ranges. The traditional approach of selecting the best solution, along with the analysis of traffic conditions, is based on the cost estimation of solution implementation. This approach is especially present when it comes to spatial and traffic interventions in smaller urban environments. On the other hand, sustainable and socially sensitive traffic planning must take into account a wider range of criteria as well as the subjective criteria of different stakeholders involved in the planning process, and equally important of future users—citizens—so this pointed in the direction of MCA application.

In this paper, we presented the results of the application of the AHP multi-criteria analysis method, which is the most common method in traffic analysis, to the selection of the optimal solution for the reconstruction of a segment of the secondary traffic network in the center of the small town of Beliš´ce in Croatia.

Traffic microsimulations were used to define a critical scenario for future traffic demand before applying the MCA. However, in order to choose among three alternative solutions, other than functional, we defined four more criteria groups with a greater number of criteria on lower levels. The AHP methodology use enabled us to combine different manners of assessing the criteria in the next step: numerically expressed sub-criteria obtained from microsimulation; pair-wise comparison of alternatives in relation to a specific criterion, which was used to evaluate economic criteria, since at this stage it was not possible to exactly define the costs of construction and maintenance; and assessment of preferences of the criteria of the various stakeholders involved in the process, which was used to evaluate the spatial–urban criteria.

The AHP analysis was performed according to the preferences of each of the groups involved—experts, citizens, and students. The sensitivity analysis for selecting the optimal reconstruction solution for all combinations of the weight coefficients of the criteria groups ultimately gave the same ranking order of alternative reconstruction solutions, although statistically significant differences were shown for the relevance of the evaluation criteria themselves for different groups of respondents. The ranking was also analyzed for the rank reversal phenomenon, which did not occur in this case.

Experts and students highlighted functional criteria as the most important criteria, and citizens highlighted traffic safety criteria as most important, which was expected. The next most important criteria for citizens were spatial–urban criteria, which they ranked with a very high weight, which indicates their interest in the manner in which they will use their everyday space in the future and the justification for their active involvement in this analysis with the aim of information and education.

Compared to existing studies, this research brings a methodological contribution through a holistic approach to the evaluation of alternative solutions, which in addition to quantitative introduces socially oriented qualitative parameters and involves stakeholders in the decision-making process.

The described methodology of the application of MCA in combination with traffic micro-simulations in spatial–traffic planning, although applied to problems of larger spatial coverage, proved to be justified when applied in a smaller urban settlement. Alternative A1, which was chosen as optimal, best reconciles the requirements of future traffic demand with environmental impacts and the expectations of future users related to traffic safety and the quality of space.

The methodology for selecting the optimal reconstruction solution presented in this paper is applicable to different segments of the urban traffic network of larger and smaller cities and should be validated in future research.

A potential challenge for the future is the application of the developed methodology to a wider urban area with more complex traffic and spatial situations. A potential issue is whether the large area should be analyzed as one zone or divided into smaller network segments. As for the traffic microsimulation, one larger zone would give a better understanding of the future functionality of the network and the implication of the reconstruction to the whole area. The partialization of large spatial coverage is justified in terms of traffic, due to the temporary regulation of traffic, and also in terms of construction, due to the dynamic plan of reconstruction work.

In larger urban areas, real potential users should be taken into account and a representative sample of citizens should be included in the evaluation of alternative solutions. In the continuation of this research, the influence of different weight factors on the lower-level criteria on the final result of the selection of the optimal alternative should be analyzed, and the needs of vulnerable traffic users, such as children and the elderly and people with disabilities, should be analyzed in the evaluation of alternative reconstruction solutions, too.

The selected MCA that was applied in this research was AHP, so further research should take into account other MCA methods, including the fuzzy approach, and compare their adequacy regarding the specifics of the problem analyzed.

**Author Contributions:** Conceptualization, I.I.O., B.K., A.D.-T., S.Š. and M.M.; methodology, I.I.O., B.K. and A.D.-T.; software, I.I.O., B.K. and M.M.; validation, B.K., A.D.-T. and S.Š.; formal analysis, I.I.O., B.K. and M.M.; investigation, I.I.O., B.K., A.D.-T., S.Š. and M.M.; data curation, I.I.O., B.K. and M.M.; writing—original draft preparation, I.I.O., B.K. and A.D.-T.; writing—review and editing, A.D.-T. and S.Š.; visualization, I.I.O., B.K. and M.M.; supervision, I.I.O., B.K. and A.D.-T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was co-funded by the University of Rijeka projects: "Transport infrastructure in the function of sustainable mobility" (uniri-tehnic-18-143) and "Implementation of innovative methodologies, approaches and tools for sustainable river basin management" (uniri-tehnic-18-129).

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Faculty of Civil Engineering and Architecture Osijek Ethics Committee, Approval number: 003-06/21-05/04 from 5 February 2021.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Transport Accessibility in a Suburban Zone and Its Influence on the Local Real Estate Market: A Case Study of the Olsztyn Functional Urban Area (Poland)**

**Agnieszka Szczepa ´nska**

Department of Socio-Economic Geography, Institute of Spatial Management and Geography, Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, Prawoche ´nskiego 15, 10-724 Olsztyn, Poland; aszczep@uwm.edu.pl

**Abstract:** The development of real estate markets in the vicinity of cities is linked with suburbanization processes. The migration of the population to suburban areas contributes to the growth of the residential property market (houses, apartments and construction plots). To minimize commuting costs, property buyers opt for locations that are situated close to the urban core. This article analyzes construction plots on the local real estate market in the Olsztyn Functional Urban Area, in terms of their temporal accessibility and demographic changes. Spatial variations in population distribution were analyzed with the use of the Gini index and geostatistical interpolation techniques. Spearman's rank correlation coefficient was calculated to determine the relationships between the analyzed variables. The study revealed differences in the spatial distribution of the population and real estate transactions as well as strong correlations between average transaction price, number of transactions, commuting time and population. The highest number of transactions were observed in cadastral districts situated in the direct vicinity of Olsztyn's administrative boundaries and the major transportation routes due to their high temporal accessibility.

**Keywords:** transport accessibility; real estate market; population; concentration

#### **1. Introduction**

Suburbanization leads to the sprawl of residential areas in the vicinity of an urban core. This process affects the morphology of suburban areas, their functions and the development of suburban infrastructure. The migration of the population to suburban areas contributes to the formation of settlement subsystems around the city center. This process stimulates the growth of local real estate markets, in particular, the number of transactions involving construction plots. City residents migrating to suburbs base their purchasing decisions on a property's location relative to the urban core. Real estate in the city is swapped for much cheaper and more attractively located properties in a suburban zone. Lower prices and a cleaner environment are the main reasons why city dwellers seek property in suburban areas.

Commuting time strongly influences the decisions made by the buyers of suburban property [1,2]. In suburban areas, the share of space intended for residential, commercial and industrial purposes are increasing [3]. The urbanization of rural areas surrounding the city is closely linked with distance to the functional urban core [3] and the availability of transport infrastructure [4–7]. Feedback mechanisms between accessibility, land-use and travel behavior were observed [8]. Buyers search for property in areas with well-developed road networks. Dwellers who migrate to suburbs continue to maintain close links with the city, and commuting entails high costs [9,10]. The cost and time of travel to work [11,12], school or commercial outlets is lower in suburban areas that are easily accessed from the urban core [13,14]. Daily commuting is the consequence of population shift from cities to suburban areas, which increases the demand for locations [15,16] with well-developed transport infrastructure [17–19]. Property buyers hope to maximize benefits and minimize

**Citation:** Szczepa ´nska, A. Transport Accessibility in a Suburban Zone and Its Influence on the Local Real Estate Market: A Case Study of the Olsztyn Functional Urban Area (Poland). *Land* **2021**, *10*, 465. https://doi.org/ 10.3390/land10050465

Academic Editor: Luca Salvati

Received: 11 April 2021 Accepted: 23 April 2021 Published: 29 April 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

costs by searching for a compromise between the price and quality of property and its distance from the urban core. They strive to minimize both the physical and temporal distance between the place of residence and the city. The above increases the demand for locations characterized by shorter commuting time and lower transportation costs [20–22].

Commuting increases road traffic because passenger vehicles are the main mode of transport for suburban residents [23] since public transport options are limited and do not always cover local transport needs. Daily commuting costs are a significant item in household budgets, but they are compensated by lower property prices in suburban areas. It has been said that the expenditures associated with daily travel are a different form of mortgage carrying costs. Time is also a factor that has specific economic value because it is a limited resource; therefore, commuting time, rather than distance, is the key determinant of transport accessibility [24]. Research studies conducted at various levels have also demonstrated that transport accessibility can influence property prices [25–27]. The balance between transport accessibility and the remaining attributes of real estate in rural areas (specific locations) have also been examined in suburban areas in Poland [28,29]. Therefore, the intensity and the spatial extent of suburbanization are inversely proportional to the demand for real estate, and the demand for and supply of construction plots influences real estate prices and the development of real estate markets in the suburban zone [30]. The demand for construction plots shifts the boundaries of the urban real estate market outwards [31–33]. The intensity and trends of suburbanization are reflected on the suburban real estate market and the accompanying changes in land use, population structure and investments. These processes are mutually interdependent, and they contribute to the formation of the urbanization cycle (Figure 1).

**Figure 1.** The urbanization cycle. Source: [34].

The suburban real estate market is characterized by a high and growing number of transactions involving vacant land. The supply of and the demand for construction plots continues to increase, which increases property prices. The prices of residential property are influenced by numerous factors, but the prices of suburban property are determined mainly by the size of the town or village and its distance from the urban core. Land prices generally decrease with an increase in distance from the city [35–37], and the development of transport infrastructure drives urbanization and home prices [36,38–40]. This observation is consistent with the new urban economics concept, which postulates

that the prices of residential property are influenced by distance from the city core (land prices decrease as the distance from the city increases).

Urban expansion is thus directly linked to the growth of the real estate market, and real estate prices are determined by numerous factors. Deng et al. [41] proposed the following formula to illustrate differences in urban expansion as a function of economic and geophysical factors:

Urban land area = f (income growth, demographic shifts, agricultural land value, transportation costs, changes in the structure of the economy, other location and geophysical variables) (1)

> These factors also influence the real estate market [42,43]. Transportation costs are one of such factors, and the changes in and the reach of functional urban areas are linked with the real estate market [41,44]. Therefore, temporal accessibility is one of the key factors that influence the choice of location and, consequently, the real estate market. Transportation costs, expressed both in time and money, significantly influence property value, and buyers strive to maximize benefits in the process of selecting the optimal location.

> Similar processes and interactions are observed in the former Soviet bloc countries of Central-Eastern Europe. In the socialist era, cities were relatively homogeneous functional urban areas with clearly delimited centers, whereas suburban zones were not well developed. The political transformations of 1989 led to changes inside urban areas and their surroundings. Central planning was abandoned, and free market principles were introduced. These changes affected the housing market and introduced a new urban order, which gave rise to capitalist-type cities and urban sprawl. Growing levels of social and economic inequality induced new types of migration, including urban to suburban migration. Rural areas surrounding cities developed rapidly, and suburbanization became one of the most spectacular phenomena in the former Soviet bloc countries [45,46]: Estonia [47], Slovakia [48], Bulgaria [49], Romania [50] and the Czech Republic [51].

> The introduction of ground rents and property rights as a result of political and economic transformations also triggered suburbanization processes in Poland [52–55], and real estate purchases in suburban areas increased due to growing affluence. The growth of real estate markets in suburban areas accelerated after the political transformations of 1989 [56,57], especially around big cities [58–60]. Since 1990, spatial planning policies and progressing suburbanization have led to significant changes in land-use structure (increase in residential area), infrastructure development, population and the number of transactions involving residential property. The type and scope of these changes have varied intensity and territorial scope, and they are largely determined by an urban area's status in the hierarchy of cities. The migration of the urban population to suburbia proceeded most rapidly in the largest cities, followed by medium-sized towns.

> Suburbanization is a relatively new phenomenon in Poland, and it largely follows the trends observed in other countries. However, this process has certain unique characteristics in Poland [53,61]:


In Poland, most suburbanization processes are uncontrolled due to the passive stance of urban planning authorities and the relaxation of planning regulations. The above leads to spatial chaos, low economic effectiveness of new settlements and a shortage of vital services. The low spatial accessibility of suburban settlements increases development costs and public expenditures, including transport costs and the time needed to introduce complementary services that are essential for the functioning of territorial and social systems [62–64]. Transport accessibility to the urban core is determined by the daily demand for commuter services, and accessibility tends to decrease over distance from the city [65–67]. Transport accessibility significantly impacts purchase decisions and consumer behavior on property markets. The choices made by real estate buyers can be analyzed based on the information about the location and prices of traded property.

Olsztyn, the city in north-eastern Poland, has a rapidly developing suburban zone. This medium-sized city is the capital of the Warmi ´nsko-Mazurskie Voivodeship, and it occupies a high rank in the hierarchy of Polish cities. The following research hypotheses were tested in the study:


The aim of the study was to determine whether suburbanization processes in the vicinity of Olsztyn conform to trends that are characteristic of Poland, and to define the territorial reach and dynamics of suburbanization based on local market data. The areas most affected by suburbanization were identified, and the demand for and supply of construction plots were analyzed. In this article, the number of transactions involving construction plots on the suburban real estate market was analyzed based on commuting time and distance from the city core. The observed processes were linked with demographic changes, which are the key determinant of suburbanization. The links between temporal accessibility expressed by commuting time, the prices on the local real estate market and demographic processes were identified, and their spatial distribution was described based on data for 2007–2018.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The studied area was the Olsztyn Functional Urban Area (FUA) composed of six municipalities that are directly adjacent to the city: Barczewo, Dywity, Gietrzwałd, Jonkowo, Purda and Stawiguda (Figure 2). Olsztyn is the capital city of the Warmi ´nsko-Mazurskie Voivodeship in north-eastern Poland and is an important regional hub. Olsztyn had a population of around 170,000 in 2018. The population of the suburban zone was estimated at 44,000.

The residents of the Olsztyn FUA were surveyed in 2014, and the results were published in a report entitled "Spatial relations in public and private transport in the Olsztyn Functional Urban Area in the context of urban mobility" [68]. The study revealed that private cars were the preferred mode of transportation for suburban residents commuting to the city. Passenger cars are widely available [69,70], and they are the main means of transport [71] between suburban zones and the urban core. The surveyed respondents relied on private means of transport to commute to work, school and commercial outlets, and the choice of private cars was dictated mainly by shorter commuting time. As a result, the number of passenger cars registered in the county of Olsztyn increased from 48,013 in 2009 to 71,549 in 2018 (data from the Central Statistical Office). The study also revealed

considerable differences in access to residential areas in the Olsztyn FUA, and temporal accessibility was highest in towns and villages located along the main transportation routes. Nearly a third of the towns and villages in the Olsztyn FUA were characterized by low transport accessibility.

**Figure 2.** Study area. Source: own elaboration.

Public transport services include bus lines operated by the municipalities (public bus lines reach only 11 out of 138 towns and villages, some of them only on a seasonal basis) and, to a limited extent, by railway lines. Bus lines are also operated by commercial carriers, but they only target the most highly populated towns and villages. The vast majority of suburban settlements have to rely on private means of transport which are characterized by the shortest commuting time and the highest travel comfort [72–74].

Integrated Territorial Projects (ITP) are one of the priority goals of the Strategy of the Olsztyn FUA, and they were initiated to improve the quality of transportation in that area. This development tool was introduced to implement territorial strategies in an integrated manner. The initiative enables local authorities to launch joint projects that are co-financed

by the European Regional Development Fund and the Social Fund as part of the European Union's Regional Operational Programs.

#### *2.2. Methods*

Temporal accessibility is evaluated based on physical distance as well as travel time. Travel time in the existing road network of the Olsztyn FUA was determined with the use of the Google Maps application. Travel time was measured from the administrative center of Olsztyn (City Hall) to different locations (cadastral districts) in the suburban zone. The analysis was carried out for travel by passenger car on the same date and at the same time (Wednesday, averaged result for 5 and 19 September 2018, 6 p.m.). Commuting times were interpolated for the entire studied area. The spatial distribution of the unit prices of construction plots in 2007 and 2018 were interpolated in the next stage of the analysis.

The information about the prices of construction plots (Register of Real Estate Prices and Values) and their location (base maps in the Land and Building Register, data in dxf format for cadastral plots in Barczewo, Dywity, Gietrzwałd, Jonkowo, Purda and Stawiguda) were obtained from the County Center for Geodetic and Cartographic Documentation in Olsztyn.

In both cases (temporal accessibility and prices of construction plots), data were interpolated by kriging. This method is widely applied to interpolate the spatial distribution of data, in particular, prices [75–81]. Kriging is the main method for estimating continuous surfaces based on the measured points. The estimated value is calculated as the weighted average of n measured points surrounding the estimated location. The kriging estimate (Formula (2)) is a realization of the random function *Z*(*si*):

$$Z^\*(S\_o) \ = \sum\_{i=1}^n w\_i Z(s\_i),\tag{2}$$

where *w<sup>i</sup>* denotes kriging weights, *Z* ∗ (*So*) is the value at the interpolated point and *Z*(*So*) is the value at the measured point.

Kriging is used to define the range and anisotropy of spatial correlations and to identify directional changes in spatial phenomena. Data were interpolated in ArcGIS software.

Spearman's rank correlation coefficient was calculated (Formula (3)) to describe the relationships between the number of transactions, average unit price, population and temporal accessibility. This ranking method is not highly sensitive to outliers.

$$r\_s = \frac{\frac{1}{6}(n^3 - n) - (\sum\_{i=1}^n d\_i^2) - T\_z - T\_y}{\sqrt{\left(\frac{1}{6}(n^3 - n) - 2T\_z\right)\left(\frac{1}{6}(n^3 - n) - 2T\_y\right)}},\tag{3}$$

where *r<sup>s</sup>* is Spearman's rank correlation coefficient, *d<sup>i</sup>* = *Rx<sup>i</sup>* − *Ry<sup>i</sup>* is the difference between the ith rank of variable *x* and the *i*th rank of variable *y*, *T<sup>z</sup>* and *T<sup>y</sup>* are the coefficients of tied ranks expressed as *T* = <sup>1</sup> <sup>12</sup> ∑*<sup>j</sup>* (*t* 3 *<sup>j</sup>* − *tj*) and *t<sup>j</sup>* is the number of observations with the *j*th rank in the analyzed dataset.

Spearman's rank correlation coefficient was calculated, and scatter plots were generated in the Statistica program.

The Gini index, which measures concentration relative to uniform distribution in the overall set of values of the analyzed variable, was calculated to determine the inequality in the spatial distribution of the analyzed variables (Formula (4)).

$$G\_{i} = \frac{\sum\_{i=1}^{n} (2i - n - 1)y\_{i}}{n^{2} \overline{y}} , \tag{4}$$

where *G<sup>i</sup>* is the Gini index, *y<sup>i</sup>* is the value of the ith observation, *y* is the average value of all observations and *n* is the total number of observations.

The Gini index was computed with the use of an online calculator available at [82].

#### **3. Results**

The construction of the Olsztyn ring road commenced during the analyzed period. Planning and design work began in 2005. The contracts for the construction of the first and second segment of the ring road were signed in 2015 and 2016, and the first segment was commissioned for use in December 2018. The construction of the Olsztyn–Olsztynek express road intersecting Stawiguda municipality took place in the same period, and it improved transport accessibility in Stawiguda which already had the most highly developed road infrastructure in the Olsztyn suburban zone. The ring road considerably improved transport conditions, in particular, by diverting heavy transit traffic away from downtown Olsztyn. Despite the above, the ring road had only a minor impact on the temporal accessibility of suburban settlements.

The travel time for the entire studied area (between downtown Olsztyn and all settlements in the Olsztyn urban functional area) was interpolated based on the calculated commuting times (Figure 3). The location of the construction plots sold between 2007 and 2018 were marked on the temporal accessibility map.

**Figure 3.** Temporal accessibility and spatial distribution of transactions in the suburban zone of Olsztyn in 2007–2018. Source: own elaboration.

The spatial distribution of temporal accessibility revealed local variations, mostly in the southern and south-western parts of the Olsztyn FUA. These areas are intersected by major roads, including the road linking Olsztyn with the Polish capital of Warsaw. The number of transactions change with an increase in travel time. These results indicate that the density of settlements decreased exponentially with an increase in distance from the urban core, which is consistent with urban development theories [83]. The map in Figure 2 also illustrates the extent to which the quality of transportation routes influences temporal accessibility. The number of transactions concluded in the cadastral districts of the Olsztyn FUA are presented in Figure 4.

**Figure 4.** Number of transactions concluded in the cadastral districts of the Olsztyn FUA between 2007 and 2018.

The distance to the urban core and the number of real estate transactions are bound by an inversely proportional relationship. The number of transactions decreases with an increase in distance from Olsztyn's administrative boundaries (Figures 2 and 3). The highest number of transactions were concluded in cadastral districts situated in the direct proximity of Olsztyn and along the express road to Warsaw (south-west of Olsztyn). The outlier in the north-eastern part of the studied area is a cadastral district which remains under the influence of another city, Mr ˛agowo.

The distribution of the unit prices of construction plots was also analyzed to determine the spatial pattern of real estate transactions on the local market. This is an important consideration because the choice of location is linked with prices, and prices influence decision-making [84]. The spatial distribution of the unit prices of construction plots was interpolated (Figure 5a,b) to identify trends in the development of the suburban zone and prime locations (based on prices).

The results of the interpolation point to an increase in the prices of construction plots between 2007 and 2018 and reveals prime locations with the highest temporal accessibility. The prices of construction plots were highest along the administrative boundaries of Olsztyn and in Stawiguda municipality, followed by the municipality of Gietrzwałd. These areas were characterized by the highest temporal accessibility.

The choices made by real estate buyers influence land use. New residential areas are created, which leads to changes in population [85]. Temporal accessibility induces demographic changes, and differences in commuting time influence population density. In the studied area, changes in population were strongly linked with access to transport infrastructure. As demonstrated in Figure 6, population decreased with an increase in commuting time. More people settle in areas with better accessibility.

**Figure 5.** Interpolation of the unit prices of construction plots in the Olsztyn FUA in 2007 (**a**) and 2018 (**b**). Source: Own elaboration based on the Register of Real Estate Prices and Values kept by the County Center for Geodetic Documentation and Maps in Olsztyn.

**Figure 6.** Relationship between population and commuting time in 2018. Source: own elaboration.

Similar population trends were noted in cadastral districts. Relative population growth was highest in the vicinity of Olsztyn and in districts with the highest temporal

accessibility (Figure 7). At the same time, the population of the city of Olsztyn decreased by about 2%.

**Figure 7.** Relative population growth in the Olsztyn FUA in 2007–2018. Source: own elaboration.

The distribution of transactions, population and unit prices determines the homogeneity of the processes observed in the suburban zone. Therefore, the Gini index was calculated for the above variables (Table 1).


**Table 1.** Gini index of variables in the suburban zone of Olsztyn.

Source: own elaboration.

The calculated values of the Gini index revealed growing inequality in population distribution in the Olsztyn FUA (decrease in population uniformity). The number of transactions and the average unit prices in cadastral districts were also characterized by high values of the Gini index, which points to variations in the spatial distribution of these variables. The values of the Gini index also revealed considerable differences in the temporal accessibility of the analyzed districts, which suggests strong links between these variables.

The relationships between the analyzed variables were evaluated by calculating Spearman's rank correlation coefficients (Table 2).


**Table 2.**Spearman's rank correlation coefficients for the analyzed variables.

Source: own elaboration.

The calculated values of the Spearman's rank correlation coefficient point to moderate and strong correlations between the analyzed variables (>0.5000). The strongest relationships were noted between the number of transactions and population, between travel time and population growth, and between travel time and average price. Travel time was negatively correlated with the remaining variables.

The results validate the assumption that the local real estate market was directly linked to urbanization processes expressed by demographic data. The analyses also confirmed the presence of cause-and-effect relationships between the evaluated variables.

The correlations between commuting time vs. the number of transactions and average unit prices were illustrated in scatter plots (Figure 8a,b). The scatter plots also present the influence of temporal accessibility on the local real estate market.

**Figure 8.** Correlations between (**a**) commuting time and the number of transactions, (**b**) commuting time and average unit prices. Source: own elaboration.

#### **4. Discussion**

The study demonstrated that suburbanization proceeds rapidly in the areas surrounding Olsztyn, but the spatial distribution of suburban settlements is irregular and characterized by significant local variations. The present findings validate the hypothesis that suburbanization influences the number of transactions on local real estate markets, and that transaction volumes reflect the dynamics and territorial reach of suburbanization. The results of this study also indicate that access to the transportation network significantly influences local real estate markets in suburban areas. Similar observations were made by other authors, who demonstrated that transport accessibility influences prices and transactions on the real estate market [86,87]. The correlations noted in this and other studies were similar, regardless of the size of the urban core [88,89].

Transport accessibility also influences the choice of locations on the real estate market and, consequently, population growth [90]. This feedback mechanism stimulates the growth of the real estate market. Buyers have a greater interest in residential areas with well-developed technical infrastructure and public facilities. Most areas that meet the above requirements are located in the direct vicinity of cities, and their greatest advantage is the proximity of the urban core. However, suburban areas are often deficient in social infrastructure, and many of them feature only basic retail outlets. Local residents have to commute to Olsztyn to satisfy other life needs. In contrast, rural municipalities situated further away from Olsztyn rely on local potential and attributes to develop local infrastructure. These municipalities have schools, healthcare facilities, local government offices and shops. As a result, basic life needs are met locally, and the residents do not have to travel to Olsztyn.

Herding behavior can also play an important role in real estate choices. Herding behaviors disrupt rational decision making because individuals follow other people and

imitate group behaviors. Buyers who search for suburban property often follow trends without realizing that the most popular locations are characterized by high levels of urbanization and do not differ considerably from cities (in particular, locations adjacent to city limits). In an attempt to escape the hustle and bustle of urban life, many buyers ultimately pay a high price for locations that resemble the city.

The above factors lead to spatial disruptions. The analyzed variables were not evenly distributed and contributed to disproportions between the evaluated cadastral districts. A comparison of the Gini index for the first and last year of the analyzed period revealed growing differences in population distribution, the number of transactions and average prices, which contributed to disproportions between cadastral districts. Due to similar transport accessibility, cadastral districts adjacent to Olsztyn are characterized by the highest population, the highest number of property transactions and the highest prices of construction plots. However, an analysis of the areas located south-east of Olsztyn revealed considerable deficiencies in transport planning policies both at the local (municipal) and supralocal level. These observations are validated by the spatial distribution of the analyzed variables in the maps presented in Section 3. High values of the Gini index point to uneven distribution, which suggests that the Olsztyn FUA is characterized by considerable fragmentation of the analyzed phenomena. The above generates spatial chaos, and it compromises effective land management. The intensity and spatial concentration of the investigated processes were determined by distance from the city core and the proximity of the main transportation routes, which influences transport accessibility of the studied cadastral districts. Land prices, the number of real estate transactions and changes in the population growth rate decrease with an increase in distance from the urban core. Similar trends were reported by other authors [83,91].

#### **5. Conclusions**

Olsztyn is an example of a city where transport accessibility has an influence on the development of the suburban area and the local real estate market. Similar processes are observed in all large cities in Poland, particularly in areas situated in the vicinity of regional capitals and along the main transport corridors, for example, Białystok [31], Toru ´n-Bydgoszcz [92,93], Kraków [94] and Gda ´nsk-Gdynia-Sopot [95], because suburban dwellers rely mainly on private transport, whereas other forms of transport play a secondary role [63,96]. The results of this study validate the observations made by Sleszy ´nski [ ´ 97], who noted that accessibility expressed in monetary terms does not discount the improvement in temporal accessibility resulting from the construction of new roads.

This article presents the results of the first stage of research. The eastern, southern and western fragments of the Olsztyn ring road were commissioned for use in July 2019. The ring road and the accompanying access roads will considerably improve transport accessibility in the Olsztyn FUA. Faster travel and the higher throughput of traffic entering the city will increase temporal accessibility between cadastral districts in the evaluated suburban zone. The new road system will facilitate transport between the urban core and suburban districts, and it will provide suburban commuters with easier access to the city. These changes could stimulate new residential development projects in the suburban zone. Similar observations were made by other authors [98,99], including in Polish cities [100,101].

The new ring road plays a very important role in the 2016 Strategy of the Olsztyn Functional Urban Area. The new route will facilitate transit between Olsztyn and suburban districts, and the residents of the Olsztyn FUA will no longer have to cross the city center when traveling between suburban municipalities. The ring road will eliminate traffic bottlenecks, increase the throughput of the existing transportation routes and make travel between Olsztyn and suburban municipalities more comfortable. These changes could affect local real estate markets in the suburban zone. In the second stage of the research, the real estate markets in the Olsztyn FUA will be evaluated two years after the launch of the ring road.

The added value of the present research is that data were analyzed at the level of cadastral districts, whereas most studies of the type are conducted at the level of municipalities. The adopted approach supports more accurate assessments of the observed changes, which is particularly important in medium-sized (and smaller) cities and towns that exert a weaker effect on the surrounding areas. In contrast, analyses conducted at the level of municipalities produce generalized and less precise observations.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

#### **References**


## *Article* **The Role and Importance of a Footbridge Suspended over a Highway in the Opinion of Its Users—Trabzon (Turkey)**

**Maria Hełdak 1,\* , Sultan Sevinc Kurt Konakoglu <sup>2</sup> , Banu Cicek Kurdoglu <sup>3</sup> , Hande Goksal <sup>3</sup> , Bogdan Przybyła <sup>4</sup> and Jan K. Kazak <sup>1</sup>**


**Abstract:** In the urban landscape, footbridges appeared along with the development of urbanization through the implementation of more complex spatial structures. The introduction of transport transit to cities or sometimes urban gravity towards the important communication routes imposed their construction in order to ensure pedestrians' safety and smooth flow of traffic. The aim of the study is to determine how an overpass in the city of Trabzon is used by people of different ages, the security and motivation problem, the possibility of a footbridge, and how an overpass is perceived as a crossing over a highway. The study addresses the problem of safety and motivation related to the use of an overpass by people of different ages and is focused on the perception of an overpass as a crossing over a highway. The overpass connects the northern part of the city with the parking lot and the seaside boulevards in Trabzon. It has been constructed over the road no. D010, also known as the Black Sea Coastal Highway. In total, 124 members of the urban population who used the overpass participated in a questionnaire, which included multiple-choice and open-ended questions. The 'Semantic Differential Scale' was also used to evaluate the results. The research revealed that the respondents who chose the road through the overpass for safety in most cases used it relatively rarely (59%). People who used the footbridge every day or frequently marked safety as the reason in 39% of cases only. This means that as pedestrians use the overpass more often, they begin to notice other functional features of the footbridge. The results of the study showed that the overpass was most frequently used for the purpose of going down to the coast (76.0%), although 51.2% reported rarely using the overpass, which showed it was used very little by pedestrians.

**Keywords:** footbridge; urbanization; functions of pedestrian bridges; Trabzon

#### **1. Introduction**

An urban space as a whole consists of structures and open areas where all urban activities are perceived by the inhabitants of the city. In other words, these spaces are where the activities in the lives of the urban dwellers take place. They include places for housing, working, entertainment, transportation and recreation [1].

According to Hasan and Napiah [2], for centuries, people have tried to walk over environments that contained a variety of terrains such as mountains, hills, valleys and rivers. With the advancement of civilization and urbanization of cities, which resulted in more complexes and civilized environments of habitation, and also with the development of cities having more complex communication systems, attempts were made to solve the problem of pedestrians' safety.

**Citation:** Hełdak, M.; Kurt Konakoglu, S.S.; Kurdoglu, B.C.; Goksal, H.; Przybyła, B.; Kazak, J.K. The Role and Importance of a Footbridge Suspended over a Highway in the Opinion of Its Users—Trabzon (Turkey). *Land* **2021**, *10*, 340. https://doi.org/ 10.3390/land10040340

Academic Editor: Iwona Cie´slak

Received: 5 March 2021 Accepted: 22 March 2021 Published: 27 March 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

A footbridge was one of the objects that appeared in space along with the development of urbanization. The reason for implementing this type of solution was, firstly, ensuring the safety of pedestrians and, secondly, maintaining uninterrupted vehicle traffic. Therefore, the contemporary definition of a footbridge indicates that it is a vertical separation device used to separate pedestrians from road traffic without risking an accident [3].

Urban inhabitants must use urban spaces in all aspects of their lives. However, only the presence of appropriate and adequate technical, functional, aesthetic and behavioral reinforcement elements can enable people to use urban spaces in the best possible way. Reinforcing elements not only meet the needs of the people but also have psychological effects on them [4].

The most active forms of transportation are walking and bicycling, which have the lowest impact on the environment and improve the physical health of pedestrians and bicyclists. The most common problem impeding the preference for walking and bicycling is traffic safety [5–7].

Pedestrians are most at risk when they are crossing the road. This represents a significant proportion of all fatalities among pedestrians, amounting to 50% in non-built-up areas and 75% in built-up areas. The most frequent reason for this kind of accident is failure to give way [8,9].

Pedestrians who are crossing are considered to be one source of congestion [10]. According to Binti Kadzim, a pedestrian bridge is the best solution for pedestrians to cross the road. A pedestrian bridge is only one way to increase road network capacity [11].

Bridges connect destinations in communities and provide access to emergency and essential services. Bridges that lack pedestrian and bicycle accommodations can force substantial detours or sever routes entirely, discouraging or eliminating the option to walk and bike for transportation. Those who do travel on bridges without proper accommodations may increase their risk of being involved in a crash [12,13].

Pedestrian and bicycle bridges will provide that pedestrian and bicycle users can cross the road safely and go to school or businesses safely. Therefore, where it is needed, the design and the material used in the design are very important. Pedestrian and bicycle bridges should be both "functional" and "aesthetic" [14].

In both urban and landscape design studies, there is a need for a variety of structural elements that meet the physical needs of the population and are necessary for their understanding, safety and comfort in terms of health and a clean environment. These elements are defined as urban elements, and they should be compatible with other elements and improve the visual quality of the landscape design as a whole when used correctly [15]. Among these elements are pedestrian overpasses that provide pedestrian access, i.e., pedestrian bridges. These overpasses or bridges have the especially important goal of providing pedestrians with a means to travel within or between the areas that we call urban spaces. Although the main purpose of pedestrian bridges is to facilitate transport and increase pedestrian safety, this function alone is not enough for users. Bridges must also have an aesthetic value. They should also accommodate different types of activities for pedestrians. This condition determines the value of an urban pedestrian bridge [16–19].

Tunnels and pedestrian bridges should be used as collision-free crossings when [20] pedestrian routes intersect with higher class roads, pedestrian routes intersect with the roads of G (main road) or GP (major trunk road) class with heavy traffic, and the location of an at-grade pedestrian crossing may pose a serious hazard to pedestrians or cause long delays for vehicles and pedestrians.

According to studies conducted by Jamroz et al. [20], Ivan et al. [21], Congiu et al. [22] and Arroya et al. [23], most pedestrians and bicyclists were at risk of accidents. In today's cities, pedestrian zones are being implemented to provide comfortable and safe passage for pedestrians.

Pedestrian overpasses are structures that allow pedestrians to carry out their movements easily and conveniently. For this reason, some of them are built over vehicle traffic roads, some over parks and gardens and some over waterways. Their construction materi-

als can be wood, stone, brick, reinforced concrete, steel or metal, and hybrid solutions are also used, in which the structural elements are made of different materials working together. They can be in the form of beam, arch, cantilever, suspension or truss bridges [24–28].

These structures pass over roads that are open to vehicle traffic and, in addition to their functional purpose of facilitating and directing the continuous flow of uninterrupted pedestrian movement and ensuring pedestrian safety, these passageways function as urban reinforcement elements that, with their aesthetic aspects, can affect the city's appearance. Therefore, the design criteria used for features and fixtures in urban spaces should also be applied to pedestrian overpasses.

Various examples of functional solutions for pedestrian bridges over roads can be listed as well as the attempts of classifying such constructions can be taken up considering their secondary function—apart from the basic one, i.e., ensuring safety for pedestrian, pedestrians and cycling traffic in cities, or providing the possibility of reaching dangerous and attractive landscape spots located in open spaces. In the urbanized area (city) the following can be identified:


The efforts aimed at revitalizing railway bridges and road viaducts into overpasses can be noticed both in cities and in open areas. The examples of using constructions that previously performed different functions and created by conversion of remnants are the "Promenade Plantee" in Paris and in the USA the "High Line Park", "Walkway Over the Hudson" in New York, the "Kinzua Bridge State Park" in Pennsylvania and the "Vance Creek Bridge" in Washington State.

The overpasses built in open areas are often examples of architecturally outstanding constructions, harmoniously integrated into the natural landscape. They frequently constitute impressive structures offering complex architectural solutions, e.g., the "High Trestle Trail Bridge" in Madrid, the "Moses Bridge" in Holland, the "Plitvice Lakes National Park Bridge" in Croatia, the "Zhangjijie Grand Canyon Glass Bridge" in China, the "Langkawi Sky Bridge" in Malaysia, the "Jasper National Park Glacier Skywalk" in Canada and the "Tree Top Walkway" in London.

Pedestrian bridges, especially in heavy traffic areas, were designed as a solution in the context of pedestrian safety and the continuity of the roadways. In addition to being designed engineering structures, their aesthetic aspects can be evaluated as elements of significant urban fixtures. Pedestrian overpasses are important elements that should be considered not only for their functional requirements, but also as elements that can affect the city image and aesthetics. Consequently, pedestrian bridges can be regarded as being important to the city as creative elements that can attract attention due to their larger scale [29,30]. It is worth noting two approaches in the architectural shaping of footbridges (and bridges in general), this first aiming at creating a characteristic element, recognizable and noticeable even from a distance, and the second assuming the maximum integration (concealment) of a new object in the landscape [16,18,31].

The authors of the presented research focused on the problem of safety and motivation for using an overpass by people representing different age groups and on the perception of a footbridge as a crossing over a highway.

The authors noticed that the residents of Turkish cities are reluctant to use overpasses, which results in numerous problems related to road safety (in the case of an overpass construction, traffic lights and pedestrian crossing are not located in the same place, i.e., "a zebra crossing" through the road). There is evidence that some types of facilities are

generally disliked by pedestrians, which leads to a high incidence of informal road crossing behavior, away from crossing facilities [32–35].

The research addressing footbridges is focused on


There is a gap in analyzing opinions on the multifunctionality of buildings over highways in cities in terms of assessing a viaduct in the category of an aesthetic, functional and identity type of element, which was the basis for the conducted research. However, the primary function of a footbridge (ensuring safety) cannot be neglected, which constitutes the background of the conducted research.

The following research hypotheses were put forward in the article:

**Hypothesis 1 (H1).** *Young people and students of different age groups Trabzon residents are more likely to use overpass than older and working people.*

**Hypothesis 2 (H2).** *Young people pay attention to the advantages of a footbridge other than safety.*

**Hypothesis 3 (H3).** *The awareness of ensuring safety and motivation for oneself and family grows with age.*

The purpose of the research was to show the preferences of Trabzon residents and visitors in terms of using the overpass built over the highway. The overpass connects the northern part of the city with the parking lot and the seaside boulevards in Trabzon. It has been constructed over the road no. D010, also known as the Black Sea Coastal Highway. This road is the main west-east highway in Turkey, serving the Black Sea Coast. In this part of the city, the road is a barrier to access the Black Sea shore. On the one hand, the city's spatial development and the density of buildings and, on the other hand, the significant traffic of motor vehicles resulted in the need to search for solutions in the form of safe access to coastal boulevards. There are seven footbridges along the section of the route running through the city of Trabzon, whereas only a small part of the road runs through an underground tunnel. The importance of this study is to reveal the design, safety, comfort, aesthetics, symbolic element and importance for transportation of an overpass in Trabzon city.

#### **2. Materials and Methods**

The Imperial Overpass, which is located within the boundaries of Ortahisar district of Trabzon city, where area field studies were carried out in the study, constitutes the main material of the study. Observations and questionnaire studies, photographs taken during field studies, SPSS 16.0 statistical program were other materials used in the research.

Imperial Overpass, which connects Gazipasa Street in the city center of Trabzon to the Black Sea Coastal Highway, has been chosen as the study area (Figure 1). The Black Sea Coastal Highway is the main west–east highway in Turkey, serving the Black Sea Coast. The reasons for choosing Imperial Overpass are as follows: it is very close to the city center of Trabzon, it is used intensively by pedestrians, there are bus stops that provide transportation to Trabzon city with other districts, it connects the city center with the coastal area and it provides access to the parking lot of the Imperial Hospital.

**Figure 1.** Satellite image showing the study area. Source: own study.

Steel was used in the construction of the footbridge, and the floor is of cast concrete. One abutment is located over a section of road in a residential area, while the other is connected to the hospital parking area (Figures 2 and 3).

**Figure 2.** The view of the overpass over the road no. D010, also known as the Black Sea Coastal Highway, after construction of the roofing. Source: own study.

**Figure 3.** The view of the overpass over the road no. D010, also known as the Black Sea Coastal Highway, after construction of the roofing. Source: own study.

The presented research was carried out according to the following research scheme:


Some of the issues concerning the footbridge include:


Taking into account the overpass location along the frequently used pedestrian route and also in an important cultural and landscape place, i.e., in the center of Trabzon city, the research was carried out to identify the expectations and motives of those using the aforementioned footbridge over the road. The popular and busy communication route (highway no. D010) definitely cut off the coast and seaside boulevards from the city, and the overpass under study allows reaching these places without collisions. However, does it meet the residents' expectations? Does its form correspond to the significance of the space in which it is located? In order to obtain answers to a number of doubts and to find out the preferences of the overpass users, the following research questions were formulated:


In the study, the quantitative research (questionnaire) method was applied to inhabitants of Trabzon in order to determine the current usage status of the Imperial Overpass and to determine the feasibility of pedestrian and bicycle bridges in the city. For the study, a questionnaire-based field survey was administered and face-to-face interviews were carried out to determine the reasons the people of Trabzon city used the overpass and how frequently they visited it (Supplementary Material). The purpose of the quantitative research method was to qualify the numerical information. Before the questionnaire questions were prepared, on-site observations were made by going to the study area. The studies of Arslan Selcuk and Er Akan [1], Hasan and Napiah [2], Ku¸skun [4], Mumcu [41] and Denli [42] were consulted during the preparation of the questionnaire. The urban population of 2019 was taken into account when determining the number of people to be surveyed. According to the data obtained from Turkish Statistical Institute, the population of Trabzon Central District in 2019 was 328,457. During the preparation of the questionnaire, care was taken to ask clear and understandable questions that could be answered, while still respecting people's privacy. The questionnaires were designed for individuals over the age of 18, with the idea that they would take a more conscientious approach. The questionnaire was carried out between March and August 2019, with 124 people living in the city center of Trabzon. The random sampling method was used to determine the sampling size for the questionnaire study. The inhabitants who participated in the questionnaire were asked a total of 12 multiple-choice questions and also provided information about their demographic characteristics. Each questionnaire took an average of 20 min.

The results of the demographic composition of the 124 people who participated in the survey were as follows:

Gender: 78 (60.5%) were female and 46 (35.7%) male.

Age: 45 (34.9%) were aged 18–23 years, 49 (38.0%) were aged 24–29 years and 30 (23.3%) were aged 30+ years. The average age of the respondents was 28.0 years of age, with a standard deviation of 9.85 years. At least half of the respondents were not older than 25. The youngest respondent was 18 years old, whereas the oldest one 63.

Educational status: 4 (3.1%) had completed primary school, 1 (0.8%) middle school, 18 (14.0%) high school, 84 (65.1%) university and 17 (13.2%) post-graduate education.

Occupational status: 27 (20.9%) were architects/engineers, 50 (38.8%) were students, and 47 (36.4%) were in other occupations.

The survey was conducted with the participation of 124 individuals, of whom 50 were randomly selected, 27 were architects/engineers and 47 were from other professions. In addition, in order to determine the value of the overpass in terms of semantic aspects, the contexts of using the overpass other than their preferences and expectations were detected in accordance with adjective pairs using the Semantic Differential Scale. In this respect, the participants were asked to evaluate the overpass using the adjectives "functional", "harmonious", "aesthetic", "attractive", "special", "memorable" and "safe" and their paired counterparts using a 7-point scale. The scale given in Table 1 below was used for scoring. The participants were people living in Trabzon who used the overpass. Therefore, assessments were facilitated by reminder photographs used during the survey (Figure 4). The data were analyzed using the SPSS 16.0 statistical program. In the study, correlation analysis was performed with descriptive statistics.

≤


α

**Table 1.** Evaluation system of the criteria used in the survey.

1: dysfunctional; 2: somewhat dysfunctional; 3: minimally dysfunctional, 4: average; 5: minimally functional; 6: somewhat functional; 7: functional. Source: own study.

**Figure 4.** Photographs used in the survey.

The significance of differences in the distribution of responses between nominal variables was checked using the chi square test of independence. To calculate the chisquare statistics, the following formula was used:

$$\chi^2 = \sum\_{i=1}^{n} \frac{\left(O\_i - E\_i\right)^2}{E\_i} \tag{1}$$

where:

O—Observed value;

E—Expected value.

For all analyses, the maximum permissible error class I α = 0.05 was adopted, and *p* ≤ 0.05 was considered statistically significant.

#### **3. Results and Discussion**

Beyond any doubt, the Black Sea route remains in conflict with the development of the city tourist function. Urbanization processes resulted in the need to search for solutions allowing the separation of pedestrians and vehicle traffic. Unfortunately, the analyzed object differs from the contemporary design solutions, which often fit into the surrounding landscape, and their implementation uses modern engineering techniques and construction materials. Frequently, such objects make the landscape more attractive, which cannot be said about the analyzed footbridge.

When asked about the frequency of using the overpass, out of the total of 124 people, 51.2% answered "Rarely", 14.0% "1–2 times per month", 10.1% "Everyday" and "Frequently", 7.0% "1–2 times per week", 3.9% "2–3 times per week".

The overpass does not connect university buildings or any other buildings used primarily by people under 25, it is generally accessible and apart from the car park and it also provides access to the seaside boulevards. Hence, it was reasonable to analyze who the main users of the overpass are and how frequently they use it.

Correlation analysis using Spearman's rank correlation coefficient confirmed a statistically significant relationship between age and the frequency of using overpass (*p* = 0.005); however, this relationship was different from that formulated in the hypothesis. Older respondents used overpass more frequently; therefore, the discussed hypothesis should be rejected (Table 2).

**Table 2.** The values of Spearman's rank correlation coefficient. The relationship between age and the frequency of using overpass.


Source: own study.

A statistical analysis was also carried out using the chi square test of independence. To this end, the respondents were divided into two groups, where the division point was the median value for the age variable (25 years of age). Some categories related to frequencies of using overpass were also combined, so that the smallest percentage of cells in the developed cross-table had an expected number of less than 5. The respondents under 25 years of age rarely used the overpass (64%), and the percentage of such population aged over 25 was smaller and amounted to 40%. The percentage of people using the overpass often and daily in the group under or at the age of 25 was 12%, whereas among older people it was 32%. The differences are statistically significant (*p* = 0.027) (Table 3).

**Table 3.** The distribution of responses broken down by age.


The statistical analysis using the chi square test of independence showed a statistically significant relationship between social status and the frequency of using the overpass (*p* = 0.018). The answer often or daily was indicated by 7% of the respondents from the group of engineers, 16% by students, most often by other respondents—34% (Table 4).


**Table 4.** The distribution of responses broken down by social status.

Source: own study.

When asked about the direction of moving through the overpass—in the first place, 76.0% of the respondents answered "To go to the coast", followed by 9.3% in second place with "To go to the city center", 7.8% in the third place with "To go home", 2.3% "To go to the hospital" and, finally, 0.8% responded "To go to the university" (Figure 5).

**Figure 5.** Reasons for overpass use.

The highway limits the possibility for taking advantage of using the coastal location of the city in many respects. First of all, by cutting off the coast and the seaside boulevards from the invested part of the city, it reduces opportunities for the development of recreation and tourism in such a large city of 240,000 residents.

As has been confirmed by the conducted research, the primary purpose of using the overpass is to ensure safety—out of the total number of 124 participants, 36.4% provided the answer "For security", 27.9% "There is no alternative", 20.2% "It's a convenient route", 10.1% "It's the way to the coast" and 1.6% "It's the way to the parking lot" (Figure 6).

Ensuring safety on the one hand and smooth traffic flow of motor vehicles on the other obviously remains the essential purpose of constructing overpasses. However, not all of the respondents indicated safety as the reason for choosing the discussed footbridge, other reasons were as follows: no alternative, convenience and the way to reach the destination (coast, parking lot). Indeed, the traffic of motor vehicles on the highway no. D010 is so intense that virtually all along its length in Trabzon, it is required to use either an overpass or an underpass to get to the other side.

It is important that such a possibility of reaching the sea is also attractive for the pedestrians in every respect (apart from safety, the equally important factors are comfort, harmony with the surroundings and an attractive architectural form of the facility).

The respondents who chose the way through an overpass for safety reasons generally used it rarely (59%), and the percentage of such people among those selecting an overpass for another reason was smaller and amounted to 31%. Such individuals often used an overpass daily or often (39%). The analysis using the chi square test of independence showed that the discussed differences are statistically significant (*p* = 0.019) (Table 5).


**Table 5.** Reasons for choosing vs. frequency of using an overpass.

The statistical analysis did not show any significant correlation between the respondent's age and the choice of safety as the reason for traveling through an overpass (Table 6).

**Table 6.** Reasons for the choice vs. age.


Source: own study.

The role of pedestrian bridges is not being fully implemented, because according to Abojaradeh, in Jordan, over 60% of pedestrians choose not to use pedestrian bridges for various reasons [36]. Females use them more than males and children more than adults. In that case, such a correlation was not confirmed. *χ*

**≤**

The main advantage of pedestrian footbridges is that they separate pedestrians from road traffic. As a result, footbridges (and stopping pedestrians from crossing the roadway at-grade) may reduce pedestrian accidents up to 90% [8]. It was concluded that pedestrian bridges have a positive impact and have great potential to reduce the number of pedestrian fatalities [42]. When asked the question, "Do you consider the footbridge over the road no. D010 safe?", as many as 35.5% of the respondents answered "No", however, their vast majority consider it safe (64.5%) (Figure 7).

It is worth analyzing the reasons for the feeling of insecurity expressed by some of the people participating in the study. The preference for particular facilities also depends on their design and maintenance, which are associated with perceptions about crime and concerns about aesthetics and hygiene [33,35,43,44]. After performing the survey research, the overpass was covered with roofing, which definitely influenced the safety of using it (Figure 8). Unfortunately, the crossing is still not accessible, e.g., for people moving in wheelchairs, and causes many difficulties for those who have problems with mobility in general.

**Figure 8.** The overpass suspended over the highway no. D010 after constructing the roofing.

15.5

Walking I don't know Riding a bicycle Sitting/Observing/Having

10.9

fun

**How would you like to spend time in an area without vehicle traffic?**

24

34.9

Taking an overpass to cross the road may be perceived as attractive or not, and the footbridge itself, apart from ensuring a safe crossing over a communication artery, can also be used as a viewing point for walking, cycling or observing the surroundings. Building a pedestrian bridge over a highway allows for other activities. When asked, "How would you like to spend time in an area without vehicle traffic?", out of 124 participants, 34.9% responded "Walking", 24.0% "I do not know", 15.5% "Riding a bicycle" and 10.9% "Sitting/Observing/Having Fun" (Figure 9).

**Figure 9.** Activities that can be carried out on the overpass.

Taking into account the applied research methodology and the evaluation system of criteria used in the survey, presented in Table 1, the following research results were obtained. The average value of each criterion was calculated according to the results of the survey. In order to determine whether there is a relationship between the criteria, the average values given by the 124 people participating in the questionnaire for each criterion are averaged and summarized in Table 7.

**Table 7.** Average values of survey criteria.


Source: own study.

The existence of a relationship among the criteria of "functional", "harmonious", "aesthetic", "attractive", "special", "memorable", and "safe" was assessed by applying the Spearman correlation test. According to the Spearman correlation, a significant positive correlation was found among all the criteria (*p* < 0.01). Among the criteria, the highest correlation coefficient values were found for "functional" with "harmonious", "harmonious" with "aesthetic", "harmonious" with "safe", "aesthetic" with "attractive", "attractive" with "special" and "special" with "memorable" (Table 8). The expected result was that all the criteria were interdependent because they all supported each other.


**Table 8.** Relationship values among the criteria.

Sig. level: Significance level; Cor. Coe.: Correlation coefficient; Source: own study.

The results of this study have emphasized that this overpass could carry a semantic and symbolic element in addition to the binding element. In parallel with the positive examples abroad, the overpass should be re-evaluated within a correct concept, format and scenario for its environment.

The positive adjectives that we used are a powerful semantic, syntactic and pragmatic fiction for the continuing research that needs to be done towards an overpass that can serve as an aesthetic, functional and identity element. The overpass that is the subject of this study must be recreated and, by using a correct design approach, a new pedestrian bridge with an identity must be generated. From a study of pedestrian bridge crossing conducted in Bangladesh (Dhaka), it was found that 71% of pedestrians prefer an underpass rather than using foot over bridge. Research conducted in Poland also confirmed a greater tendency to use underpasses by pedestrians compared to footbridges [9]. Always, however, one should take into account local conditions, which impose the only correct solution (in the presented case, the topography is decisive). The reasons pedestrians do not use road crossing facilities are insufficient security, the fact that they are time-consuming, poor entrances, hawker's problem, discomfort, the long walk required, etc. [10].

First of all, a good spatial composition should be prepared, and spatial components and items should be brought together in a harmonious way [41]. The composition of this architecture is formed by bringing the elements together and deliberately arranging them to create a semantically, functionally and visually satisfactory whole [45].

The space composition can be defined by the following semiotics to design a balanced pedestrian bridge. These;


In order to design a balanced pedestrian bridge, the spatial composition can be defined by following the semantics. Semantics is actually the science of meaning [30]. When we consider it in a project, this term shows the semantic value, i.e., the concept, of the resulting design. In a way, it contains the main idea of the project. A concept is determined in the context of the semantic fiction, and the project is shaped by it.

Syntactic fiction contains a sense of meaning [46]. We can think of this form of syntax in design. It can be used as a form in the concept, or it can be reflected in the appropriate activities within the concept. Pragmatics is a meaningful phenomenon [46]. We can think of pragmatic fiction in design as a set of activities and a scenario showing a sequence of these activities. A space is not created solely by its existence. The activities within the space and those who use it literally exist. For this reason, the more activities provided in a space, the more open it is to use.

The research confirmed that the presented footbridge is absolutely necessary in accessing the Black Sea coast. In the near future, it is necessary to consider the construction of a new footbridge having a modified architectural form, because apart from its basic function, i.e., safety in pedestrian traffic, it should also meet an aesthetic function and confirm the cultural identity of the city. It should be considered to construct a footbridge of the 21st century using modern technologies and taking into account the tradition of the location.

#### **4. Conclusions**

The conducted research allowed answering the formulated research questions and putting forward the following conclusions:


movements of the sun. The semantic and functional aspects of Trabzon's largest and most crowded city square and Gazipa¸sa Avenue, an important street whose former value has been lost, are connected to the coast by this overpass.

8. The authors believe that the city authorities should aim at constructing a representative overpass, connecting the old part of the city with the Black Sea coast, which could also be a showcase of the city inhabited by 240,000 people and apart from the issue of safety could become a permanent element of its landscape as an attractive architectural object and a viewing point.

When the results of the study are evaluated in general, it is seen that the Imperial Overpass is safe, aesthetic and functional for Trabzon city and is used by different age groups to reach the coast. The results obtained through the questionnaires and observations confirm the hypotheses of the study. New activities can be created, and a new function can be given to the overpass in order to ensure that the footbridge can be used by everyone. Thus, the overpass gains a symbolic meaning for Trabzon city.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/land10040340/s1, Questionnaire S1.

**Author Contributions:** Conceptualization, S.S.K.K., H.G. and M.H.; methodology, H.G., S.S.K.K., M.H.; software, B.C.K.; B.P. and J.K.K.; validation, B.C.K. and B.P.; formal analysis, H.G.; investigation, M.H., S.S.K.K., B.C.K.; resources, B.P., M.H., J.K.K. and S.S.K.K.; data curation, H.G., S.S.K.K.; writing original draft preparation, S.S.K.K., H.G., B.C.K., M.H. and B.P.; writing—review and editing, M.H., S.S.K.K., H.G., B.P. and J.K.K.; visualization, H.G., S.S.K.K. and M.H.; supervision, B.C.K.; project administration, M.H. and J.K.K.; funding acquisition, J.K.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was co-funded by the Wrocław University of Environmental and Life Sciences.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

**Limitations of the Study:** One of the research aspects was to reveal a slightly different perception of the reality by the co-authors. The authors from Poland assess the current technical, functional and aesthetic condition of the footbridge more strictly, whereas the authors from Turkey are more focused on its reconstruction and modernization, approaching the footbridge as a permanent element of the city landscape. The research conducted in the footbridge area was accompanied by car noise, which made it difficult to interview the footbridge users.

#### **References**


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