The Impact of Urban Spatial Plan on Land Value: An Approach System to Relating Space Syntax Premises to the Land Price

Abstract

This research study explored the impact of an urban spatial plan on land value; it drew on the value model based on space syntax as a systematic technical piece of software that explains and analyzes the relationship between land use and land value. The study proposes a framework based on the urban spatial network. We selected the Kirkuk city master plan for evaluation purposes and hypothesized that there would be a relationship between the space syntax of the essence of urban spatial integration and the price of land. Therefore, the case selected was evaluated in three aspects of analysis: the urban spatial function, which involves the integration rates of the city’s street network and connectivity; the urban land price assessments in the context of the city’s spatial grids; and the expansion of land use distribution, including residential and commercial grounds, to explain the changing economic value of the spatial relationship in the land market. OSM, AutoCAD, depth map X8, QGIS 3.16, and SPSS were used for data cluster analysis, spatial network preparation and analysis, and correlation analysis. The results showed that the urban spatial plans and comprehensive urban socioeconomic and environmental factors had a significant impact on land price. This result can enhance the future spatial design and the economy of metropolitan areas.

1. Introduction

The availability of different types of zoning in cities due to urban planning, urban development, and design creates many problems, including land value and land price problems. On the land value side, several factors affect the land studied to determine its value. On the land price side, there is an exploratory reverse relation between land supply and its cost; an increase in supply leads to a reduction in land cost, while a lack of supply leads to a high price of land until the time comes that supply is equal to demand which is the satisfaction point. In new developments, the situation is different; increasing supply may not reduce the land price due to market fluctuation. In modern times, land has become a marketing resource used as a business tool; therefore, supply is less effective than it used to be. The availability of a weak economy and an oversupply of properties leads to low or no demand, and land prices tend to fall. On the other hand, land prices tend to rise with a strong economy, and a high demand for properties in a particular area, even with low supply. In addition, supply does not affect some properties in cities; for example, land in CBD areas, especially in developed countries, is increasing incredibly day by day without even considering supply.

In urban economy studies, various studies have attempted to determine land prices. According to the models recorded from the past three decades up until recent days, there is a need to obtain more accurate information about the relationship between the value of the urban street network and the price of land. Refs. [1,2] studied the effect of urban concentration and road networks on land value. The authors concluded that the impact on urban land values and the optimal space allocation for transportation activities still needs to be evident [2]. On the other hand, ref. [3] emphasized that land accessibility and location are the core concepts of land value and price. Land value occupies a more significant part of the research area, but it does so less implicitly in urban accessibility [3,4,5,6,7].

One of the critical characteristics of recent studies on urban morphology is the use of street networks to describe the built environment. Spatially, the city is not a collection of building blocks that may have geometrical regularities and ultimately architectural styles, but it is rather a network of interconnected open spaces created by those blocks of the urban grid [8,9,10]. Thus, the urban street or spatial grid can identify the diversity of a city. Many researchers have used street network patterns as an essential tool for urban assessment [11,12,13]. Ref. [14] (p. 66) identified that the sideways street could improve urban public spaces. Furthermore, ref. [10] studied 100 cities worldwide and demonstrated that cities could be analyzed according to their spatial network because of logical distribution: “Street networks organize and constrain a city’s transportation dynamics according to a certain spatial logic—be it planned or unplanned, ordered or disordered” [10] (p. 17). Such studies have unfolded cities in terms of their underlying spatial organization, tracing a connection between space and society, and revealing that the urban grid contains an imprint of a community [15]. Additionally, according to [16] (p. 53), the city’s communication generation mechanisms depend on the urban street network, which they defined as the spatial structure of the metropolitan area; this spatial structure can be described as a natural movement. The diversity of this natural movement causes the motion economy [16] (p. 54).

The main structural feature of urban settings is the understanding of the evaluation of urban planning and design. Through the innovative evaluation of spatial syntax analysis, there will be an opportunity to find a link between the urban street network and urban land prices because, according to some researchers, “different suggestions for resolving urban street network can be found by space syntax analysis” [3,13]. Considering the critical relationship between urban spatial plans and land price, this study hypothesizes a relationship between space syntax premises and land price. By evaluating urban spatial network and urban land price, there is an opportunity to define and discover those premises of space syntax most relevant to land costs. This study aims at shaping a new and comprehensive approach to finding out the relationship between urban street networks and urban land price through the investigation of the axial line analysis of space syntax and its economic impacts on land value. Hence the study attempts the following:

• To elaborate and explain the land value and price in the selected case.
• To examine how urban spatial networks are related to land price.
• To explain factors that influence land values and prices.
• To find out premises of space syntax correlated to a land price to predict urban spatial land price model.

In this study, the state of the urban economy is assessed under the entire urban street network concerning land prices using the bid rent theory. Kirkuk city had chosen for the purpose of evaluation, land price data collected from different real estate and evaluated and then compared them with an axial line analysis of space syntax. OSM, AutoCAD, depth map X8, QGIS 3.16, and SPSS were used for data cluster analysis, spatial network preparation and analysis, and correlation analysis. The result showed a significant impact of the urban spatial plan and comprehensive urban socioeconomic and environmental factors on the land price. Accordingly, a crucial decision in urban land zoning, master planning, redevelopment, and transportation requires assumptions concerning the long-term trend in land value. This can be achieved by optimizing the new program based on space syntax premises more closely correlated to land price. To achieve this, the last section of the study includes the recommended comprehensive determinants of land prices that can be used in future land price programming.

2. Literature Review

The Urban Economy and Urban Spatial System

As a fundamental component of the urban economy, the city center can define the origin of urban settlement and sustain it through economic growth. Several nodes can explain centrality as a theory of social and economic sciences in urban studies, with their association with the location of their network system. Traditionally, the city structure included a single city center. However, more recently, the city center has contained a monocentric city center and a multicenter of the urban form [17]. Changing economic development has led to a shift from a monocentric to a polycentric system. New urban economics can be traced back to Alonso (1964), Mutt (1969), and Mills (1967) as the creators of the pioneer theories of the urban internal structure linking land use to land markets. In urban land marketing, the Alonso bid rent theory (1964), based on van Thünen’s (1826) theory, explains the process of the formation and development of urban areas. The bid rent theory is the relation between the price and distance of land from the central business district (CBD). According to this theory, an urban spatial structure is the formation of a typical form of new classical economics; the spatial relationships between the different sectors explain the underlying drive for the functioning, appearance, and evaluation of cities. Bid rent theory is based on the reasoning that the more accessible an area is, the more profitable it is [18].

Regarding an urban street network and accessibility, ref. [19] pointed out that the city center should have a greater rate of employment for those who will thus choose to dwell in the city, considering the aspects of cost and also time. Ref. [20], in his book Edge City: Life on the New Frontier, discussed that the city’s edge is made up of many segregated centers out of the traditional business centers in suburban areas. According to [21], the new urban economic theory, as contemporary urban economics, works not only as a microeconomic theory but it also deals with urban spatial interaction theory; thus, this model balances supply and demand, the main features of urban economics. However, many other factors influence land markets at the microeconomic and macroeconomic levels; for many researchers, supply and demand primarily drive the market behavior [22,23,24] because some multi-collect factors play significant roles in supply and demand. Based on [25,26] studies, urban land price can be determined by economic fundamentals, including supply and demand at the city level. Supply and demand are among the oldest determinants of land prices; according to [27], land supply and demand are two factors that influence 80% of land price increases.

On the supply side, urban planning and design play a more significant role in defining land value; land distribution is a fundamental factor affecting the land market. For example, residential properties with access to more metro lines and stations and bus stops were associated with higher housing prices, with metro stations exerting more effects [22]. Four influencing factors have the most significant influence on housing price, distance to the inner ring, distance to the hospital, bus density, and space to the subway station [28]. Macroeconomic variables such as location, the availability of service buildings and infrastructure, and commercial activities explain land price; therefore, spatially, at the urban level, the value of land does not share the same logic as land price. Some of the most expensive plots of land are located on the city side, or in the heart of the city, and some of the lower-priced plots are situated in the suburbs or formal areas. On the demand side, land price increases as demand for land increases. Additionally, a higher order for good land causes the price of the ground to be higher; in many cases, much higher than what is affordable. For example, more affluent residents may be willing to pay a higher fee for a good view of the lake, while infrastructure barriers have an adverse effect [29]. A study by [25] demonstrated that solid demand in the Chinese real estate market causes the housing demand to exceed the supply market and raises housing prices, which subsequently raises land prices. Otherwise, economic fundamentals determine land value and cost at the urban level. Increasing capital raises the prices of local housing [30]. It can be stated that the demand for accessible land close to amenities with an applicable infrastructure is growing without considering affordability.

The modern era of the urban system differs from the 19th century; it includes a monocentric sample that calls polycentric development more attractive than before. In this way, the urban polycentric structure encourages the agglomeration of the local economy. Thus, urban economy and form can be evaluated based on spatial distribution. From an economics perspective, Alonso’s theory as a model economic distribution theory [5], and space syntax as the most relevant software for urban spatial analysis provides a better understanding of urban land economics.

3. Research Design Process and Methodology

The research method is a hypothetico-deductive model. As the first step, the study proposes a framework based on the urban spatial network. The next step is to clarify the problem, followed by developing an inductive hypothesis that the space syntax premises are related to land value. The final stage is a practical test of the hypothesis. See Figure 1.

3.1. Space Syntax

Bill Hiller’s [8,16] books significantly identified the theory of space syntax. The fundamental determination of space syntax is the structure of the grid itself; the urban grid, through its influence on the movement economy, is the essential source of the multi-functionality that gives life to cities [16]. The use of space syntax has contributed to an understanding of the spatial city’s spatial structure shaped by society on the one hand, and how it can generate or affect specific socioeconomic processes in a community on the other [13,31,32].

The urban master plan can be analyzed based on its physical and functional aspects. According to Hiller, a city can divide into two actions: stock buildings that integrate by a spatial structure and transportation and functionality that include the social economy and environmental issues [16]. However, why space syntax? According to [33], the typology of urban design theory involves different dimensions to analyze and evaluate the urban form. From his evaluation of urban design theories, it can be stated that there is a lack of a comprehensive vision in every phenomenological approach to assess and measure the parameters of urban land value because there is individual judgment in every single theory; it is subjective or objective, which is not quite enough to evaluate urban land value comprehensively. Therefore, it can only be expected to be found in space syntax theory as related to urban social–spatial configuration analysis; see Figure 2.

From the urban design literature, we can say that space syntax is the most contributed theory to the study as it can link urban design and urban land value modeling. Street networks, as a spatial configuration, can provide an understanding of urban areas and can verify urban quality, including land value [32]; see Figure 3.

3.2. Empirical Study

3.2.1. Case Study Social, Economy, and Environment Background

The city of Kirkuk is located in the northern part of Iraq; see Figure 4, with coordinates of 35°28′42.8340″ N and 44°24′6.9552″ E, with a latitude of 35.478565 and longitude of 44.401932; the total city area is 96.79 km² [34]. Kirkuk lies in a hot region with only some topographical features. It includes a river that runs from the top of the city to right down inside the heart of the city. The highest position in Kirkuk is its castle, which is 368 m above sea level. The highest place in the town is its fortress, which is 18 m in height. Economically, the city is in a mound region, which means it exists between a plain and a mountainous region between the three main towns of Sulaimaniyah, Erbil, and Baghdad; this has made the city a center for the exchange of products and goods. Kirkuk is a multi-ethnic and multi-religious melting pot, including Kurdish, Arab, and Turkmen communities. The city includes topographical features and ethnic and religious diversity [35].

3.2.2. Sampling System and Data Collection

The study used different procedures for data collection purposes. There are both primary and secondary data, with both qualitative and quantitative types. The first step was to download street network data from the city’s OSM website of the case selected and prepare it manually using AutoCAD software. The axial spatial map was analyzed using depth map X8, and for data presentation and illustration, the study used QGIS 3.16. Additionally, some data were gathered on site, such as photos, land prices, and land-use diversity. The land price data were collected from various real estate as the primary source for land marketing; first, in each sample, different locations of land were selected, and the price of the selected land was collected from seven different real estates in the city, then the average price of each land was compared with the integration and connectivity value of the depth map.

The location of the commercial activities, shops, service buildings, and residential places was registered manually. The study used a sampling technique in geographical cluster sampling as the best sampling method for urban land price evaluations and market research. Then, each sample was stratified into three groups: high, average, and low land prices. See Figure 5.

The city involves 44 districts; see Figure 5. Kirkuk Housing Directorate and real estate are two primary resources for the collection of land price data. To find out if there was a difference between each data source or not, a statistical analysis was conducted. By the Kirkuk Housing Directorate 115 samples were selected, including 37 residential areas and 78 commercial areas, and on the real estate side 29 areas were selected, including 70 examples. The statistical results showed that land prices on the real estate side are higher than in the Kirkuk Housing Directorate side. However, this does not affect the study’s evaluation because each land plot in the real estate side is approximately USD 200 per square meter higher when compared to government prices. Therefore, as there are no differences between these two sources, the study used real estate as a basic resource for the land exchange. The research methodology is demonstrated in the following Figure 6.

4. Analysis

4.1. Land Types and Street Network Integration

The city of Kirkuk, like all other types of cities, consists of different urban land prices; the commercial activities are located in the city center along with other parts of the urban formation. The survey showed that the land’s location could directly impact its value; how far the land is from the main street in the urban spatial network is the primary determinant of the price of land. The connection between the residential area and street network is partial; therefore, the cost of the land allocated for housing is approximately between USD 250 and 450 per square meter, and the most accessible roads in the spatial network are colonized by commercial activities at USD 500–950 per square meter. For some land, the price is much higher; see

Table 1. Kirkuk land price categories based on the sampling. Resource Baitmustaqbal, Mahdi, Ajras, and Haitham real estates.

Kirkuk City Land Price Categories
Lowest price per m2 (USD 60–200)	Low price in m2 (USD 250–450)	Median price per m2 (USD 500–950)	Highest price per m2 (USD 1000–1500) and more
Amal Shaby (SA) Failaq (KD) Wahd Huzairan residential (JN) Wahid Azar residential (MR)	Azma (AR) Andalus residential part (AM) Tapa residential part (TA) Azadi (AZ) Iskan (KN) Bulaq residential (BH) Barutxana residential (BK) Musala residential (MU) Kornish residential (KR) Shorja residential (SJ) Qoria residential (KU) Baho (SB) Aulama residential (UM) Runaki residential (RM) Rizgary residential (RG) Alwahda (WH) Idara Mhalia residential (LA) Xarnata (GN) Mualimin (ML) Aruba residential (UR) Alnasir residential (NS) Khathra residential (KH) Adan residential (AD) Askary residential (RZ) Skek residential (RZ) Wahd Huzairan commercial (JN) Domiz residential (DZ) Wasit residential (WS)	Andalus commercial part (AM) Tapa commercial part (TA) Bulaq commercial (BH) Musala commercial (MU) Kornish residential (KR) Shorja commercial (SJ) Qoria commercial (KU) Aulama commercial (UM) Runaki commercial (RM) Rizgary commercial (RG) Mansur (MN) Aruba commercial (UR) Alnasir commercial (NS) Khathra commercial (KH) Adan commercial (AD) Askary commercial (RZ) Skek commercial (RZ) Wahid Azar commercial (MR) Domiz commercial (DZ) Wasit commercial (WS)	Jmhury street (JM) Mansur commercial (MN) Idara Mhalia commercial (LA)

.

The urban street network configuration is a significant determinant of movement flows [9]. Previous research has shown that direct spatial integration and accessibility between buildings and streets enhance walkability [36,37]. Street networks as a spatial configuration can provide an understanding of urban areas and verify urban quality and liveliness. Therefore, the land’s value can be predicted through the street network connection [26]. The closer land is to the main street, the higher is its price, and the farther away land is from the main road, the lower is its price. A study by [37] showed that shops and retail locations on the streets where most people move are more accessible. The main roads consist of commercial activities, which have the highest land price, while residential land lies in sub-roads with fewer movements; in those directions where the neighborhood buildings are far from the main street networks, the price of the land decreases.

Figure 7 explains the various districts’ commercial distribution in the city of Kirkuk, where the value of the land in that area enjoys a high degree of integration and accessibility as the land is the most expensive. Most Kirkuk commercials occur on the urban street network with car-based shopping places.

Hillier explains that the foreground route network comprises long streets, and the background routes’ network is primarily comprised of short streets [9]; he explained that most residential streets tend to be metrically short [38]. The Kirkuk urban plan does not share a similar logic of urban structure by structuring the residential quarters in the background of the main streets and keeping them at a lower degree of integration roads; therefore, the residential area indicates the lower price of land if it compares to commercial space. The differences in land prices among various regions and cities are related to morphological development and changing trends in land use that reflect the heterogeneity of cities in the area [39].

For example, the land cost is higher in Khathra districts which specialize in the commercial area, but the price decreases within roads deep in the residential area. The retail space, including car shows, is located on the main integrated street in the urban network. PinjAli neighborhood also showed the same results as for the land price evaluation; the lowest price is located in the more bottomless streets, while the highest price is encountered at the main road with a core of greater integration. The network circuit also varies substantially between the different types of places [10]. The longitudinal land integrates more with other global structure parts [16]. Figure 8 clearly shows that the main commercial horizontal street is located on the urban greater network integration core with the highest price of land, while the district’s residential area is in the deeper part with a lower integration core that has the lower cost of land.

4.2. Space Syntax Syntactic Analysis Most Related to the Land Price

Axial global integration is defined as integration values of axial lines at infinite radius, which can use to represent a picture of an integration pattern on an enormous scale. Hillier explained that axial integration analyses in spatial syntax involve analyzing how each axis line relates to all other lines for the total number of trend changes [32]. The integration which represents (I) of an axial line shows as (I) is a function of its topological depth associated with all other axes. The integration equation of an axial line is as below [40]:

$$Li=\frac{2(n\left(log2\left(\frac{n+2}{3}\right)-1\right)+1)/(n-1)(n-2)}{2\left(\right(\frac{{\sum }_{j}n=1^{dij}}{n-1})-1)/(n-2)}$$

• $Li$: total integration
• $n$: number of segments
• $dij$: the shortest distance between two segments of i and j.

An axial line is a straight and extended line with the possibility of walking or following on foot [41,42]. Integration is also known as a movement potential; it is the way of measurement in which an axial line connects to another or the other lines, such as shallow lines or deep-distributed (rings) or non-distributed lines. A space is integrated when all other areas of the urban environment are relatively shallow from it [8].

Global integration is the global measure, including the total depth, such as in long journeys and cars. The global integration of Kirkuk city shows how people behave in the city; the urban street networks are well-integrated, the most accessible and connected streets across the red lines and the yellow line lie one step after the red line, and the green and then blue lines are good for accessibility; see Figure 9.

The case study evaluation results show that the land’s highest value is in integrated roads. The Kirkuk city urban axial map shows that the city includes good potential planning as the urban spatial network is well integrated. Two types of street can be considered within the city’s physical form, these being irregular grids and linear extensions: the integration cores of some of the oldest parts of the town have a compact and distinctive form that is a continuous sub-structure from the other part of the city, and the oldest part of the routes grow similarly to branch from the main street and segregate inside the area, whereas the linear extensions connected as the primary routes of the city street network become a structure in the new urban spaces. It is notable that in the integration label value of urban network assessments, where there are the most integrated city streets indicates the highest value of the label. The urban integration label value shows that urban street networks are highly integrated because the segment street label values are very close (see Figure 10).

Figure 11 is the northern part of the city of Kirkuk. The local integration consists of residential lands, and the dark red spots represent the lowest integration values with low-priced land. In contrast, global integration includes commercial lands, and the orange spots show the highest integration value with land of a high cost.

Centrality is computed by considering the use of center lines to create the spatial network model [41]. In contrast to all other types of old city shapes, the syntactic center of Kirkuk city does not point only to the city center, which is the bazaar, it segregates into different parts of the city. The highest price of land is located on more compact street lines within the longest integrated street on the city’s network connection, for example, in Jemhuri, Atoba, and Parezga Streets (see Figure 12 and

Table 2. Land prices in some specific urban areas in Kirkuk city.

Kirkuk Land Price
Low Price (USD 250–450/m2)	Median Price (USD 500–950/m2)	High Price (USD 1000–1450/m2)	The Highest Price (USD 1500/m2) and More
Rahim Awa residential area Asra neighborhood residential area Ikhwan injection residential area Imam Abas Qoria center Old Tsen residential area Aumal residential area	Almas residential area Asra neighborhood commercial area Ikhwan injection commercial area Alquds residential area Old Tsen commercial area New Tsen area	Rahim Awa commercial area Almas commercial area Aumal commercial area	Atuba street Jemhuri street Alquds commercial street

).

The axial connectivity of Kirkuk city displays a potential connectivity with long lines in the existing urban plan; this value of connectivity approximately enhances the movement economy in all urban areas. A high connectivity means that the land will be of a high price as it provides to be excellent value to all of the properties around the line located on the main integrated streets (see Figure 13).

The longest street line can be determined by its line length, which can be highly defined in the urban network. According to previous research, the longest street improves the land value in a specific area, especially for commercial uses. Figure 14 identifies the mean depth; it shows that the city has a higher depth value because the urban city plan of Kirkuk involves some type of organic street shape and shows how it is difficult to reach, especially in the suburban area.

5. Space Syntax Results

According to the space syntax theory, aside from the global integration value evaluations of the space syntax, the interactions between the city’s local and global characteristics of urban systems are also interesting [8]. There is some interaction in the space syntax analysis methods; for example, the relationship between the node core and the mean depth are essential for evaluating the integration value:

Integration = node core/mean depth.

Subsequently, a low integration value is the best solution for the distribution of the urban system. The case study regarding axial global integration shows a low degree of integration, meaning that the node, which is the number of streets available in the urban area, is more than the depth, see

Table 3. Depth map axial line attribute summary.

Attribute Summary	Minimum	Average	Maximum
Integration	0.103816	0.315708	3.66667
Node count	1	179489	23296
Mean depth	1.3333	33.0419	143.712

.

Spatial integration is calculated based on the number of road direction changes from one to all others. The Kirkuk integration red and yellow lines show that the roads have the fewest number of direction changes over all other streets; as a result, those streets achieved the highest degree of spatial integration within the highest price of land occupied for commercial activities, as outlined previously (see Figure 9, Figure 10, Figure 11 and Figure 12).

The case study statistical analysis shows the more excellent relationship between integration, connectivity value, and land price, where the cost of land increases with the increase in the value of integration and connectivity (see

Table 4. Integration and land price statistical model summary of Kirkuk city.

Equation	Model Summary					Parameter Estimates
	R Square	F	df1	df2	Sig.	Constant	b1
Linear	0.239	14.794	1	47	0.000	−76.517	1395.968

The independent variable is integration. Dependent variable: average price. Sig = 0.000, the relation is significant.

and

Table 5. Connectivity and land price statistical model summary of Kirkuk city.

Equation	Model Summary					Parameter Estimates
	R Square	F	df1	df2	Sig.	Constant	b1
Linear	0.540	55.219	1	47	0.000	441.038	33.622

The independent variable is connectivity. Dependent variable: average price.

, and Figure 15 and Figure 16).

The statistical analysis shows that the values of integration connectivity are closely correlated with land price (see Figure 15 and Figure 16). However, there are some differences between these multi-collinearity variables. The scatterplot clearly shows that the higher the integration and connectivity value, the higher the probability of the land price, but some segregated points in different positions show differences between these values (see scatter plot Figure 17). Furthermore, it can indicate that space syntax premises may not provide the common factor that affects land prices. Additionally, the SPSS parameter illustrates that the relationship between integration, connectivity, and land price is a cubic nonlinearity correlation (see Figure 18, Figure 19, Figure 20 and Figure 21 and

Table 6. SPSS model summary and parameter estimation to identify the integration correlation type.

Equation	Model Summary					Parameter Estimates
	R Square	F	df1	df2	Sig.	Constant	b1	b2	b3
Linear	0.420	49.178	1	68	0.000	211.888	1590.335
Logarithmic	0.349	36.481	1	68	0.000	983.202	556.197
Inverse	0.268	24.924	1	68	0.000	930.674	163.571
Quadratic	0.557	42.105	2	67	0.000	1086.050	5285.186	8225.368
Cubic	0.701	51.660	3	66	0.000	2216.629	24,338.908	72,444.494	68,350.002
Compound	0.577	92.707	1	68	0.000	108.884	23.750
Power	0.524	74.757	1	68	0.000	1220.747	1.157
S	0.451	55.803	1	68	0.000	7.042	0.360
Growth	0.577	92.707	1	68	0.000	4.690	3.168
Exponential	0.577	92.707	1	68	0.000	108.884	3.168
Logistic	0.577	92.707	1	68	0.000	0.009	0.042

Model summary and parameter estimates. Dependent variable: Average price; The independent variable is integration.

and

Table 7. SPSS model summary and parameter estimator to identify the connectivity correlation type.

Equation	Model Summary					Parameter Estimates
	R Square	F	df1	df2	Sig.	Constant	b1	b2	b3
Linear	0.530	76.628	1	68	0.000	211.888	1590.335
Logarithmic	0.480	62.843	1	68	0.000	983.202	556.197
Inverse	0.313	31.050	1	68	0.000	930.674	163.571
Quadratic	0.530	37.769	2	67	0.000	1086.050	5285.186	8225.368
Cubic	0.532	24.976	3	66	0.000	2216.629	24,338.908	72,444.494	68,350.002
Compound	0.480	62.827	1	68	0.000	108.884	23.750
Power	0.484	63.894	1	68	0.000	1220.747	1.157
S	0.363	38.811	1	68	0.000	7.042	0.360
Growth	0.480	62.827	1	68	0.000	4.690	3.168
Exponential	0.480	62.827	1	68	0.000	108.884	3.168
Logistic	0.480	62.827	1	68	0.000	0.009	0.042

Model summary and parameter estimates. Dependent variable: Average price; The independent variable is integration.

), and that it has a high degree of R2 in both indicators.

Integration cubic nonlinear correlation R2 = 0.702.

Connectivity cubic nonlinear correlation R2 = 0.532.

Despite the correlation between integration, connectivity, and land price, it is essential to point out that it reveals a macro-level correlation, not causality at a micro-level, so we need to decipher the micro-level factors of land price indicators. For example, a small, segregated plot of land has a limited value and price because it is far from any transportation links or facilities, thus it has no clear access. The property value and price might increase if the location of the land is close to a popular city destination. Other property attributes, including building features, location characteristics, and neighborhood amenities, were explanatory variables [22]. The layout of public facilities, the planning and spatial location of transport, etc., greatly influence housing prices [28]. According to [32] (p. 134), quantifying, calculating, and visualizing spatial relationships becomes particularly useful when comparing these results with empirical data on socioeconomic activities. Connecting the results from space syntax analyses with the primary and secondary data of human activities provides new knowledge about the society–space relationship. In addition to the accessibility and location attributes of the land, the study focused on the availability of land services and infrastructure as attributes of land price.

According to [39], the intensity and scope of the interactions between urban land prices are positively correlated with the quality of the land prices and the degree of accessibility between cities, while the quality and distance of urban land prices are essential parameters for analyzing the relationship between urban land prices. Among all these common land economic problems, the multi-collinearity relationship is the basis for the estimate, as it can lead to transactions with opposite signs to the actual relationship. Therefore, the assessment of segregated points from Figure 22 provides a better understanding of the comprehensive impact factors on land price; see

Table 8. Samples and land price indicators under the use of space syntax premises.

Case Number	Samples	Land Price/USD	Integration Value	Connectivity Value	Affected Factors
1	Amal Shaby LR *	120	0.159	2
2	Barutkhana LR	270	0.189	2
3	Skek LR	280	0.367	4	Transcend lands decreased the price of land
4	1st Huzairan LR	290	0.294	1	Transcend lands decreased the price of land
5	Amal Shaby CR *	300	0.2965	3	Connectivity is high because the land is close to the highway
6	Bulaq LR	300	0.2982	2	Connectivity is low. The land is located in the complex city center on irregularly shaped roads.
7	Alwahda LR	300	0.3201	2
8	Aruba LR	300	0.3325	2
9	Alnasir LR	300	0.3586	3
10	Rzgari LR	310	0.3652	3	Low integration because the land is located on a cul-de-sac road
11	Khathra LR	320	0.3693	2	Low connectivity because it can be reached from 2nd local main road, not directly from the main road, and on the other side, there is no connection to the highway
12	Musala LR	350	0.4621	5	Low price with high integration and connectivity because the land is far from services (school) and close to the cemetery
13	Qoria LR	350	0.4722	3	Low connectivity because the land is not connected to the nearby way
14	Skek CR	350	0.4934	5	High connectivity because it has direct access to highway
15	Ausra Local Road	350	0.4393	1	High price with low integration and connectivity because the land is close to many services (as opposed to primary school, close to playground, and mosque)
16	Aulama Local Road	360	0.5008	9
17	1st Azar Local Road	360	0.4928	8
18	Bulaq CR	370	0.5009	3
19	Iskan LR	400	0.3447	3	High price, low integration, and connectivity because the land is close to the city center and includes many services (public garden and local garden, primary school, pharmacy, mosque, restaurant, and mall)
20	Barutkhana CR	400	0.4454	13	High price but low integration because the land is located on the main road between the two cities of Sulaimaniyah and Erbil
21	Adan LR	400	0.5197	4	High integration and low connectivity because the land includes few roads
22	1st Huzairan CR	400	0.5381	4
23	Ausra CR	420	0.5386	14
24	Askary LR	450	0.5461	13
25	Shorja LR	450	0.5009	18
26	Aulama CR	450	0.5593	7
27	Aruba CR	450	0.5599	10
28	Domiz LR	450	0.2169	3	High price, low integration, and connectivity because the land includes many services (primary and secondary school, offices, market, mosque, and mall)
29	Askary CR	460	0.5623	27
30	Musala CR	470	0.5415	5
31	Alwahda CR	470	0.5067	16
32	1st Azar CR	480	0.5389	21
33	Runaki LR	490	0.5008	9	High price with low integration and connectivity because the land includes many services (primary and secondary school, public garden, market, mosque, and 20 m street width
34	Qoria CR	500	0.5592	3
35	Baho LR	500	0.5473	3
36	Rzgari CR	500	0.5218	4
37	Xarnata LR	500	0.5271	3
38	Alnasir CR	500	0.5552	3
39	Domiz CR	500	0.5388	21
40	Rahim Awa LR	500	0.3255	5	High price with low integration because the land is located on the main road between Sulaymaniah and Erbil
41	Khathra CR	530	0.5491	3
42	Kornish LR	550	0.4081	2	High price with low integration because the land is located in the city center (all commercial)
43	Runaki CR	560	0.5584	6
44	Shorja CR	580	0.5554	3
45	Xarnata CR	600	0.5555	6
46	Adan CR	600	0.5389	21
47	Tsein LR	600	0.5344	3
48	Rahim Awa CR	600	0.4398	7	High price with low integration because the land is located on the main road between Sulaymaniah and Erbil Cities
49	Kornish CR	650	0.5474	3
50	Baho CR	650	0.5679	6	The land is close to the city center and includes more commercial areas
51	Idara Mhali LR	650	0.5188	3
52	Mansur LR	660	0.5516	3
53	Idara Mhali CR	700	0.5527	6
54	Aruba C *	700	0.5752	22
55	Alwahda C	790	0.5856	27
56	1st Huzairan C	790	0.5737	18
57	Rzgari C	800	0.587	26
58	Aulama C	850	0.5856	27
59	Skek C	850	0.5737	18
60	Bulaq C	900	0.5825	16
61	Baho C	900	0.5869	23
62	Runaki C	900	0.587	23
63	Ausra C	950	0.5824	32
64	Shorja C	1000	0.5825	16
65	Khatra C	1000	0.5737	18
66	Mansur C	1000	0.5782	30
67	Adan C	1100	0.5826	38
68	Domiz C	1100	0.587	26
69	Askary C	1200	0.5722	37
70	Kornish C	1500	0.5864	20

* LR = land located on a local road (generally used as residential land). * CR = land located on a collector road (is used as residential and sometimes used for commercial purposes). * C = land located on a commercial road.

(the samples identified in Figure 22).

6. Discussion

Land price variables include the environment, transportation, social and economic, and policy factors which provide a reference point for constructing urban residential land price factors [39]. Despite the effects of integration and connectivity on land prices as the central promises of space syntax, Table 8 shows a variety of social, economic, and environmental factors affecting land prices in relation to urban spatial plans, which are summarized below:

• Location: Land prices can vary greatly depending on the location within the city. Land close to main roads, transportation hubs, business districts, and desirable amenities such as parks, playgrounds, medical facilities, schools, and shopping areas is likely to be more expensive than land in less desirable locations because it reduces time consumption and increases economic benefits.
• Zoning regulations and land use policy: Zoning regulations can significantly impact land prices by limiting or encouraging development in certain areas. For example, in a particular area designated as a playground, park, or natural area, the land price may be higher than in other areas. In contrast, land closer to parking lots, heritage places, cemeteries, or government activities is lower in price. Additionally, the phenomenon of illegal houses affects land prices; normally, in the city of Kirkuk, illegally built houses are located on transected lands. Syntactic analysis of land price assessment found that suburban slums and segregated areas influence land value, which influences land prices as it has the lowest integration value and a low correlation with low land prices.
• Infrastructure: The availability and quality of infrastructure such as roads, water, and electricity can also impact land prices. Areas with good infrastructure may be more expensive because they are more desirable for development and easier to access in contrast to transcend lands that are lower in price.
• Economic conditions: Accessible land for economic activities, such as being close to retail and shops, increases land prices.
• Physical attributes: Land attribute impacts land price, for example, location quality, climate, and topography characteristics.

The actual issue of implementation is summarized in the

Table 9. Space syntax and urban spatial plan land price indicators.

Case Study	Space Syntax Premises Indicators	Urban Plan Indicators
		Social	Economy	Environment
Kirkuk	Integration Connectivity	School Mosque Hospital Park Playground Infrastructure Transport Shops and retails	Infrastructure Transport Shops and retails Close to the main roads (between Erbil, Sulaimaliyah, and Baghdad)	Street width Topography Climate

below:

All land price indicators resulting from space syntax analysis and urban spatial social, economic, and environmental factors are shown in

Table 10. Urban spatial land price indicators—Study results.

Space Syntax Land Price Indicators	Facilities and Infrastructure Land Price Indicators		Bid Rent Theory Land Price Indicators: How Far Is the Land from Commercial Activities?
x I = integration x C = connectivity	Positive impacts: x S = school x M = mosque x SW = street width x P = park (or playground) x H = health	Negative impacts: x - CC = close to a cemetery x - T = transcend lands	x SH = shop (retail or mall) x R = restaurant x OS = oil station x O = offices

Study results: (LP) land price indicators.

below. There are (13×) land price indicators, including (11×) positive indicators and (2×) negative indicators as the result of the study.

The statistical regression analysis of the space syntax showed that:

• F = 53.996 (p < 0.001)
• R2 = 60.6%.

The finding demonstrated that the space syntax premises including integration and connectivity highly correlated with the land price

Table 11. Results of linear regression to determine the effect of integration and connectivity on land price.

	Price Land	B	SE	t	p	Decision
Independent Variables
(Intercept)		−9.41	99.627	−0.094	>0.05	N. Sig
Integration		0.35	222.440	3.909	<0.001	H. Sig.
Connectivity		0.53	2.396	5.878	<0.001	H. Sig.

, therefore the urban spatial plan integration, and connectivity can be used to determine the urban land price, and as the main aim of the research, the study can predict the model for an urban spatial land price that can predict more than 60% price of urban land as R2 = 60.6%. The predicted model is as follows:

Y = β + b1 × 1 + b2 × 2

Y = β + 0.354 × 1 + 0.533 × 2

• Y = land price
• X1 = integration value
• X2 = connectivity value

As the result, the Urban spatial land price model and urban affected factors can predict approximately 100% of the urban land price.

7. Conclusions

Urban street networks can be used as a model to provide a unique analytical configuration that contributes to land price determination. Integration and connectivity are the two main premises of space syntax that help to assess the value and price of urban land. The results show that business activities located on the longest axial line, with a high degree of connectivity and integration, were where the land had the highest cost. Controversially, residential places in the densest urban areas with less integrated street networks were where land was the cheapest.

The study results confirm that the spatial network integration and connectivity with multi-collinearity factors, including the availability of service buildings and infrastructure, contribute to the urban land price; therefore, any improvement in urban performance will affect the value and price of urban land. The study showed that there is a need to link the city of Kirkuk with a new urban planning design or redevelopment design for the future sustainable urban design and the built environment; this can be done through the use of space syntax to control the spatial arrangement and street network of the city to control the urban land price fluctuation. Additionally, beneficiary sectors in Kirkuk can use the land price model indices to estimate the price of land in any desired location in the city. These results support the urban proposal that involves understanding the complexities of the urban spatial–functional interaction that can enhance the efficiency of urban performance in terms of the design and economic aspects, which aim to improve both social–economic conditions in urban areas. This result can be used in future urban spatial economic studies to create a new land pricing program based on space syntax premises.