Exploring the Coordination of Park Green Spaces and Urban Functional Areas through Multi-Source Data: A Spatial Analysis in Fuzhou, China

Abstract

The coordinated development of park green spaces (PGS)with urban functional areas (UFA) has a direct impact on the operational efficiency of cities and the quality of life of residents. Therefore, an in-depth exploration of the coupling patterns and influencing factors between PGS and UFA is fundamental for efficient collaboration and the creation of high-quality living environments. This study focuses on the street units of Fuzhou’s central urban area, utilizing multi-source data such as land use, points of interest (POI), and OpenStreetMap (OSM) methods, including kernel density analysis, standard deviational ellipse, coupling coordination degree model, and geographical detectors, are employed to systematically analyze the spatial distribution patterns of PGS and UFA, as well as their coupling coordination relationships. The findings reveal that (1) both PGS and various UFA have higher densities in the city center, with a concentric decrease towards the periphery. PGS are primarily concentrated in the city center, exhibiting a monocentric distribution, while UFA display planar, polycentric, or axial distribution patterns. (2) The spatial distribution centers of both PGS and UFA are skewed towards the southwest of the city center, with PGS being relatively evenly distributed and showing minimal deviation from UFA. (3) The dominant type of coupling coordination between PGS and various UFA is “Close to dissonance”, displaying a spatial pattern of “high in the center, low on the east-west and north-south wings”. Socioeconomic factors are the primary driving force influencing the coupling coordination degree, while population and transportation conditions are secondary factors. This research provides a scientific basis for urban planning and assists planners in more precisely coordinating the development of parks, green spaces, and various functional spaces in urban spatial layouts, thereby promoting sustainable urban development.

1. Introduction

PGS are the main component and construction focus of urban green spaces, providing recreational and service facilities for urban residents and serving comprehensive functions, such as ecological maintenance, environmental beautification, and disaster mitigation and refuge [1]. Early research on PGS focused on functional qualitative analysis and optimization strategies in areas such as landscape planning and design concepts [2], stormwater management [3], the cool island effect [4], and air quality [5]. Later, the focus gradually shifted to aspects such as PGS landscape patterns at the urban or metropolitan scale [6], green space value [7], functional effects [8], environmental quality [9], and supply–demand levels [10,11]. In recent years, research trends have shifted towards in-depth studies on fairness and justice [12], the socialization of health effects [13], and resilient ecological functions [14]. For example, Xihan Yao et al. quantified the threshold effect of physiological health benefits from green space exposure [15]. Fangzheng Li et al. explored the levels of recreational services in large urban parks and their influencing factors within urban clusters [16]. Nero et al. utilized methods such as the Gini coefficient to measure spatial equity evolution in green spaces comprehensively [17]. In related studies, the pattern of PGS is closely related to urban planning positioning, policy orientation, natural resources, and topographical factors. It is additionally affected by UFA, including residential distribution, traffic accessibility, and industrial layout [18,19].

UFA is the spatial projection of various urban functions, and land use methods and types are effective representations [20]. The theory of urban functional spatial layout originated in Western countries between the 1920s and 1940s, with Le Corbusier’s ideas on functional zoning, Saarinen’s “organic diffusion theory”, and the Chicago School of Ecology’s theories of “concentric zone, sector, and multiple nuclei” being classic contributions to the study of urban spatial structure. In recent years, scholars have focused on the differentiation characteristics of functional spaces [21], the division and identification of urban functional zones [22], and the optimization of urban functional spatial layouts while also gradually exploring their development processes, mechanisms, and effects [23]. As part of UFA, the degree and status of PGS’s association with other UFA can reveal the process of improving quality and efficiency within the city, as well as the level of green and sustainable development, leading scholars to increasingly focus on research related to the association between PGS and UFA. In empirical studies, some scholars have explored the spatiotemporal correlation characteristics between PGS and UFA [24] and the influence of UFA on green space patterns [25]. On the other hand, research from the perspective of single-type spaces, such as residential or transportation spaces, has focused on their interactions with PGS [26], with topics mainly centered around green space landscape patterns [27], spatial equity [28], accessibility [29], functional space identification [30], spatial characteristics [31], and the environmental effects of functional spaces [32]. Existing research primarily focuses on the relationship between single-type and PGS, with relatively less attention given to the overall coordination of UFA. As a result, studies on coordination and matching relationships lag behind practical development. Thus, further exploration is needed to study the coupled and coordinated development of PGS and UFA.

Coupling coordination model analyses the interactions between systems and their degree of coordination. It originally derives from the concept of “coupling” in physics, referring to two or more systems influencing each other through some connection. This model is commonly used to analyze the interactions between different urban systems (social, economic, ecological, etc.) and their degree of coordinated development. For example, Kun Cheng et al. used a dynamic coupling coordination model to comprehensively evaluate the coordinated development of urban water resource systems [33]. Yi Liu et al. studied a comprehensive method for the coupling coordination between urbanization and flood disasters [34]. Mo Wang et al. applied the coupling coordination model to evaluate the supply and demand of green stormwater infrastructure (GSI) [35]. Haojia Wang et al. used the coupling coordination degree model to study the coupling relationship between green finance and ecosystem service demand in China [36]. A typical supply–demand relationship exists between ecosystem services and urban spaces, prompting scholars to conduct multi-scale research on the relationship between urban development and ecosystem service value, examining how ecosystem service values respond to urbanization across different scales, including the national scale [37], urban agglomeration scale [38], and watershed scale [39]. However, there has been relatively little research on the coupling coordination between PGS and UFA.

In the early stages of rapid development in the southeastern coastal regions of China, production and service functions were the primary focus. Substantial economic growth led to spatial expansion and environmental degradation, while the role and value of PGS were not fully explored or utilized. In recent years, driven by the construction of ecological civilization and the planning of “park cities”, significant progress has been made in developing PGS, resulting in large-scale, systematic, high-quality green open spaces and urban–rural park systems. For example, Luo Chang et al., from an ecological perspective, developed a comprehensive evaluation index system for the supply and demand of PGS systems [40]. Jiang Jiayi et al., based on functional space identification, conducted a systematic evaluation of the spatial structure of Shanghai’s urban green space system, revealing the complex relationships between different functional areas and green spaces [24]. At the same time, with the widespread use of mobile signaling, points of interest (POI), and check-in data, research on UFA based on multi-source big data has become a hot topic among scholars domestically and internationally [41]. For example, Huang Yan et al. used multi-source data analysis from a multi-scale perspective to construct a framework exploring the spatial equity of PGS and its coupling coordination with socioeconomic deprivation (SED) [42]. Wu Xuan et al. used the geographical detector to reveal the spatial coupling patterns and characteristics of PGS and residential land from a spatiotemporal perspective and explored their influencing factors [43]. Zheng Zhuo et al. used multi-source data, the coupling coordination model and an expanded range of indicators to measure the overall supply–demand level of PGS in community units [44].

As urban planning shifts from expansion to stock management, the relationship between PGS and UFA in Fuzhou is restructuring to prevent spatial mismatches between PGS and UFA during stock planning and urban renewal processes. This study uses Fuzhou as a typical case, employing multi-source data such as land use data, POI, and OSM and applying methods like spatial clustering analysis and the geographic detector at the urban street scale to deeply reveal the coupling patterns and spatial characteristics of PGS and UFA, as well as to explore the key driving factors influencing the coupling coordination degree. The research provides a scientific basis for planning Fuzhou’s urban renewal and functional space optimization. It offers theoretical reference and practical guidance for other cities with similar rapid urbanization characteristics [45].

2. Study Area and Data Sources

2.1. Overview of the Study Area

Fuzhou City (25°15′–26°39′ N, 118°08′–120°31′ E) is located in the eastern part of Fujian Province, China, in the downstream region of the Min River and along the coastal areas. It is a first-tier city in China and serves as the political, economic, and cultural center of Fujian Province. The topography of Fuzhou is characterized by a typical estuarine basin, and it has a typical subtropical monsoon climate with a suitable temperature and warm and humid conditions. In 2020, Fuzhou was designated by the State Council as one of the central cities of the Economic Zone on the Western Coast of the Taiwan Strait and recognized as an eco-friendly garden city along rivers and the coast. By the end of 2023, Fuzhou had a permanent population of 8.469 million, a green space area of 13,861.07 hectares, with a per capita PGS area of 15.35 m²/person. The green coverage rate within the built-up area was 45.40%, and the green space ratio was 42.24%. This study selects the central urban area of Fuzhou (including Gulou District, Taijiang District, Cangshan District, and Jin’an District) for analysis (Figure 1). The high population density and economic activity in the central urban area create an urgent demand for PGS. Therefore, studying the coordination between PGS and other urban functional zones in this area can more accurately reflect the distribution and utilization of urban green spaces. This analysis not only helps assess the current urban ecological environment in Fuzhou but also provides robust support for future urban planning and green space layout optimization

Although this research focuses on Fuzhou, this analytical framework and method also apply to other cities with similar characteristics, particularly areas with dense populations, rapid urbanization, and the uneven distribution of green space resources. Similar cities can use the analytical tools from this research to evaluate the coordination between urban green spaces and functional spaces, optimize spatial layouts, and enhance ecological benefits. Therefore, by using Fuzhou as a case, this research also provides a replicable analytical approach for other cities, contributing to sustainable urban development and the optimization of green space layouts.

2.2. Data Collection

2.2.1. Spatial Type Data

POI data: points of interest (POI) is an open-access data type, with each POI data point containing four attributes: latitude and longitude, name, category, and address [46]. In this study, we obtained POI data from Amap, a navigation platform in China that is rich in geographic business data. Amap includes POI data categorized into 23 primary types, 261 secondary types, and 4705 tertiary types. This study refers to relevant research on the classification of UFA [47] and combines the “Classification of Urban Land Use and Planning Standards for Development Land (GB 50137-2011)” [48] to select five categories—residential, transportation, public service, leisure, and commercial—as UFA. The PGS data are primarily composed of PGS POI data, supplemented by land use data points converted from land use area data (G1 type in the “Classification Standard for Urban Green Space” (CJJ/T 85-2017) [49]. The integration of POI data with land use data ensures the comprehensiveness of PGS data. (

Table 1. Data types of PGS and UFA.

Primary Category	Secondary Category	Quantity/Units
PGS	PGS point of interest (POI) and land use point data	195
Residential space	Residential complexes	2719
Transportation space	Bus stations, parking lots, train stations, airports, long-distance bus stations, etc.	4367
Public service space	Hospitals, banks, schools, sports venues, government agencies, etc.	5120
Leisure space	Dining: Chinese cuisine, foreign cuisine, fast food, cafes, dessert shops, tea houses, etc.	29,465
	Shopping: Malls, commercial streets, markets, supermarkets, convenience stores, etc.
	Life services: Public offices, post offices, telecom business centers, laundromats, beauty salons, newspapers, intermediary agencies, etc.
	Entertainment: Cinemas, theaters, bars, chess rooms, internet cafes, KTV, game centers, etc.
	Sports and fitness: Stadiums, gyms, swimming pools, comprehensive sports centers, outdoor fitness venues, etc.
Commercial space	Companies, financial institutions, etc.	30,480

).

Administrative boundary data: The administrative boundary data are sourced from the Resource and Environment Science and Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 1 August 2024). Road Network Data: The road network data are sourced from OpenStreetMap’s open road data (https://www.openstreetmap.org/, accessed on 1 August 2024), and road parcels were generated using ArcGIS 10.8 technology. Based on the principles of topological analysis, redundant and minor roads were removed from the road network dataset, and primary road buffers were established. Finally, the road buffers were dissolved to produce unit parcels for Fuzhou.

2.2.2. Socioeconomic and Environmental Data

Socioeconomic and environmental data encompass a variety of crucial information sources that are essential for a comprehensive analysis of urban development. First, the socioeconomic data are sourced from the GDP grid dataset created by Zhao Naizhuo’s team (https://github.com/thestarlab/ChinaGDP, accessed on 3 August 2024), which provides information on the intensity and distribution of economic activity within urban areas. Nighttime light data obtained from the National Geophysical Data Center (NPP-VIIRS) (http://www.ngdc.noaa.gov/, accessed on 3 August 2024) are used to assess economic activity levels and urbanization by analyzing the intensity of urban lighting during nighttime. Population density data are derived from the Landscan Global Population Density spatial distribution data (https://landscan.ornl.gov/, accessed on 3 August 2024), offering critical insights into the population distribution within the study area. Additionally, land cover data provided by the Sentinel-2 Land Cover Explorer (https://livingatlas.arcgis.com/, accessed on 4 August 2024) are utilized to evaluate land use and ecological environment changes. Lastly, the Digital Elevation Model (DEM) data are sourced from the Geospatial Data Cloud (http://www.gscloud.cn, accessed on 4 August 2024), providing topographic information for the study area and supporting further spatial analysis and environmental assessment.

3. Research Methods

This study focuses on the central urban area of Fuzhou, analyzing the coupling coordination relationship between PGS and UFA in the region, as well as the influencing factors. First, PGS and UFA POI data for Fuzhou were obtained from the Amap open platform. These data were combined with multi-source data, including land use and administrative boundaries, to clean and organize the data, thereby constructing the spatial dataset required for the research. Second, the kernel density analysis method was used to reveal the spatial clustering characteristics of PGS and UFA within the study area. The standard deviation ellipse method was then employed to analyze the distribution direction and expansion trends of PGS and UFA. Third, the coupling coordination degree model was applied to calculate the coupling coordination degree between PGS and UFA, identifying different coordination types in various areas and visualizing their spatial differentiation characteristics. Finally, the geographical detector method was used to quantitatively analyze the main factors influencing the coupling coordination degree between PGS and UFA, exploring the role and impact of factors such as population density, transportation conditions, and socioeconomic status on the coupling coordination relationship. The flowchart of this study is shown in Figure 2.

3.1. Kernel Density Analysis

Kernel density estimation (KDE) is a method that calculates the density of features within their surrounding neighborhood to reflect distance decay patterns, converting discrete POI data into a continuous curve that reflects their spatial distribution. Thus, it is widely used across various disciplines. In this study, we applied a Gaussian kernel function to smooth the spatial data, and the bandwidth parameter was selected using Silverman’s rule of thumb. This bandwidth selection method can determine the optimal smoothing parameter based on the data distribution characteristics, ensuring that the density estimation reveals the spatial patterns while avoiding over-smoothing. KDE analysis estimates the density of POIs in UFA and park cities. The formula used to perform the kernel density analysis is as follows:

$$f(x,y)={\displaystyle \frac{3}{n{h}^2\pi }}{{\displaystyle \sum _i^n\left[1-\frac{{(x-x_i)}^2-{(y-y_i)}^2}{h^2}\right]}}^2$$

(1)

where f(x, y) represents the kernel density index at location (x, y); h denotes the search radius; x_i and y_i are the coordinates of the sample point i; and n is the number of sample points contained within the search bandwidth at location (x, y).

3.2. Standard Deviation Ellipse

Standard deviation ellipse (SDE) analysis is a spatial statistical method used to detect the distribution patterns and development trajectories of sample data. It accurately reveals various characteristics of urban spatial distribution, such as centrality, spread, and directionality. By measuring parameters such as the elliptical center, major axis, minor axis, and azimuth angle of geographic features, this method characterizes the directional shifts and distribution trend evolution of PGS and UFA [50]. The formulas used to perform standard deviation ellipse analysis are as follows:

Mean center：

$$\overline{X}={\displaystyle \frac{{\displaystyle \sum _{i=1}^n}{w}_i{x}_i}{{\displaystyle \sum _{i=1}^n}{w}_i}},\overline{Y}={\displaystyle \frac{{\displaystyle \sum _{i=1}^n}{w}_i{y}_i}{{\displaystyle \sum _{i=1}^n}{w}_i}}$$

(2)

Azimuth angle:

$$\tan \theta ={\displaystyle \frac{\left({\displaystyle \sum _{i=1}^n}{w}_i^2\overline{x_i^2}-{\displaystyle \sum _{i=1}^n}{w}_i^2\overline{y_i^2}\right)+\sqrt{{\left({\displaystyle \sum _{i=1}^n}{w}_i^2\overline{x_i^2}-{\displaystyle \sum _{i=1}^n}{w}_i^2\overline{y_i^2}\right)}^2+4{\displaystyle \sum _{i=1}^n}{w}_i^2\overline{x_i}\overline{y_i}}}{{\displaystyle \sum _{i=1}^n}2w_i^2\overline{x_i}\overline{y_i}}}$$

(3)

Standard deviation in the X-axis:

$${\sigma }_x=\sqrt{{\displaystyle \frac{{\displaystyle \sum _{i=1}^n}{\left(w_i\overline{x_i}\cos \theta -w_i\overline{y_i}\sin \theta \right)}^2}{{\displaystyle \sum _{i=1}^n}{w}_i^2}}}$$

(4)

Standard deviation in the Y-axis:

$${\sigma }_y=\sqrt{{\displaystyle \frac{{\displaystyle \sum _{i=1}^n}{\left(w_i\overline{x_i}\sin \theta -w_i\overline{y_i}\cos \theta \right)}^2}{{\displaystyle \sum _{i=1}^n}{w}_i^2}}}$$

(5)

3.3. Coupling Coordination Degree Model

The coupling coordination degree model is a tool used to address the interactions and mutual influences among multiple subsystems within complex systems. This model is primarily applied to study and optimize the coordination relationships among several interrelated decision variables and objective functions. In this research, a coupling coordination degree model is constructed to calculate the coupling coordination degree between PGS and urban functions, thereby assessing the level of coordinated development among the subsystems. A higher coupling coordination degree indicates a higher level of coordinated development among the subsystems [51]. Based on the relevant literature [52], the coupling coordination degree (D) is divided into five levels, as shown in

Table 2. Classification criteria for coupling coordination degree levels.

D Value Interval	Degree Levels	Degree of Coupling Coordination
(0.0, 0.2]	1	Moderate disorders
(0.2, 0.4]	2	Close to dissonance
(0.4, 0.6]	3	Primary coordination
(0.6, 0.8]	4	Intermediate coordination
(0.8, 1.0]	5	Well coordinated

.

$$C={\left[{\displaystyle \frac{{\displaystyle \prod _{n=1}^n}{U}_i}{\left(\frac{1}{n}{\displaystyle \sum _{i=1}^n}{U}_i\right)}}\right]}^{{\displaystyle \frac{1}{n}}}=\sqrt{{\displaystyle \frac{U_1{U}_2}{{\left(\frac{U_1+U_2}{2}\right)}^2}}}={\displaystyle \frac{2\sqrt{U_1{U}_2}}{U_1+U_2}}$$

(6)

$$T=\sum _{i=1}^n{\alpha }_i\times U_i,\sum _{i=1}^n{\alpha }_i=1$$

(7)

$$D=\sqrt{C\times T}$$

(8)

where D represents the coupling coordination degree, C represents the system coupling degree, T is the comprehensive evaluation value of the system, and variables U₁ and U₂ represent the indicators of PGS and UFA, respectively. We used the min–max normalization method to standardize the data to the [0, 1] range. The specific formula is as follows:

$$X^{\prime }={\displaystyle \frac{X-X\min }{X\max -X{\min }^{\prime }}}$$

(9)

where X is the original data value, and Xmin and Xmax are the minimum and maximum values of the variable in the dataset, respectively. In this way, all variables are transformed to the same scale, ensuring comparability in the analysis between different systems. α and β are undetermined coefficients, with α + β = 1. This study considers the two systems, PGS and UFA, as being equally important; therefore, α = β = 0.5.

3.4. Geographical Detector

This paper primarily utilizes factor detection and interaction detection to explore the driving factors of the coupling coordination degree between PGS and UFA, identifying the influencing factors of high coupling coordination based on the results of geographic detector analysis. The geographic detector is a statistical model that detects spatial stratified heterogeneity and reveals its driving forces. It can be used to analyze the relationships between variables without relying on linear assumptions or variable collinearity [53]. This study uses the geographic detector to identify the contribution of each influencing factor quantitatively. The calculation formula is as follows:

$$q=1-{\displaystyle \frac{{\displaystyle \sum _{h=1}^L}{N}_h{\sigma }_h^2}{N{\sigma }^2}}$$

(10)

where q represents the explanatory power of the factor or factor interaction, with a range of [0, 1], indicating the degree of coupling coordination between PGS and UFA. A higher q-value indicates a more substantial explanatory power of factor X on the results; conversely, a lower q-value indicates a weaker explanatory power. h represents the level of coupling coordination between PGS and UFA or the zoning of influencing factors; N_h and N are the sample sizes of PGS and the entire sample, respectively; and σ_h and σ² represent the variance of PGS and the overall sample, respectively.

The interaction of the geographic detector is mainly used to study the interaction effects between different influencing factors. In this study, interaction analysis is used to evaluate the combined effect of factors X1 and X2 on the coupling coordination degree between PGS and UFA, revealing their overall influence, either enhancing or weakening the impact on the coupling coordination degree Y, denoted as q(X1⋂X2). The evaluation method involves first calculating the q-values of factors X1 and X2 on Y: q(X1) and q(X2), and then calculating their interaction q-value: q(X1⋂X2), followed by a comparison of q(X1), q(X2), and q(X1⋂X2). There are five types of factor interactions (

Table 3. Geographical detector interaction types.

Condition	Interaction Type
q(X1⋂X2) < Min(q(X1),q(X2))	Nonlinear weaken
Min(q(X1),q(X2)) < q(X1⋂X2) < Max(q(X1)), q(X2))	Univariate nonlinear weaken
q(X1⋂X2) > Max(q(X1),q(X2))	Bivariate enhancement
q(X1⋂X2) = q(Xl) + q(X2)	Independence
q(X1⋂X2) > q(Xl) + q(X2)	Nonlinear enhancement

).

4. Results and Analysis

4.1. Characteristics of PGS and UFA Clustering

According to the spatial kernel density analysis, PGS and UFA in the central urban area of Fuzhou show a clear pattern of spatial clustering (Figure 3). Overall, PGS and various functional spaces display high density in the city center, with a concentric, gradually decreasing distribution pattern toward the periphery, forming a spatial structure centered around Gulou and Taijiang, with attenuation toward the outskirts. Specifically, PGS are distributed mainly in an isolated single-core form, with the high-density core concentrated around Wushan Street, Yangqiao Street, and Bayi Seven Street in the Gulou District. The distribution of PGS in this area is closely related to natural topographical conditions, leveraging famous tourist spots like Sanfang Qixiang and Wuyi Square, creating significant natural ecological spaces such as Wushan Scenic Area and Yushan Scenic Area.

The high-density values of residential spaces are prominently observed in the Gulou, Taijiang, Cangshan, and Jin’an districts, with the highest density values distributed primarily along Binjiang Avenue, indicating a strong correlation between residential spaces and the riverside region.

Transportation spaces and public service spaces are mainly distributed along a north–south axis, forming a relatively continuous planar structure. A primary high-density cluster of transportation spaces and public service spaces forms around Wusi Road, Hualin Road, and Jin’an South Road. In contrast, a secondary high-density cluster is found near Wuyi Middle Road and Guohuo Road. This spatial overlap reflects the close relationship between transportation spaces and public service spaces in meeting daily travel needs.

The clustering effect of leisure spaces is quite pronounced, with high-density areas mainly distributed around urban PGS and along the Minjiang River. High-density cores are formed near Niugangshan Park, Jin’an Lake Park, and Binjiang Park. Meanwhile, secondary high-density clusters have formed around Xihu Park, Pingshan Park, and Minjiang Avenue. This distribution pattern indicates that the layout of leisure spaces in Fuzhou’s central urban area is closely related to urban PGS and water resources, meeting the recreational needs of residents.

Commercial spaces are mainly concentrated along Wusi Road, Yangqiao Road, and Binjiang Fourth Avenue, forming several commercial clusters of varying sizes. This distribution pattern reflects the strong driving force of high population density and robust consumer demand in the central urban area to develop commercial spaces [54].

4.2. Directional Distribution Characteristics of PGS and UFA

Using the standard deviation ellipse (SDE) method, the directional distribution characteristics of PGS and various UFA in the central urban area of Fuzhou were analyzed from three perspectives: central tendency, axial trend, and expansion trend (Figure 4). The analysis revealed that both PGS and UFA have spatial centers that deviate to some extent from the city’s center (119°18′18″ E, 26°3′53″ N), with both generally skewed towards the southwest. In terms of deviation distance, the order of deviation from the city center, is as follows: commercial space > leisure space > public service space > transportation space > residential space > PGS. The commercial space showed the greatest deviation at 13.30 km, while the PGS exhibited the smallest deviation at 11.784 km, indicating a relatively balanced distribution of PGS compared to other UFA and a closer alignment with the city center.

Further analysis using the SDE method provided basic data on the standard deviation ellipses of PGS and UFA (

Table 4. Basic data of standard deviation ellipses for PGS and UFA.

UFA	Longitudes/°E	Latitude/°N	Short Axis/km	Long Axis/km	Angle of Rotation/(°)	Area/km2	Perimeter/km
PGS	119.302	26.077	6.722	7.843	179.062	133.058	41.061
Residential space	119.304	26.075	4.627	5.905	170.153	85.843	33.211
Transportation space	119.305	26.070	5.081	6.473	162.036	103.335	36.432
Public service space	119.303	26.072	4.624	6.310	165.594	91.663	34.555
Leisure space	119.303	26.067	5.124	6.557	169.867	105.535	36.833
Commercial space	119.303	26.065	5.254	6.746	170.113	111.335	37.843

). The ratio of the major axis to the minor axis for all spaces was greater than 1, with the highest ratio observed in public service space (1.365), indicating a strong clustering trend and significant spatial centripetal force. In contrast, PGS had the smallest ratio (1.167), suggesting a more balanced and dispersed distribution. Overall, the distribution axes of PGS and UFA tend to extend in a north–south direction. Regarding the expansion trend, the coverage area of the standard deviation ellipse was largest for PGS, followed by commercial space, leisure space, transportation space, public service space, and residential space. The PGS demonstrated a strong trend of dispersed distribution, characterized by an outward-oriented and balanced overall layout.

4.3. Analysis of the Coupling Coordination Relationship between PGS and UFA

4.3.1. Spatial Differentiation Patterns and Coupling Coordination Degree Classification

The degree of coupling coordination between PGS and UFA in the central urban area of Fuzhou shows distinct spatial differentiation characteristics. The spatial visualization results, generated using ArcGIS 10.8 software, are shown in Figure 5. The degree of coupling coordination decreases spatially from the central areas of the Gulou and Taijiang districts toward the periphery. Areas with higher coupling coordination degrees are primarily concentrated in the urban center, where transportation advantages are prominent, core functional areas are concentrated, and public infrastructure is well developed, while lower coordination degrees are found in the peripheral areas far from the city center. The calculation results of the coupling coordination degree model, shown in Table 2, reveal different distribution characteristics of the coupling coordination degree between PGS and various functional spaces at the street level (Figure 6). Overall, the near-imbalance type dominates. Streets with good coordination are few, with the highest proportion in residential spaces (2.44%), followed by transportation spaces (1.80%), public service spaces (1.49%), leisure spaces (0.42%), and commercial spaces (0.11%).

The distribution of streets with intermediate coordination shows that residential spaces (15.71%) still have the highest proportion. In comparison, commercial spaces (3.93%) have the lowest. The proportion of streets with primary coordination is relatively high, with residential spaces (34.39%) and transportation spaces (34.18%) ranking highest. In comparison, commercial spaces have the lowest proportion (17.73%). In the near-imbalance type, commercial spaces (58.28%) have the highest proportion of streets, followed by transportation spaces (57.86%) and public service spaces (51.06%). In the moderate imbalance type, streets with commercial spaces have the highest proportion, reaching 63.06%.

4.3.2. Trend Analysis of Spatial Differentiation

This study conducted an in-depth trend analysis of the coupling coordination degree between PGS and functional spaces in Fuzhou using the trend surface analysis method, further investigating its spatial differentiation characteristics. As shown in Figure 7, the trend lines of the coupling coordination degree exhibit steep changes in both east–west and north–south directions, indicating significant spatial differentiation. Specifically, the coupling coordination degree shows a spatial pattern of being “high in the middle and low on the east-west and north-south wings”. This spatial pattern suggests that the coupling coordination between PGS and UFA is higher in the central urban area but relatively lower in the east–west and north–south wing regions.

The coupling coordination degree in the southern region is consistently higher than in the northern region due to the well-developed economy, abundant resources, and well-established infrastructure of the old Gulou district in the south. Additionally, the southern region’s mature level of regional development has attracted a large population, further increasing the demand for and utilization of PGS. In contrast, due to its higher elevation and abundant natural forestry resources, the northern region has a sparse population, which has limited the development of UFA. As a result, the degree of coupling coordination between PGS and UFA is lower.

4.4. Influencing Factors of the Coupling Coordination Degree between PGS and UFA

The coordinated development of PGS and various UFA results from the combined influence of socioeconomic factors, population distribution, transportation conditions, natural geographical environment, and land development intensity [55]. Based on previous research [56] and considering the development status of Fuzhou’s central urban area and the nature of PGS and UFA, this study selects seven indicators as influencing factors of the coupling coordination degree between PGS and UFA: DEM (X1), population density (X2), GDP (X3), nighttime light index (X4), topographic relief (X5), road network density (X6), and land development intensity (X7).

Among these, the spatial homogeneity of GDP (X3) requires further clarification. A spatially homogeneous GDP implies no significant differences in its impact across various urban areas [57]. However, in Fuzhou’s central area, economic activity is highly concentrated, making GDP’s impact more pronounced in these regions, while economic development in peripheral areas lags, potentially reducing GDP’s influence. Therefore, GDP is considered an influencing factor due to economic disparities between central and peripheral areas. The natural breaks classification method was used to divide the primary influencing factors into nine levels for discretization [58,59]. The geographic detector was applied to calculate the explanatory power of the main factors influencing the degree of coupling coordination between PGS and UFA.

4.4.1. Analysis of Influencing Factors of Coupling Coordination Degree

As shown in

Table 5. Geographic detection results of the spatial coupling and coordination between parks and green spaces and urban functions.

UFA	X1	X2	X3	X4	X5	X6	X7
Residential space	0.426	0.651	0.778	0.429	0.300	0.611	0.573
Transportation space	0.370	0.639	0.742	0.457	0.574	0.625	0.467
Public service space	0.334	0.688	0.724	0.424	0.521	0.651	0.475
Leisure space	0.345	0.522	0.721	0.354	0.464	0.627	0.340
Commercial space	0.459	0.679	0.721	0.427	0.449	0.621	0.654

, all influencing factors passed the 1% significance level test. The explanatory power of different factors on the coupling coordination degree between PGS and UFA varies significantly, further confirming the spatial heterogeneity of GDP’s influence between central and peripheral areas. According to the data analysis in Table 3, GDP (X3) has the strongest explanatory power for the coupling coordination degree between PGS and UFA, with q-values greater than 0.721. This reflects the central role of economic development in the allocation of UFA, indicating that economic prosperity influences the layout of public service spaces, residential spaces, and the agglomeration of commercial and leisure spaces [60].

Next, the q-values for road network density (X6) and population density (X2) are above 0.611, highlighting the importance of these factors in the coordinated development of urban spaces. This indicates that transportation conditions and population distribution significantly impact the spatial coordination of urban development. In the Gulou and Taijiang districts, the efficient allocation of population density and transportation networks has promoted a rational distribution of UFA, further advancing the coordination between PGS and functional spaces. Land development intensity (X7) and the nighttime light index (X4) also significantly affect the coupling coordination degree of residential and commercial spaces, reflecting how urban development and nighttime economic activities influence demand for these spaces.

In contrast, the distribution of PGS is more influenced by ecological and social benefits, with economic factors playing a lesser role. Therefore, urban planning must balance ecological, social, and economic benefits to achieve sustainable urban spatial development. Comparatively, the q-values for DEM (X1) and topographic relief (X5) are relatively low, primarily because the terrain in Fuzhou’s central urban area is flat, making development easier and limiting the impact of topographical factors on the distribution of UFA. These results highlight the significant differences in how the seven influencing factors explain the coupling coordination degree between PGS and various functional spaces in Fuzhou’s central urban area, revealing the complex effects of various spatial factors on urban space layout. Urban planning must comprehensively consider these critical factors to optimize UFA configuration.

4.4.2. Interaction Analysis of Influencing Factors on Coupling Coordination Degree

To gain deeper insights into the complex influencing factors influencing the coupling coordination degree between PGS and UFA [61], interaction analysis was introduced to explore the combined effects of different factor combinations on the coupling coordination degree, revealing their overall influence. As shown in Figure 8, the results show that different UFA exhibit varying levels of dependency on influencing factors (Table 5). Specifically, transportation and public service spaces are most dependent on GDP and road network density, with the combined effect of population density and the transportation network being key factors in determining the coupling coordination degree of functional spaces. In densely populated areas, the accessibility of the transportation network directly influences the layout and development of functional spaces. The interaction between GDP and the nighttime light index has the most significant effect on the coupling coordination degree of commercial and residential spaces, indicating that the intensity of economic activity and the development of the nighttime economy play key roles in these two space types. In leisure and entertainment spaces, the interaction between topographic relief and GDP significantly affects their coupling coordination degree, indicating that topographical features should be noticed in the layout of leisure and entertainment facilities. For example, the flat terrain in southern Fuzhou is conducive to concentrated development.

In contrast, the higher terrain in the north limits the development of some functional spaces. However, it also provides unique natural advantages for leisure facilities. Hence, future planning should consider topographical features to ensure a coordinated layout of leisure spaces and PGS.

5. Discussion

5.1. Spatial Patterns

Through an analysis of the spatial patterns of PGS and various UFA in Fuzhou’s central urban area, the results show that PGS and different functional spaces exhibit high density in the city center: a ring-like, gradually decreasing distribution toward the periphery. This finding aligns with the results of Lian, ZX et al. [62], reflecting a typical spatial expansion pattern seen during urban development. The primary function of PGS is to provide leisure, recreation, and greening services for urban residents, typically concentrated in city centers or near large Residential spaces to maximize service provision. As a result, the distribution of PGS tends to be more concentrated and balanced, forming a single core, while other UFA, to accommodate broader economic activities and social needs, exhibit multi-core or planar distributions [63]. Fuzhou’s PGS layout pattern may have overlooked the green space needs of residents in peripheral areas, leading to an inequitable distribution of ecological and social benefits for these residents. Therefore, future research should further evaluate the impact of this layout on urban sustainable development [64].

5.2. Coupling Coordination Degree

Through the analysis of coupling coordination development, it was found that Fuzhou’s main type of coordinated development is “Close to dissonance”. This indicates that during the planning and design process, the layout of PGS did not fully consider the comprehensive needs of transportation space, public service space, and leisure space, resulting in the distribution and actual utilization of PGS falling short of expectations. This planning imbalance may stem from the need for overall coordination in the traditional urban development model’s zoning of functional spaces, hindering the synergy between different functional areas, thus limiting PGS from fully realizing its ecological, economic, and social benefits. Further analysis suggests that the coordination between PGS and residential space is relatively high, mainly due to urban residents’ demand for an excellent natural environment, the rapid growth of China’s real estate market [43], and government policy interventions. During real estate development, PGS is often prioritized as a critical resource for improving the living environment, attracting homebuyers, and increasing regional value. The government has also implemented a series of policies encouraging the incorporation of green space in real estate projects to improve the urban environment and enhance residents’ quality of life [65]. The government has also implemented a series of policies encouraging the incorporation of green space in real estate projects to improve the urban environment and enhance residents’ quality of life [66,67]. However, the coordination between PGS and commercial space, transportation space, and leisure space is significantly lacking. This phenomenon can be attributed to several internal factors, with the difference in priority given to economic-driven planning being one of the main reasons. Commercial space and transportation space typically prioritize economic activities, focusing on economic efficiency and benefits, often relegating ecological considerations to secondary importance. Commercial space and transportation space require extensive infrastructure and land, compressing the layout of green spaces and preventing them from being adequately integrated into these functional spaces [42]. On the other hand, although the government imposes strict requirements for green space allocation in residential space, the standards for green space planning in commercial space and transportation space are more relaxed, leading to ecological needs being overlooked in these areas during planning, resulting in a disconnect between them and PGS [68].

Additionally, the need for coordination between PGS and leisure space arises from overlapping and differentiating functional roles. At the same time, both share similar functions to some extent, but they differ in specific functional focuses. PGS focuses more on natural ecological functions, offering green environments and open spaces [68]. At the same time, leisure space often includes more commercialized and facility-intensive entertainment projects such as shopping malls, cinemas, and gyms. Therefore, in urban planning, leisure space may prioritize commercial and economic benefits, neglecting organic integration with PGS, resulting in a low level of functional integration between the two [69]. Hence, future urban planning and governance should emphasize the organic integration of PGS with various functional spaces to promote sustainable urban development.

5.3. Driving Factors

After describing the spatial patterns of PGS and UFA, it is necessary to conduct a more in-depth analysis of the driving factors behind these patterns, revealing the complex impacts of economy, population, transportation, and natural terrain on the coupling coordination degree. This study shows that the economic development level (GDP) is central to the degree of coupling coordination between PGS and UFA. This phenomenon reflects the fundamental logic of market-driven urban spatial allocation, where areas with high economic activity tend to have more resources and better facilities, forming core regions with high coupling coordination degrees [70]. On the other hand, the coupling of PGS and UFA is supported by population resources, guided by crucial transportation conditions, and catalyzed by natural terrain. Population density directly affects the layout of public service space and commercial space [71]. The high population density in Fuzhou’s central urban area has facilitated the coordinated development between PGS and functional spaces. Transportation conditions, especially in commercial spaces and transportation hubs, serve as a critical guiding factor, where an efficient transportation network enhances the connectivity between functional spaces. Natural terrain significantly affects the layout of leisure space. Complex terrain limits large-scale development but provides ideal leisure and natural landscape conditions. Therefore, future planning should integrate population density to rationally allocate green space and public service space, facilitating their integration with commercial and residential space. At the same time, strengthening the connectivity of transportation networks will improve the linkage between functional spaces. Natural terrain should be utilized to plan green spaces in a way that is tailored to the local context, ensuring ecological protection. Additionally, this study used streets as the research scale, further refining the research unit [72]. Based on the analysis of the coupling coordination relationship between PGS and UFA, this study also examined the influencing factors of the coupling coordination degree, expanding new research dimensions and directions for the layout and coordination of other UFA.

6. Conclusions and Outlook

This study takes the main urban area of Fuzhou as a typical case, utilizing spatial type data, socioeconomic data, and environmental data, combined with spatial pattern analysis and the geographic detector method, to reveal the spatial pattern, coupling coordination degree, and influencing factors of PGS and UFA at the street scale. The research methods and conclusions provide practical references for other cities with similar characteristics. The main conclusions are as follows:

(1)

Spatial clustering characteristics: Both PGS and UFA in the central urban area of Fuzhou show distinct spatial clustering characteristics, with high density in the city center and a concentric, gradually decreasing pattern toward the periphery. PGS is primarily distributed in a single-core form, concentrated in the city center and densely populated areas, while UFA present planar, multi-core, or axial distributions, adapting to the diverse demands of urban functions and spatial development strategies.

(2)

Spatial distribution centers and expansion trends: The spatial distribution centers of PGS and UFA are skewed towards the southwest of the city center. In terms of deviation distances, the order from most to most minor deviation is commercial space > leisure space > public service space > transportation space > residential space > PGS. Regarding expansion trends, the coverage area of the standard deviation ellipses decreases in the following order: PGS > commercial space > leisure space > transportation space > public service space > residential space. In future urban planning, other cities can adopt similar methods to analyze the distribution centers and expansion trends in PGS and UFA, optimizing the distribution structure of functional areas and green spaces to meet the diverse needs of functional spaces better.

(3)

Coupling coordination development type: The coupling coordination development type between PGS and UFA primarily shows a “Close to dissonance” pattern. The coupling coordination degree shows a spatial pattern of “high in the center, low on the east-west and north-south wings”, with higher coordination in Gulou District and Taijiang District, and lower coordination in the northern part of Jin’an District, the southern part of Cangshan District, and the eastern and western edge areas.

(4)

Influencing factors analysis: Socioeconomic levels, population distribution, transportation conditions, and land development intensity significantly impact the coupling coordination development between PGS and UFA. Among these, socioeconomic levels have the most muscular explanatory power on the coupling coordination degree, highlighting the critical role of economic development in optimizing urban spatial structure. Road network density and population density are secondary influencing factors. Similar cities can adopt the methods used in this study, utilizing multi-source data to analyze their urban spatial patterns, optimize the coordinated development of functional areas, and achieve sustainable urban development.

This study mainly focused on the static spatial patterns and coupling coordination degree of PGS and UFA, revealing the current spatial differentiation characteristics and influencing factors. However, there are limitations in the following areas:

(1)

Lack of time dimension: This study did not consider the dynamic changes in PGS and UFA over different historical periods. In the future, historical data could be introduced, and time-series analysis could be used to explore the spatiotemporal evolution patterns and changes along with urban development.

(2)

Insufficient comprehensive analysis of multidimensional factors: Although this study analyzed several key influencing factors, it did not cover other factors such as policy planning, ecological conditions, and residents’ needs. Future research could expand the range of influencing factors, using more complex models, such as multiple regression or machine learning methods, for multidimensional comprehensive analysis.

(3)

Accuracy of POI data and selection of spatial analysis parameters: POI data may have untimely updates and incomplete data, which could affect the analysis results. In the future, remote sensing imagery and field surveys can be used to cross-validate the data to improve its reliability. Sensitivity analysis can also be used to optimize the selection of spatial analysis parameters and reduce bias.