Optimizing Green Spaces Significantly Improves Wind Environment and Accessibility in County Towns

Abstract

With the increasing frequency of extreme disasters, effectively utilizing park green spaces in both daily life and disaster scenarios has emerged as a new challenge, particularly in county-level cities. In this context, the core planning area of Anxi County in Quanzhou was selected as the study site. By adjusting the layout and scale of park green spaces, this research investigates how such modifications influence the quality of the urban wind environment and green space accessibility in county-level cities. The results show the following: (1) Under the vegetation ratio of trees–shrubs–herbaceous plants, the ventilation performance of the urban wind environment improved at a daily wind speed of 5 m/s. The wind speed increased from the current low base speed (0–1 m/s) to a moderate speed (2–5 m/s), significantly enhancing the comfort of the population. (2) Under the vegetation ratio of trees–shrubs–herbaceous plants, the overall disaster-prevention performance of the county improved. During typhoon wind speeds (50 m/s), the wind speed gradually decreased from partially higher speeds (40–50 m/s) to lower speeds (10–20 m/s), resulting in a significant improvement in the wind environment. (3) After optimizing the layout of park green spaces, accessibility was greatly enhanced, better meeting the needs of the population in the developed area.

1. Introduction

The rapid development of urbanization has led to many challenges for the sustainable development of cities, among which the lack of urban green space has become one of the urgent problems to be solved. Urban green spaces provide a wide range of ecosystem services with many recognized economic and ecological benefits. It can help fight urban diseases and improve the lives and health of urban residents [1]. Numerous studies have demonstrated the multifunctionality as well as the economic, ecological, environment, recreation, and health benefits of urban green spaces, including but not limited to improving air quality, mitigating urban heat island effects, maintaining biodiversity, enhancing esthetic values, and promoting people’s physical and mental health and spiritual well-being [2,3]. In addition, the role of urban parks in disaster prevention and avoidance should not be underestimated. To ensure the ecological safety of cities and promote the sustainable development of human and natural systems, urban parks and open green spaces are of strategic importance to the quality of life of our increasingly urbanized societies [4]. The reduction in urban green space can significantly impact the sustainable development of cities, adversely affect the well-being of both people and nature, and threaten the overall living environment.

With the rapid development of cities worldwide and the high concentration of people, materials, and facilities, densely populated metropolises exhibit high vulnerability to earthquakes, floods, fires, and other disasters. As an earthquake-prone country, China has experienced approximately 20 major earthquakes since the early 20th century, resulting in hundreds of thousands of deaths and economic losses amounting to hundreds of billions [5]. In response to the high frequency of disasters, external disaster-prevention space, mainly urban green space, plays a key role in refuge and housing. However, due to varying spatial characteristics and population needs across different locations, there is often a significant gap between the supply and demand for urban green space [6]. Meanwhile, as cities were mainly built in an incremental mode in the past, space resources have been gradually saturated. The amount of urban green space is greatly insufficient, the effective refuge space of residents is insufficient, and the accessibility of green space is low [7], resulting in low evacuation and refuge efficiency during disasters. Particularly in small and medium-sized cities, the coverage of urban green spaces shows a declining trend year by year [8]. For instance, Wei et al. (2020) revealed that such shelters often suffer from uneven site distribution, insufficient public awareness of shelter locations, and a concentration of emergency shelters in more developed urban areas, while disaster-prevention systems in rural regions are frequently overlooked [9]. Therefore, it is essential to organize and integrate the current state of urban green spaces and attempt to improve their layout. This can provide urban residents with more equitable access to green spaces and help prevent and reduce losses caused by disasters.

In addition to the above-mentioned earthquake disasters, we have also noticed that cities along the eastern coast of China are frequently affected by typhoons in summer. The extreme weather, accompanied by sustained winds, heavy rainfall, and storm surges, poses a devastating threat to coastal areas with growing populations and economies [10,11]. In the face of extreme weather conditions, green spaces in parks serve a role that is fundamentally different from their function in geological disasters like earthquakes. However, many studies focus on the influence of urban green space on the urban wind environment under daily wind speed. It has been found that urban green spaces can influence wind speed and direction through the arrangement and coverage of plants, thereby enhancing residents’ comfort and safety [12]. Increasing natural air ventilation can effectively promote the entry of fresh and cool air from the suburbs into the urban area, improving the urban microclimate and alleviating the urban heat island effect [13,14]. While there is extensive research on the impact of urban green space on urban thermal environments, thermal comfort, and the urban heat island effect (UHI) [15,16,17,18], there is a notable lack of studies addressing the influence of urban green space on the urban wind environment under extreme weather conditions [19].

The accessibility index for human subjects can indicate whether the distribution of park green space is equitable [20]. Accessibility was proposed by Hansen in 1959, who initially defined reachability as the potential for interaction opportunities [21]. Then, Ṕaez et al. (2012) defined accessibility as the potential to obtain spatially distributed opportunities such as employment and entertainment [22]. As the frequency with which people use park green spaces decreases with increasing distance [23], ensuring accessibility and equitable access to urban green space services becomes crucial for urban environmental sustainability [24]. For instance, Wei et al. (2020) provided optimization strategies for locating parks and green spaces considered vital for emergency shelter purposes [9], while Li et al. (2020) investigated the accessibility and service areas of these spaces when serving as emergency shelters [25]. To a large extent, the research on accessibility not only considers the equal rights of people in different directions to use urban green space within the research scope, but also considers whether people can access the nearest escape shelter when disasters come. Therefore, it is essential to couple this index with the analysis of urban green space layout to ensure both equitable access and effective disaster preparedness.

Researchers have employed several methods to analyze park green space accessibility, such as the local method, nearest neighbor method, and gravity model-based method [26]. These early methods have several limitations. Then came the Gauss-based two-step floating catchment (2SFCA) method [19]. However, this method does not account for the fact that accessibility diminishes as travel costs increase, leading to the development of several improved models in subsequent research [27]. In addition, some scholars also use the network analysis method in Arcgis software to further analyze the relationship between the distribution of park green space and the road network within the study area by constructing a traffic network and creating service area [20,28]. Another mature method for analyzing accessibility is space syntax, which is mainly used by three mainstream software or plug-ins, such as Depthmap [29,30]. Some scholars have utilized the Axwoman plug-in in ArcGIS software for space syntax analysis [31]. In this study, the sDNA tool is employed for its better compatibility with ArcGIS software [32,33]. Specifically, this study uses the sDNA tool to construct a space syntax model of the road network within the research area. This method aims to provide a more objective and compatible model for assessing road network accessibility. The study will perform a coupling analysis of the kernel density distribution maps of park green space before and after adjustments and additions, to determine whether the layout of the adjusted park green spaces effectively aligns with areas of high road network accessibility.

Previous studies have primarily focused on the differences in the spatial distribution of Refuge Green Space (RGS) or Disaster-Prevention and Risk-Avoidance Green Space (DPRAGS) to address the existing supply and demand shortages [6,7]. These studies often concentrate on a single research subject. However, in many counties across China, urban disaster-prevention green spaces have not been extensively categorized, and urban green spaces can provide residents with basic and immediate disaster-prevention functions. Therefore, it is crucial to consider urban green space holistically. Additionally, most current research on urban green space layout focuses on the fairness of the distribution, overlooking the impact of green space layout in county parks on the wind environment and walkability during both daily and extreme weather conditions. To fill this research gap, this study references the specialized green space system plan of Anxi County and adjusts the existing park green space layout, ensuring efficient land resource utilization. By tapping into the potential of dispersed, underutilized urban spaces, it proposes the addition of 117 new park green spaces (including recreational parks and pocket parks) by carrying out the following: (1) evaluating whether adjustments and additions to park green spaces can improve wind environments and green space accessibility under both daily and extreme weather conditions; (2) analyzing the impact of adjustments to park green spaces on accessibility to provide better green space services, thereby enhancing residents’ quality of life.

2. Methodology

2.1. Research Area

This study selected the core planning area of Anxi County, Quanzhou City, Fujian Province, China as the study area (see Figure 1). Anxi County is located along the southeastern coast of Fujian Province, in the northwestern part of the Xiamen–Zhangzhou–Quanzhou Golden Triangle in southern Fujian, and is administered by Quanzhou. Notably, Quanzhou is recognized by United Nations Educational, Scientific and Cultural Organization (UNESCO) as the sole starting point of the Maritime Silk Road. Anxi County belongs to the south and middle subtropical marine monsoon climate. The climate is four distinct seasons throughout the year: summer is not hot and is followed by early cold autumn and winter, and spring comes late. Crops often suffer from “three cold” (spring cold, plum cold, autumn cold) harm. The main meteorological disasters are typhoons, heavy rain and flood disasters, cold air with low temperature and freezing damage, tropical cyclone and heavy rain attacks, autumn cold and so on. The average annual absolute humidity in the county is 20.0 millibar, and the average annual relative humidity is 76%. The precipitation pH value ranges from 5.93 to 7.02, and the frequency of acid rain ranges from 0 to 10.0%, which belongs to the non-acid-rain region.

2.2. Data Sources

2.2.1. Planning Area Model Data

According to the survey and mapping results of the core planning area of Anxi County, the overall architecture, green space, and city outline were drawn by AutoCAD 2014 software. Then, the CAD line draft was imported into SketchUp 2018 software to create a simplified 3D model combined with the mapping data.

2.2.2. Data on Park Green Spaces Before and After Adjustments and Additions

Remote sensing technology was used to extract urban green space information in the study area, with remote sensing image data sourced from the Sentinel-2 satellite digital products. Additionally, based on field verification, we extracted the current spatial distribution of park green spaces (see Figure 2). The distribution of park green spaces after adjustments mainly involves the addition and expansion of areas that require improvement in the current wind environment and areas with insufficient accessibility related to park green spaces and points of interest (POIs). Furthermore, we referred to the specialized planning of the green space system in Anxi County and ultimately added 117 small to medium-sized embedded parks and green spaces in high-demand residential areas, covering an area of approximately 162.38 hectares (see Figure 3).

2.2.3. POIs Categorized as Parks

POI data are usually the geographic locations uploaded by online map users from their mobile devices. These data label specific locations as useful or interesting and contain a wealth of information about human activity [34]. The data include four attributes, name, category, coordinates, and classification, which can reflect the location information of multi-category functional units in GIS [35]. The points of interest used in this study are from the Autonavi map. When selecting POIs for Anxi County in 2022, we systematically screened for POIs categorized as parks. Additionally, based on corrections according to actual land use, a total of 96 POIs related to green space were finally selected.

2.2.4. Wind Speed Data

Wind speed data come from the National Oceanic and Atmospheric Administration’s (NOAA) National Center for Environmental Information (NCEI). Based on the longitude and latitude of meteorological stations and the daily average wind speed of meteorological stations, the national daily average wind speed grid is obtained by interpolation (using inverse distance weight interpolation). Then, based on the administrative boundary data of prefecture-level cities and the wind speed grid diagram, the daily average wind speed of Quanzhou City is calculated, and then, the annual average wind speed is calculated based on the daily average wind speed value.

2.2.5. Road Data

The road data used in this study were sourced from OpenStreetMap (OSM), a widely recognized platform for publicly available geographic information. To refine these data, the authors simplified the original road network using the ArcGIS 10.8 software, incorporating actual field investigations and satellite imagery to enhance accuracy. Focusing on the core planning area of Anxi County, the study identified a total of 649 roads, spanning approximately 375 km.

2.3. Procedure

2.3.1. Partition

The core planning area of Anxi County mainly includes the whole of Fengcheng Town, the whole of Chengxiang Town, most of Shennei Township, and part of Qidou Town, with an area of about 132,000 square meters. In order to facilitate the description of the research results, we made a preliminary partition of the research area according to the partition diagram in the upper planning (see Figure 4). From west to east, Zone A is divided by the west of Fengshan and the south section of Xingfu Road, and Area B is divided by Xixi adjacent to South Hebin Road. The intersection of G358 and East Second Ring Road is divided into areas C and D.

2.3.2. Kernel Density Analysis of Park Green Space

Kernel density analysis is a spatial data analysis technique commonly used in ArcGIS to understand patterns in changes in geographic locations. This method utilizes the original data without being influenced by subjective factors, providing results that reveal gradual changes and detailed features [36]. In this study, the kernel density method was used to analyze POIs related to green space, as well as the spatial distribution of park green space before and after adjustments and additions. The results of the kernel density distribution were categorized into eight classes using the Jenks Natural Breaks Classification method. It is a statistical method for dividing a dataset into multiple groups by maximizing within-group homogeneity and between-group heterogeneity, thereby automatically identifying “natural breaks” in the data distribution and achieving optimal classification.

2.3.3. Wind Environment Simulation

In the current case study, computational fluid dynamics (CFD) simulations have been widely used to study the physical environment of buildings at different scales, including cities, neighborhoods, building units, and their interior areas [37]. This technique enables quantitative analysis of airflow dynamics around buildings, identifying potential airflow problems such as turbulence, backflow, and local areas of low pressure.

The software WindperfectDX is suitable for outdoor wind environment simulation research. The CFD simulation settings and models are as follows: ① The 3D models of buildings, terrain, and park green spaces before and after adjustments and additions are imported layer by layer; ② Automatic grid generation; ③ Setting of meteorological data; ④ The numerical algorithm is used to solve the problem, and the preset value is used to calculate the simulation result.

The park green space model needs to be processed separately before it is introduced. According to the types of vegetation, we divided the composition of vegetation into trees–shrubs–herbaceous plants (group A, 6 m), trees–shrubs (group B, 9 m), and trees (group C, 14 m). Therefore, the park green space model (current and adjusted) has a total of 6 groups.

In setting up the weather simulation conditions, the prevailing wind direction and wind speed during summer were selected. This is because the hot summer climate significantly impacts the comfort level of residents. The environmental simulation conditions were set for the south wind and 5 m/s as the dominant wind direction and wind speed, respectively. The other variables were set according to the location of Anxi County and the weather conditions of previous years. At the same time, we also considered the impact of typhoon wind speeds on the adjustments and additions to park green spaces before and after the changes. This is because the southern region is often affected by destructive typhoons, such as the No. 5 Typhoon Du Suri in 2023, which swept Fujian with a wind speed of 15. Therefore, improving disaster-prevention performance through optimization is of utmost importance. To address this, a simulation calculation was conducted using a typhoon wind speed of 50 m/sec (approximately equivalent to a Category 15 typhoon). A total of 12 sets of wind environment simulations were generated, covering various heights and wind speeds before and after the optimization of park green space. At the same time, in order to be close to the breathing height of the population, the wind environment simulation maps in this study were all taken from 1.5 m away from the ground.

2.3.4. Quantification of Wind Environment Simulation Results

Considering that the directly generated wind environment simulation result maps have significant visual interference and are only in JPG format (see Figure 5), we adopted the Iso unsupervised classification tool in ArcGIS 10.8 to quantify the wind environment simulation results. Since the wind environment simulation results are represented by a five-class color scheme, our iso clustering classification was also set to five classes, with other parameters set to their default values. Additionally, we established a spatial grid of 50 m × 50 m and used the spatial joining tool to connect the quantified wind speed data with the grid points, achieving a translation from image to data visualization. This generated a wind speed kernel density map for further analysis. The quantified wind environment simulated kernel density map allows for a more intuitive observation and comparison of the differences in wind environment characteristics presented by the park green spaces before and after adjustments (see Figure 6).

2.3.5. Accessibility Analysis of Park Green Space Based on sDNA Space Syntax

Spatial Design Network Analysis (sDNA) is a new method to reflect the relationship between road network structure changes and urban spatial expansion. Compared with other space syntax software, sDNA is more compatible with Arcgis, and its use in Arcgis is more conducive to subsequent research. sDNA evaluates the service capability of the current road network through space syntax. Therefore, the reachability can be analyzed more objectively and reasonably. In this study, we adopt closeness and betweenness for the accessibility analysis, consistent with most previous research [32,33]. Specifically, closeness is primarily computed using NQPDA, which is a form of proximity measure commonly referred to as a gravity model that simultaneously accounts for the quantity of network weights and accessibility [38]. Betweenness is mainly determined using wwo-phase betweenness (TPBt), indicating that destinations must compete with one another for the origin’s attention. It represents an intermediary measure that assesses overall traffic toward a destination by considering this competition [38].

In sDNA, NQPDA is generally used to measure closeness. NQPDA represents the distance between a street and other streets within the search radius, and represents the level of location accessibility in the road network. The higher the closeness, the higher the accessibility and the stronger the central-agglomeration effect. The calculation formula is as follows [33]:

$$\mathrm{N}\mathrm{Q}\mathrm{P}\mathrm{D}\mathrm{A}(\mathrm{x})=\sum _{\mathrm{y}\in \mathrm{R}\mathrm{x}}{\displaystyle \frac{{\left(\mathrm{W}\right(\mathrm{y}\left)\mathrm{P}\right(\mathrm{y}\left)\right)}^{\mathrm{n}\mathrm{q}\mathrm{p}\mathrm{d}\mathrm{n}}}{{\mathrm{d}}_{\mathrm{M}}{(\mathrm{x},\mathrm{y})}^{\mathrm{n}\mathrm{q}\mathrm{p}\mathrm{d}\mathrm{d}}}}$$

where (x,y) is the shortest path distance and, in angle analysis, is the angular topological distance; P(y) in the discrete space, if the point is within the search radius, is 1, and 0 otherwise, and in the continuous space, it is determined in proportion to the radius and the road length, between 0 and 1; W(y) is the weight of the road section; nqpdn and nqpdd default to 1, representing the NQPDA numerator and denominator, respectively.

Through the analysis of this index, it can reflect the service level of the current road network in the core planning area to a certain extent and then adjust, add and plan the green space in the area with high road service potential. In order to improve the accessibility of park green space and the convenience of residents’ disaster prevention and avoidance when disasters occur, the higher the location closeness of green space, the higher the accessibility.

The calculation of betweenness is based on the number and length of paths between nodes in the network, reflecting the traffic potential of street segments. Nodes with high betweenness are usually the key nodes connecting different areas or paths in the network and play an important role in the overall connectivity and accessibility of the network. The higher the degree of betweenness, the greater the probability of choosing to pass through the street in the actual trip. Since the accessibility based on angular distance has been proved to have a good correlation with the actual observed distribution of passenger-vehicle behavior (Chiaradia et al., 2015) [34], the AngularBetweenness based on angular distance is used as a measure of the accessibility of road networks. The calculation formula is as follows [33]:

$$\mathrm{b}\mathrm{e}\mathrm{t}\mathrm{w}\mathrm{e}\mathrm{e}\mathrm{n}\mathrm{n}\mathrm{e}\mathrm{s}\mathrm{s}(\mathrm{x})=\sum _{\mathrm{y}\in \mathrm{N}}\sum _{\mathrm{z}\in \mathrm{R}\mathrm{y}}\mathrm{W}\left(\mathrm{y}\right)\mathrm{W}\left(\mathrm{z}\right)\mathrm{P}\left(\mathrm{z}\right)\mathrm{O}\mathrm{D}(\mathrm{y},\mathrm{z},\mathrm{x})$$

where y and z are geodesic endpoints; x is the location of the node where the traverse degree is measured; W(y) and W(z) are the weights; OD(y,z,x) is the shortest topological path between y and z that passes through x within the search radius. The values are as follows:

$$\mathrm{OD}(\mathrm{y},\mathrm{z},\mathrm{x})=\left\{\begin{array}{c}1,\mathrm{x}\mathrm{l}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{f}\mathrm{i}\mathrm{r}\mathrm{s}\mathrm{t}\mathrm{g}\mathrm{e}\mathrm{o}\mathrm{d}\mathrm{e}\mathrm{s}\mathrm{i}\mathrm{c}\mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m}\mathrm{y}\mathrm{t}\mathrm{o}\mathrm{z}\\ 1/2,\mathrm{x}=\mathrm{y}\ne \mathrm{z}\\ 1/2,\mathrm{x}=\mathrm{z}\ne \mathrm{y}\\ 1/3,\mathrm{x}=\mathrm{y}=\mathrm{z}\\ 0,\mathrm{o}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{s}\end{array}\right.$$

Parameter standardization of the total number of nodes within the search radius is carried out in sDNA, and the obtained two-stage traversal parameter represents traversal, which is characterized by the distribution of the starting weight on the target weight. The calculation formula is as follows [33]:

$$\mathrm{T}\mathrm{P}\mathrm{B}\mathrm{t}(\mathrm{x})=\sum _{\mathrm{y}\in \mathrm{N}}\sum _{\mathrm{z}\in \mathrm{R}\mathrm{y}}\mathrm{O}\mathrm{D}(\mathrm{y},\mathrm{z},\mathrm{x}){\displaystyle \frac{\mathrm{W}\left(\mathrm{z}\right)\mathrm{P}\left(\mathrm{z}\right)}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{W}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\left(\mathrm{y}\right)}}$$

where TPBt (x) is the value of two-stage traversal degree, and TotalWeight (y) is the total weight of nodes within the search radius of each y.

According to the requirements of the grading control scale of residential areas in the Planning and Design Standard for Urban Residential Areas GB 50180-2018 [39], the walking distance of the community living circle in 15 min is 800–1000 m. In sDNA, the local closeness analysis and betweenness analysis with radius R = 1000 m are carried out, and the distance and angle rotation are combined to correspond to the distance and direction of walking to the destination park. The global reachability measure for any point in the study area can be used to describe the reachability of vehicles [33]. In sDNA, the global closeness and betweenness analysis of R = N (N is the maximum length number) is used to test the accessibility of green space to judge the attractiveness of green space, and the combination of distance and angle deflection is also adopted.

2.3.6. Moran’s I Index Analysis

In order to make the above analysis of closeness and betweenness more intuitive and three-dimensional, the Moran’s I index in Arcgis was used for cluster analysis of the above contents. In the early stage, spatial autocorrelation tools were used to conduct global Moran’s I analysis to observe whether the data were clustered and distributed. After ensuring that the data are clustered or discrete, the cluster and outlier analysis tool is used for local Moran’s I analysis. From this, we can find out where the outliers or clusters are in the study range and generate the LISA (Local Indicators of Spatial Association) map.

2.3.7. Coupled Analysis of LISA Map and Park Green Space Kernel Density Map

The LISA map generated by local Moran’s I analysis can reflect statistically significant spatial outliers (high values surrounded by low values or low values surrounded by high values) within the study range. It can directly reflect the advantages and differences in the road network service level in the study area. On this basis, the kernel density maps of park green spaces before and after adjustments and additions can be superimposed to observe whether the distribution of park green spaces after adjustments matches the aggregation level of overall road network accessibility.

2.3.8. Software

This study uses several software to assist the research (see Figure 7). We utilized SketchUp 2018 to model the core planning area of Anxi County. Subsequently, Windperfect DX software was used to simulate and analyze the wind environment before and after the adjustments and additions to the park green spaces. Additionally, ArcGIS 10.8 was used for various analyses, including kernel density analysis of POIs related to green spaces and the distribution points of park green spaces before and after adjustments and additions. The iso cluster unsupervised classification tool quantified the wind environment simulation results, while sDNA space syntax software was used to analyze the spatial accessibility of the road network. The results of the reachability analysis were further examined using Moreland cluster analysis. Finally, the kernel density analysis and accessibility cluster diagrams of park green space were integrated to provide a comprehensive view.

3. Results and Analysis

3.1. Kernel Density Distribution Characteristics of Park Green Space

As shown in Figure 8, the analysis of the current situation indicates that the number of green parks is insufficient and their distribution is uneven, particularly in the northern parts of Zones A and B, and the southeastern areas of Zones C and D. Green space-related POIs are primarily concentrated in study area A, while the POI distribution in Zones B, C, and D is relatively scattered, exhibiting weak service performance. This indicates that the existing green space POIs are inadequate to meet the full range of residents’ needs in the core planning area. The adjusted layout of park green spaces addresses these deficiencies by creating a more balanced distribution and appropriately increasing the number of green spaces, thereby enhancing the accessibility of park green spaces. Additionally, green spaces contribute to improved air quality, help mitigate the urban heat island effect, and provide residents with areas for cooling, leisure, and recreation [40]. Therefore, we will further focus on whether the adjusted and added layout of park green spaces can effectively improve the wind environment in the county.

3.2. Spatial Simulation Characteristics of Wind Environment

The simulation analysis results of urban wind environments with three different vegetation composition structures are shown in Figure 9. Group A consists of trees, shrubs, and herbs. It can be clearly observed that under daily wind speed (5 m/s), the urban wind environment after the adjustments and additions of park green spaces exhibits improved ventilation performance compared to the current situation, transitioning from a large base of low wind speeds (0–1 m/s) to moderate wind speeds (2–5 m/s). In the case of typhoon wind speed (50 m/s), the wind environment after the adjustments to park green spaces shows enhanced disaster-prevention performance compared to the current situation, with wind speeds gradually decreasing from some higher speeds (40–50 m/s) down to (10–20 m/s), resulting in a significant improvement in the wind environment.

Group B consists of trees and shrubs, and it can be observed that under daily wind speed, the ventilation performance of the urban wind environment after the adjustments to the green spaces is weaker than that of the current situation. Under typhoon wind speed (50 m/s), the optimized wind environment shows improvements compared to the current disaster-prevention performance, leading to significant enhancements in the wind environment. Group C consists solely of trees, and it can be observed that under daily wind speed, the changes in the wind environment before and after the adjustments to the green spaces are slight. There is a minor improvement under typhoon wind speed, but overall, the changes are more subtle compared to the other two groups.

In summary, Group A exhibited the most significant improvement in the wind environment after the layout optimization.

3.3. Accessibility Evaluation of Park Green Spaces

3.3.1. Closeness Evaluation

Closeness reflects the degree to which a park’s green space location attracts nearby people and vehicles, serving as a key metric for measuring the accessibility of park services. The higher the closeness of a park’s location, the greater its accessibility. As shown in Figure 10, at the local scale, which represents walkability, districts A and B exhibit high overall closeness. In contrast, areas C and D show lower levels of closeness. On the overall scale representing vehicle accessibility, the overall accessibility increases compared to the local scale. Notably, the central areas of Zones A and B have closely connected road networks, resulting in high closeness. The main roads radiating from these two zones exhibit greater traffic potential, with an increase in the number of red and orange streets, particularly in the connecting area between Zones A and B. This suggests that major roads and large parks have higher vehicle accessibility compared to smaller streets. In contrast, the closeness in the northern and southern parts of Zones C and D is low. The hilly terrain and sparse road network in these areas limit vehicle traffic capacity, reducing regional accessibility.

3.3.2. Betweenness Evaluation

Betweenness represents the likelihood of people or vehicles passing through the streets where park green spaces are located. The higher the betweenness, the greater the chance that pedestrians will stop and rest in these green spaces. This metric is particularly relevant at the local, walking scale, where the influence of walking betweenness is more pronounced than that of global vehicle betweenness. Residents are more likely to stop and rest while walking through streets than when driving, making this measure a clear indicator of a park’s attractiveness to walkers. As shown in Figure 11, on the walking scale, the main roads in the central areas of Zones A and B are marked in red and orange, indicating higher betweenness and, therefore, greater appeal to walkers. In contrast, the northeastern part of Zone C, the southeastern part of Zone D, and the areas along the mountains throughout the study area are predominantly dark blue, reflecting low betweenness and limited walking accessibility, similar to the patterns observed in the closeness analysis.

3.4. Moran’s I Index Results Analysis

Therefore, Moran’s I index is chosen to clarify the functions of road sections and their relationship with surrounding roads, which can objectively explain the impact of the road environment on the location of parks and provide corresponding solutions. It will also contribute to the subsequent further analysis and verification of the evaluation results of spatial syntax.

3.4.1. Global Moran’s I

The results of the global Moran’s I index analysis for both global and local closeness and betweenness are shown in

Table 1. The global autocorrelation analysis results of sound source perception.

Global Moran’s I Analysis		Moran’s I	z-Values
Overall	Closeness	1.041429	67.595648
	Betweenness	0.218508	14.197220
Locality	Closeness	1.216339	79.083934
	Betweenness	0.110657	7.237766

. The Moran’s I indices of the four evaluation indicators are all greater than 0, with p-values less than 0.01 and z-values significantly greater than +2.58, indicating a confidence level of 99%. This suggests that there is a significant positive spatial autocorrelation, exhibiting a highly significant clustering pattern. Therefore, local Moran’s I index analysis can be conducted based on these results to trace the areas of clustering and outliers.

3.4.2. Local Moran’s I

Observing the local Moran’s I index analysis for pedestrian closeness in Figure 12, we can find that the pink high–high clusters representing high proximity indicate better pedestrian accessibility, belonging to the most vibrant and attractive areas in the core planning area of Anxi County. The road network surrounding the central parts of Zones B and C has a higher attraction, and the surrounding park green spaces are more likely to have high attractiveness. The red high–low clusters are relatively scattered and fragmented, and can basically be ignored. The blue low–high clusters are mainly located on the southern side of Zone B and both sides of the river, especially in areas near the river with more public buildings and residential areas. There are many dead-end roads in these areas, reducing their pedestrian attractiveness. In the local Moran’s I index analysis for pedestrian betweenness, pink high–high clusters are concentrated around the centers of Zones A and B, indicating that these road sections have higher pedestrian traffic, more intersections with branch roads, and higher permeability of surrounding road sections. Blue low–high clusters are clustered around the pink road sections, representing low permeability, mainly found in semi-closed areas such as residential districts, and partially along mountain ranges, rivers, and streams.

In the global Moran’s I index analysis for vehicular closeness, the pink high–high clusters are primarily observed in areas radiating outward from the central circle of Zone B, as well as the loop road and surrounding road sections in the central part of Zone A. These areas gradually connect to the blue low–high clusters. Overall, the central road network demonstrates strong vehicular attractiveness and high park accessibility. However, the poor road connectivity in Zone C leads to lower park attractiveness. The accessibility of park green spaces and the connectivity between Zone C and Zones A, B, and D can be improved by increasing and planning park green spaces, as well as enhancing the connectivity and completeness of the road network. In the global Moran’s I index analysis for vehicular betweenness in Figure 8, the pink high–high clusters are concentrated along riverside roads and some main roads, indicating high traffic volume, more intersections with secondary and branch roads, and higher permeability of surrounding road sections. It can be understood that main roads have higher road service levels, stronger traffic capacity, and higher park accessibility.

3.5. Coupling Analysis of Park Green Space Kernel Density Map with LISA Map

To investigate whether the distribution of park green spaces after adjustments and additions can optimize the accessibility of existing park green spaces, we conducted further overlay analysis using ArcGIS 10.8. This analysis compared the relationship between green space POIs, the kernel density distribution maps of park green spaces before and after adjustments, and the Moran’s I LISA maps reflecting the walkability and vehicular accessibility of the road network. The goal was to assess the alignment between the distribution of park green spaces, POIs, and road networks with superior service levels.

As shown in Figure 13, the overlay analysis of the global and local closeness LISA maps reveals that the kernel density distribution map of park green spaces after adjustment largely covers road networks with significant service advantages, strong accessibility, and central-agglomeration effects. The coverage is extensive and deep, particularly in the center and surrounding areas of Zone B, the western part of Zone A, and Zone C. This addresses the issues of the current scarcity and uneven distribution of park green spaces, providing convenience for residents to access park green spaces on foot or by car. Additionally, in times of disaster, the improved accessibility of park green spaces can provide residents with more accessible outdoor open spaces, reducing the likelihood and negative impacts of disasters. The overlay analysis of the global and local betweenness LISA maps shows that the adjusted park green spaces have improved overall aggregation.

4. Discussion

4.1. The Impact of Urban Park Green Spaces with Different Vegetation Compositions and Structures on Urban Wind Environment

Trees of diverse species serve as interlocutors between human beings, society, and nature, providing a range of direct and indirect benefits for modern cities, such as eliminating air pollution, reducing carbon emissions, lowering urban temperatures, and relieving human psychological stress [41]. Meanwhile, plants are crucial components of outdoor space planning and design. Due to their transpiration and leaf obstruction, plants significantly influence the temperature, humidity, and wind environment of the surrounding areas [42]. If the vegetation planting in park green spaces is not properly planned, it can reduce the wind speed, which in turn has adverse effects on human thermal comfort, especially under downwind conditions [43]. This highlights the necessity of understanding how different vegetation compositions affect urban wind environments.

To investigate this, we designed three vegetation configuration groups to simulate their impacts on the wind environment of park green spaces: Group A (trees, shrubs, and herbs), Group B (trees and shrubs), and Group C (trees only). Based on the plant height histogram studied by Scheffer et al. [44], the average heights of shrubs and herbs were set at 3.6 m and 0.72 m, respectively. Considering the artificial and managed nature of most trees in the study area, a tree height of 14 m was adopted, referencing common species in Anxi County and seedling catalogs from South China. Accordingly, we assigned average vegetation heights of 6 m for Group A, 9 m for Group B, and 14 m for Group C to represent the structural characteristics of each group. These configurations are largely consistent with previous studies [16,45,46], although some research focused on individual vegetation layers—trees, shrubs, or herbs—while others neglected herbaceous plants entirely. Importantly, unlike studies that emphasize isolated parks, our research evaluates how changes in the overall green space layout across the core planning area of Anxi County influence wind conditions on a larger scale. Therefore, we adopted different vegetation combinations to simulate the effective green space height and to identify the optimal structural configuration for improving urban wind environments.

Wind speed is a crucial factor affecting thermal comfort, and its impact on pedestrians varies depending on the specific conditions of the surroundings [47,48]. Different vegetation compositions affect the simulation results of urban wind environments, thus influencing people’s comfort levels. As clearly observed from the wind environment simulation diagrams at 1.5 m above the ground for three groups, Group A (trees, shrubs, and herbs; 6 m) shows significant effectiveness in improving urban wind environments under both daily and extreme weather conditions (such as typhoons). It not only enhances ventilation and airflow within the city under normal wind speed conditions, improving the static wind situation [4,18], but also raises the wind speed (2–5 m/s) to enhance comfort levels for the population [49]. Moreover, it also reduces wind speed to some extent during typhoons, mitigating the negative impacts of extreme weather on urban residents.

Interestingly, similar findings have emerged from studies examining the impact of various vegetation types on urban thermal environments under different conditions. These studies often find that the order of effectiveness in improving air temperature, thermal comfort, and safety (e.g., PET and WBGT) at 1.5 m above the ground is as follows: trees > lawn > shrubs. For areas downstream of planting zones, trees and shrubs can reduce wind speed and lessen the pressure on buildings. Conversely, in areas downstream of non-planting zones, these plants can increase wind speed and pressure [16]. Other studies report different results. For example, an analysis of microclimate and comfort characteristics in summer found that parks with tall trees and large-canopy vegetation provided the highest level of microclimate comfort, while parks with extensive lawn coverage and few small trees offered the poorest comfort [17]. The main issue with large lawn areas is the lack of shading from large-canopy trees, leading to prolonged sunlight exposure and strong wind flow, which can negatively affect thermal comfort. Conversely, parks with tall trees offer high thermal comfort but may have weaker wind flow, which can limit ventilation in the surrounding area. This study considers the collective impact of multiple urban parks and green spaces on urban wind environments, and the optimal vegetation composition structure obtained from wind environment simulations (Group A: trees, shrubs, and herbs) can explain the differences with the aforementioned studies. Additionally, this vegetation composition aligns with the common vegetation composition of actual urban parks and green spaces [16,45,46].

4.2. Analyzing the Accessibility of Park Green Spaces from the Perspective of Space Syntax

In the past two decades, China has witnessed rapid urbanization, which has led to increasing urban public safety pressures. Emergency shelters have become an important and urgent issue, and urban park green spaces hold significant potential. Therefore, the equitable allocation of park green spaces has attracted much attention from scholars. However, due to the subjectivity, variability of urban boundaries, and the spatial distribution of urban green spaces, traditional urban green space indicators (such as green space ratio and per capita green space area) cannot accurately measure the quality of urban greenery. Therefore, people-oriented accessibility indicators have been increasingly applied [50]. Accessibility is one of the crucial indicators reflecting the equitable allocation of park green spaces [20,51,52]. Studies on accessibility have largely considered the equal rights of people in different locations within the study area to use park green spaces, as well as whether people can have nearby evacuation shelters in times of disaster.

This study employed the sDNA spatial syntax tool to analyze the accessibility of park green spaces. The results showed that the current distribution of park green spaces is uneven and does not fully cover the road network with advantageous road accessibility, which may reduce the equity and fairness of people’s nearby access to park green space services [53,54]. This finding is similar to other studies, where despite differences in research methods, scholars have found through spatial syntax analysis using sDNA, Gaussian two-step floating catchment area (2SFCA), and GIS network analysis that the current urban green space distribution within the study area is uneven and does not meet people’s needs [6,20,32,55,56,57]. After improving the uneven distribution of the above-mentioned park green spaces, we conducted a coupling analysis of the optimized park green space distribution map and the road accessibility LISA map, revealing that the uniformity of the park green space distribution has been effectively enhanced, covering a large area with high accessibility, enabling people to use park green spaces nearby. Interestingly, some scholars have used the Gaussian two-step floating catchment area (2SFCA) method to study the spatial–temporal changes in urban green space accessibility, and their results are consistent with this study, showing that the adjusted park green space layout results in a more balanced overall accessibility, providing more equal benefits to people in different regions [56,57].

To further investigate the spatial correlation, clustering degree, and identify outliers of road accessibility, this study conducted Moran’s I analysis. Some scholars have also used kernel density tools to analyze accessibility maps, observing their spatial characteristics and relationships with urban green spaces and population distribution [32]. While this tool has advantages in analyzing the spatial clustering characteristics of accessibility, it also has limitations. It can intuitively reflect the density differences in accessibility in different regions and the spatial development trends of transportation networks. However, for analyzing certain special elements, such as road networks that are closely interconnected, the kernel density analysis method will mainly focus on high-value elements, ignoring regions with outliers or low values. Whether high-value elements are statistically significant hotspots requires further analysis. The clustering analysis adopted in this study can avoid this limitation, highlighting regions with high and low values and outliers. The distribution of outliers reflects the non-stationarity of various indicators in space, providing a reference for adjusting the layout of park green spaces, dispersing the distribution of park green spaces in high-value clusters, increasing the number of park green spaces in outlier regions, improving the accessibility of the overall region, and providing nearby park green space facilities for residents [7].

4.3. Optimizing the Layout of Park Green Spaces Has a Comprehensive Impact on the Urban Wind Environment and the Accessibility of Green Spaces

With the advancement of urbanization, improving the quality of human settlements has become an urgent priority. The urban green space, as the environment that supports the survival and life of all creatures, possesses profound and significant meaning. Firstly, in daily life, urban green spaces contribute to human health by promoting physical activity and mental well-being [58,59,60]. Secondly, when natural disasters strike, people instinctively seek refuge in open green spaces to protect themselves [61]. Both aspects illustrate the necessity of rational and equitable allocation of urban green spaces. Therefore, considering the actual needs of society today, we find that focusing solely on the optimization of park green space layout for people’s daily lives is too one-sided. We should also increase our awareness of “preparing for a rainy day” and recognize that effective utilization, planning, and design of green spaces are crucial for preventing and avoiding disasters [6]. To comprehensively evaluate the effectiveness of park green spaces in terms of both daily use and disaster prevention, this paper selects two aspects, urban wind environment and accessibility, to analyze the impact of adjustments and additions to the park green spaces.

Firstly, urban wind environment refers to the comprehensive phenomenon of the temporal and spatial distribution of wind within a city and its interaction with the urban environment, which is a component of urban climate [61]. The surrounding mountains in the core planning area of Anxi County mostly have slopes ranging from 18 to 50 m, and the planning area is surrounded by mountains such as Fenghuang Mountain, Bijia Mountain, Wufeng Mountain, Shijiang Mountain, Tiefeng Mountain, and Dazhai Mountain. The surrounding mountains lead to natural wind blowing from the surrounding mountainous and rural areas into the city, which is easily blocked by mountains and dense urban construction, resulting in reduced wind speed, increased wind pressure, and a significant pressure difference between the windward and leeward sides of the city, causing still wind phenomena within the city. To improve this situation, we have conducted a simulation assessment of the wind environment before and after the adjustments and additions to the park green spaces. This aims to explore whether the improved layout of the park green spaces can effectively enhance ventilation under daily wind speeds, alleviate still wind phenomena above the city, and effectively reduce the destructive losses caused by natural disasters such as typhoons.

The wind environment is usually evaluated through WT tests, field measurements, and computational fluid dynamics (CFD) [46,62,63,64]. The limitation of WT tests is that they can only consider limited locations, while CFD can provide output data at any point in the study area [65]. CFD has been widely used in wind studies in urban areas [42,66]. This advantage is particularly suitable for quantitative and qualitative analysis of large geometric structures and large-scale areas. Therefore, this study adopts CFD technology for wind environment simulation, using the software WindPerfectDX, which can simulate and analyze different scale ranges to obtain results for wind environment analysis and evaluation. It has been used in previous studies to analyze wind and thermal environments [67,68,69]. Compared with other CFD software (such as Phoenics), it not only uses the Cartesian finite volume method to calculate airflow, but also has a well-designed preprocessing and post-processing interface [42], as well as advantages such as multi-scale simulation and easy export of visualization analysis functions. Meanwhile, all simulation result maps in this study are taken at a height of 1.5 m from the ground, which is consistent with other studies. International descriptions of wind speed levels generally use the Beaufort scale, but since the Beaufort scale describes wind speeds at a height of ten meters above the ground, it cannot be equated with the wind environment experience at pedestrian scale. Therefore, the wind speed values studied in this paper should be based on the wind speed values at a pedestrian height of 1.5 m [15,16].

Secondly, in terms of accessibility, we conducted a relevant study on the road network within the research scope using sDNA spatial syntax. We selected two indicators that best highlight accessibility for global (vehicular) and local (pedestrian) analysis, namely closeness and betweenness. After clustering the results, we found that areas with higher accessibility for local pedestrian and global vehicular traffic (pink high–high clusters and blue low–high clusters) are mostly distributed near the densely populated urban center and main roads. As can be seen, the research results are consistent with other studies [33], all indicating that the accessibility in urban centers is strong. Additionally, it is noteworthy that in this study, the central area of Zone A and its surrounding regions have high accessibility. Although this area is not the urban center, it has a high degree of road network connectivity, appropriate hierarchical connections, and strong service capabilities. Therefore, the layout of park green spaces should be considered from a global perspective.

There are some limitations in our study. Firstly, the classification of vegetation composition could be further refined by incorporating additional factors such as vegetation density. As this study only distinguishes vegetation based on height, it may not fully capture the influence of planting density on the wind environment. Secondly, the types and scales of park green spaces could also be categorized in more detail to better explore the differentiated needs of various user groups. Future research will aim to address these limitations by incorporating vegetation density and park typologies, thereby enhancing the understanding of how green space layouts in county-level cities influence both wind environments and accessibility.

5. Conclusions

With the advancement of society and improvements in living standards, there is an increasing demand for higher quality in human settlements. A key aspect of this demand is the distribution of urban green spaces to provide residents with the most direct and convenient access to natural ecology within the city. In recent years, the frequent occurrence of extreme disasters, such as typhoons and earthquakes, has heightened concerns for public safety. Consequently, urban green spaces have taken on additional roles, including ecological functions, disaster prevention, risk mitigation, and socio-economic benefits. However, due to the urban development of county towns, there is a lack of attention to the distribution, establishment, and maintenance of park green spaces. Therefore, conducting a targeted assessment of the current distribution of green spaces in county towns to provide objective adjustment proposals and compare differences before and after adjustments is the core focus of this study.

This research examines the spatial simulation characteristics of the wind environment and accessibility in county towns before and after the adjustment and addition of park green spaces. The findings are as follows: (1) Under the vegetation ratio of arbor–shrub–herb, the adjusted wind environment in the county town has improved ventilation performance under daily wind speeds (5 m/s), with wind speeds changing from the current large-base low wind speed (0–1 m/s) to medium wind speeds (2–5 m/s). During typhoon winds (50 m/s), the disaster-prevention performance is enhanced, with wind speeds gradually decreasing from partially higher speeds (40–50 m/s) to lower speeds (10–20 m/s), resulting in significant improvements in the wind environment. (2) After optimizing the layout of park green spaces, accessibility was greatly enhanced, better meeting the needs of the population in the developed area. We hope our research plan can provide a more objective testing and verification pathway for the adjustment and distribution of park green spaces in county towns and observe the diverse results presented by different areas, thereby guiding the planning and management of park green space layouts in county towns more efficiently.