Study on the Accessibility of Urban Parks Within the Framework of Kunming’s 15-Min Living Circle

Abstract

With the acceleration of urbanization, the accessibility and equity of urban green spaces have become crucial issues in urban planning and public health. In the context of the 15-min living circle, whether residents can conveniently reach parks within a walkable or bikeable time frame directly impacts their quality of life and social well-being. Traditional park accessibility evaluation methods, such as the G2SFCA, effectively measure accessibility but fail to fully account for the diversity of travel modes and the impact of regional disparities on equity. This study employs the TB-G2SFCA method, integrating the concept of the 15-min living circle, to analyze the equity of park accessibility in the Dianchi Lake ring area of Kunming under different travel modes. The findings reveal significant disparities in park accessibility for walking and cycling, particularly in suburban communities distant from the city center, where many areas cannot reach a park within 15 min. Although accessibility improves under driving and public transit modes, resources remain concentrated in well-connected areas, leaving peripheral regions with insufficient access. Compared to the traditional G2SFCA method, the TB-G2SFCA approach more accurately reflects spatial differences and equity issues across travel modes. This study suggests that future urban park planning should optimize resource allocation, improve transportation networks, and enhance park accessibility in peripheral areas—especially for walking and cycling—to achieve a more equitable and sustainable distribution of urban green spaces.

1. Introduction

With rapid urbanization driving global urban populations to exceed 4.3 billion (66% of the total population) in 2024 [1,2], China’s urbanization rate has surged from 17.9% in 1978 to 67.0% in 2024. This explosive growth has amplified urban residents’ demand for green spaces by 37% since 2015 [3,4]. As essential urban green infrastructure, parks serve multiple functions, including recreation, cultural activities, and ecological enhancement [5,6,7]. They contribute not only to urban livability but also significantly reduce urban heat island effects [8,9] and lower depression rates by 18–23% in frequent users [10,11].

However, rapid urban expansion has created severe spatial inequities: only 41.8% of Chinese cities met the national standard of 12 m² green space per capita [12], with low-income neighborhoods in Beijing having 72% less park area than high-income areas [13]. In Kunming’s Dianchi region, the park density gap between northern (0.48/km²) and southern (0.12/km²) zones reflects a 4:1 disparity [14]. Globally, 1.4 billion urban residents lack convenient park access, with peripheral areas requiring 2.3 times longer walking distances than city centers [15]. These disparities are exacerbated by transportation limitations—in Chinese megacities, public transit users face 40% longer park access times compared to private vehicle owners [16].

To promote the equitable distribution of urban green spaces, the concept of the 15-min living circle has gained prominence in urban planning and public policy research [17,18]. This concept emphasizes that residents should be able to access essential public services—including parks, healthcare, and education—within a 15-min walking or cycling distance. By reducing reliance on motorized travel [19,20], the 15-min living circle not only optimizes urban spatial organization but also enhances residents’ convenience, alleviates traffic congestion, and promotes low-carbon, sustainable development [21,22]. With the introduction of this concept, assessing and optimizing park accessibility—especially for nonmotorized travel modes such as walking and cycling—has become a key issue in urban green space research.

However, traditional accessibility analysis methods, such as the gravity-based two-step floating catchment area (G2SFCA) method, while effective in measuring the geographic accessibility of parks to residential areas, have certain limitations in assessing spatial equity [23,24]. These methods do not fully account for the diversity of travel modes, particularly the differences in accessibility impacts among walking, cycling, and public transit—key low-carbon transportation modes [25,26]. As a result, regional disparities arising from different transportation modes and geographical conditions are often overlooked. Therefore, a more refined accessibility evaluation method is needed to better capture variations in park accessibility equity across different travel modes and regions.

To address the imbalance in park accessibility caused by complex geography, uneven resource distribution, and diverse transportation infrastructures in the Dianchi Lake ring area of Kunming, this study aims to identify spatial equity gaps and provide refined planning support for urban green space development. This study applies a tailored TB-G2SFCA method, building upon the traditional G2SFCA model and integrating the 15-min living circle concept, to explore the spatial distribution characteristics of park accessibility equity in the Dianchi Lake ring area of Kunming.

The TB-G2SFCA method is particularly suitable because it not only captures multimodal transportation scenarios—such as walking, cycling, driving, and public transit—but also considers time thresholds and the actual travel behaviors of residents, making it more aligned with the real-world context of urban mobility and park usage [27]. By analyzing accessibility differences across walking, cycling, driving, and public transit modes, this study reveals the profound impact of regional and transportation disparities on equitable park services. Notably, significant differences in accessibility are observed between the northern and southern areas of Dianchi Lake, influenced by variations in transportation networks, geographic conditions, and the distribution of park resources.

Furthermore, by introducing the TB-G2SFCA method, this study offers a novel perspective and methodological framework for assessing park accessibility equity, thereby contributing to the advancement of research on urban green space accessibility. At the same time, combined with the concept of the 15-min living circle, it provides more accurate decision-making support for park planning and transportation policy in Kunming and other cities, promoting urban development in a more balanced and sustainable direction.

2. Literature Review

As the concept of sustainable urban development advances, “equitable accessibility” in urban spaces is becoming an important issue in global urban governance [28,29,30]. Green spaces, especially urban parks, have gained significant attention for their multiple functions, including ecological benefits, recreation, and mental health [31,32]. In recent years, the “15-min living circle” has emerged as a key concept for measuring the equity of urban spaces and is increasingly used as a core indicator of residents’ access to public service facilities [33]. Meanwhile, methods for measuring spatial accessibility are continuously evolving, transitioning from traditional models to multidimensional approaches that integrate behavioral data, such as TB-G2SFCA. This paper will systematically review the theoretical and methodological developments related to the “15-min living circle” and accessibility measurement, analyze the achievements and limitations of existing research, and highlight the research perspectives and innovations presented in this study.

2.1. The Multidimensional Connotations of Urban Parks and Spatial Accessibility

As a type of green space, urban parks play a critical role not only as important venues for residents’ leisure and entertainment but also in enhancing mental health and promoting social interaction [34,35,36]. With the acceleration of urbanization, improving the spatial accessibility of parks and ensuring that different social groups, especially marginalized ones, have equitable access to green spaces has become a core issue in urban planning and public policy [37,38]. Accessibility was initially viewed as the ability to overcome physical spatial barriers, emphasizing the physical path from the origin to the destination [39,40]. However, in recent years, the concept has gradually evolved into a multidimensional, multilayered framework that includes not only physical factors (travel mode, time, distance, frequency, etc.) but also extends to psychological perception (cultural identity, emotional connection) and institutional safeguards (service affordability, policy support) [41,42,43,44]. In this context, research on the spatial accessibility of urban parks has increasingly focused on “the behavior patterns and perception paths of actual users” and emphasized the interaction between geographic, transportation, and social spaces [45,46].

2.2. Integration of the “15-Min Living Circle” Concept and Accessibility Measurement

The “15-min living circle” was first proposed in municipal planning in Paris, emphasizing that residents should be able to walk or bike to basic public service facilities, such as parks, schools, supermarkets, and health stations, within 15 min [47,48,49]. Since the adoption of the fourteenth Five-Year Plan in 2021, China has institutionalized community living circle development in national urban governance, with explicit requirements for constructing fifteen-min accessible service zones [50,51]. In recent years, numerous studies have used the “15-min living circle” concept, employing spatial network analysis and service area models to evaluate the layout of urban parks. For instance, Shen et al. (2025) used GIS and network analysis methods to measure the spatial distribution of Wuhan Youth Park and proposed suggestions for optimizing the city’s walking system [52]. Tian (2025) combined walking path data with facility density to propose a comprehensive optimization model linking low-carbon travel with green space layout [53]. Elldér (2025) pointed out that the “15-min living circle” is not only a spatial distribution standard but also an important reference for measuring urban equity and the level of healthy city construction [54]. David Rojas-Rueda et al. (2024) explore the potential of the 15-min city and chronourbanism to enhance sustainability, public health, and equity by improving access to essential services, promoting walkability, and fostering social cohesion [47]. However, the implementation of this concept faces challenges in marginal areas, especially in mountainous regions and urban–rural fringes, where traditional models struggle to account for the complexity of terrain and differences in travel paths. Therefore, combining the “15-min living circle” concept with the TB-G2SFCA method can effectively address this issue, improving the accuracy and fairness of the analysis.

2.3. Evolution and Comparison of Spatial Accessibility Analysis Methods

With the development of GIS and spatial analysis methods, the quantitative study of spatial accessibility has deepened [55]. The two-step floating catchment area (2SFCA) method has been widely adopted due to its comprehensive consideration of service supply and demand relationships and spatial decay effects [56,57]. Building on this, the Gaussian two-step floating catchment area (G2SFCA) method further introduces Gaussian functions to simulate the distance decay process, enhancing the model’s ability to fit real travel behavior [58,59]. For example, Liu et al. (2024) applied the G2SFCA method to assess the accessibility of medical services in Chinese cities and found that this model significantly outperforms traditional methods in reflecting spatial service differences [60]; Kirby and Sharma (2017) also pointed out that G2SFCA allows for more precise localized assessments in high-density urban areas by using different buffer radii [61]. The advantage of G2SFCA is that it balances spatial decay, supply–demand matching, and service capacity but it has an important limitation: it does not fully integrate real travel behavior data [62]. To address this shortcoming, the TB-G2SFCA (travel-behavior-based G2SFCA) method was developed. This method corrects the biases in traditional models in simulating travel paths and behaviors by incorporating travel behavior data (such as travel mode ratios, travel time periods, and travel purposes), thereby more accurately reflecting the accessibility experiences of different groups [63]. The core improvements of this method are:

(1)

Using survey data, mobile data (such as trajectory points), or transportation card records to obtain travel behavior data;

(2)

Averaging different behavior paths to generate individual-level travel probability distributions;

(3)

Adjusting buffer zone weights based on travel probabilities to assess “spatial accessibility under behavioral perception”. In addition, the TB-G2SFCA method is particularly suitable for assessing service facilities in marginal areas, park green spaces, and slow traffic-dominated services, offering greater adaptability and a social equity-oriented approach [64]. This paper summarizes and compares the development of spatial accessibility analysis methods (

Table 1. Evolution and comparison of spatial accessibility analysis methods.

Method	Technical Features	Advantages	Limitations	Applicable Situations
Gravity model	Weighted by population and facility attraction	Theoretical rigor, considers distance and attraction	Complex parameters, high data requirements	Regional macro configuration studies
2SFCA	Two-step facility–population matching	Clear structure, easy to operate	Ignores spatial decay and behavioral differences	Community-level analysis
G2SFCA	Introduces distance decay factor	Results are closer to actual perception	Ignores time factors and dynamic behavior	Research on equity in healthcare and parks
TB-G2SFCA	Combines travel time and behavioral features	Balances time flexibility and individual differences	High data requirements, complex algorithms	Dynamic facility configuration and optimization in large cities

).

In summary, significant progress has been made in the development of accessibility measurement models, particularly the application of G2SFCA and its improved methods in evaluating the equity of urban green space services. However, current research still faces three main shortcomings:

(1)

Most studies focus on core cities or central regions, neglecting border areas like Kunming, with their unique geographical conditions and population distribution characteristics;

(2)

In terms of analysis scale, most research remains at the static physical distance or service radius level, with few dynamic modeling studies based on actual travel behavior;

(3)

Although the “15-min living circle” concept has been widely accepted, research that organically integrates this concept with improved spatial accessibility models (such as TB-G2SFCA) is still limited.

Therefore, this study will combine the “15-min living circle” policy guidance, using Kunming’s border regions as a typical sample, to conduct multidimensional measurement and optimization analysis of urban park accessibility based on residents’ travel behavior characteristics and introduce the TB-G2SFCA method. This is aimed at providing theoretical support and practical pathways for the equitable allocation and sustainable development of green spaces in border cities.

3. Materials and Methods

3.1. Study Area

Kunming is a renowned historical and cultural city in southwestern China and serves as the capital of Yunnan Province [65]. Located on the Yunnan–Guizhou Plateau, it lies on the northern shore of Dianchi Lake and functions as a key hub for connectivity between China and Southeast Asia. Kunming’s administrative jurisdiction comprises 7 municipal districts, 3 counties, 1 county-level city, and 3 autonomous counties, covering a total area of 21,012 km² with a permanent population of 8.46 million [66].

This study focuses on Kunming’s central urban area, particularly the Dianchi Lake Living Circle—a high-density zone encompassing seven core districts: Wuhua, Panlong, Guandu, Xishan, Chenggong, Anning, and Jinning [67]. The central urban area spans 2622 km², with a population density exceeding 4200 persons/km² (Figure 1).

Over the past decade, Kunming has prioritized ecological restoration and urban greening to address environmental challenges resulting from rapid urbanization. According to the Kunming Land and Space Master Plan (2021–2035), the central urban area had achieved 156.8 km² of green space by 2020, including 58.3 km² (37.2%) designated as parkland—featuring landmark projects such as Dianchi Lakeside Park and Cuihu Greenway [68,69].

Although urban green spaces have expanded significantly in Chinese cities, studies highlight persistent inequalities in park accessibility, particularly in rapidly developing areas [70,71]. As a recipient of UNESCO’s “Biodiversity City” designation and the “National Garden City” award, Kunming’s efforts to balance ecological conservation with urban development provide valuable insights for optimizing green space equity. Examining its park planning strategies is therefore crucial for promoting sustainable development in ecologically sensitive urbanized regions.

3.2. Data and Preprocessing

3.2.1. Park Data

Park boundary data were obtained from OpenStreetMap, Gaode Maps, and Baidu Maps using area of interest (AOI) data, while entrance locations were extracted from Gaode Maps’ point of interest (POI) data. First, based on the publicly available park directory from the Kunming Municipal Bureau of Natural Resources and Planning, 238 parks in the central urban area were identified. The addresses of these parks were geocoded using the Gaode and Baidu Maps APIs to convert them into latitude and longitude coordinates.

Next, AOI boundaries and entrance POI points were batch-downloaded via APIs, and OpenStreetMap park vector boundaries were processed using Python 3.9 scripts to automate data handling. The datasets from the three sources were overlaid in ArcGIS and cross-validated against satellite imagery from Google Earth to ensure boundary consistency. Regions with deviations exceeding 10% were excluded, resulting in a final dataset of 238 valid parks (Figure 2).

According to area statistics, the total park area in Kunming’s central urban region amounts to 5285.94 hectares, with individual park sizes ranging from 31,346 square meters (smallest) to 507,529 square meters (largest). The dataset includes a total of 672 entrance coordinates.

3.2.2. Population Data

In China, residential communities serve as fundamental units of urban life [72]. To analyze these units, this study employed Python-based web scraping techniques to extract data from the Kunming subsite of Anjuke (https://kunming.anjuke.com/community/ (24 April 2025)). The extracted data included community names, geographic coordinates, and household counts. These data were then spatially matched with street-level population data from the Seventh National Census, which covers 71 streets in Kunming’s central urban area, with a total population of approximately 5,832,686.

In ArcGIS, the community point data obtained from Anjuke were linked to the census street boundaries. Population density and household counts were assigned to each community based on the corresponding street. Records with abnormal coordinates or zero population (such as certain rural towns in Jinning District) were excluded. Ultimately, valid population data for 71 streets were retained. Missing coordinates or household numbers were supplemented using Gaode Maps’ POI search. Manual verification was conducted to ensure logical consistency between community household numbers and total street-level population, resulting in a data coverage rate of 98.7% and maintaining a spatial matching error within 1 km of the street boundaries.

3.2.3. Modes of Travel

Modes of travel are a core parameter in urban park accessibility analysis, directly influencing the time cost from residential communities to parks [73,74]. This study utilizes resident travel survey data from the Kunming Annual Transportation Development Report (2022), integrating Kunming’s topographical features and transportation policies to determine the proportion of various travel modes and their corresponding speed parameters.

The data reveal that in Kunming’s central districts—Wuhua, Panlong, Guandu, Xishan, Chenggong, Anning, and Jinning—the distribution of resident travel modes is as follows: walking (13.5%), electric bicycles (including shared bicycles) (32.4%), private cars (25.6%), and public transport (buses) (28.5%).

Speed parameters are established based on the Kunming Urban Road Speed Limit Management Regulations and relevant local empirical studies. Walking speed is set at 5 km/h, accounting for the impact of plateau terrain on walking pace; electric bicycles are assigned a speed of 20 km/h, which is slightly higher than in flatland cities; both private cars and buses are set at 60 km/h on major roads, reflecting the influence of significant road slopes on vehicle travel.

According to the Kunming Park Green Space Special Plan (2021–2035), the park accessibility time threshold is defined as 15 min, aligning with the city’s “15-min living circle” development objective. Based on the effective travel distances within this time frame—1.2 km for walking, 5 km for electric bicycles, and 15 km for both private cars and public transport—a multimodal transport network analysis is conducted. This comprehensive analysis helps assess the equity of park accessibility across different modes and identify existing spatial gaps. The results provide a foundation for optimizing both park distribution and transportation linkages (

Table 2. Resident travel behavior data in Kunming’s central urban areas.

Travel Mode	Travel Speed	15-Min Coverage Distance	15-Min Spatial Coverage	Proportion	Applicable Areas
Walking	5 km/h	1.25 km	Circular area with a radius of approximately 1.2 km	13.5%	Wuhua, Panlong, Guandu, Xishan, Chenggong, Anning, Jinning
Electric bicycle	20 km/h	5.00 km	Circular area with a radius of approximately 5 km	32.4%	Wuhua, Panlong, Guandu, Xishan, Chenggong, Anning, Jinning
Public transport	40 km/h	10.00 km	Linear expansion along bus routes	28.5%	Wuhua, Panlong, Guandu, Xishan, Chenggong, Anning, Jinning
Private car	60 km/h	15.00 km	Road network coverage within a 15 km radius	25.6%	Wuhua, Panlong, Guandu, Xishan, Chenggong, Anning, Jinning

).

3.3. G2SFCA Method

The two-step floating catchment area (G2SFCA) method is a widely used approach for evaluating the accessibility of public service facilities. Its core principle lies in assessing the spatial supply–demand relationship within a defined catchment area. By linking service providers (e.g., parks) with service demanders (i.e., the population), the G2SFCA method systematically analyzes the balance between supply and demand through a two-step process to quantify accessibility.

Specifically, the first step involves setting up a catchment area around the service facility and calculating the supply–demand ratio between the service population and facility capacity within that area [75,76]. In this step, for each park’s service point j, the supply–demand ratio (R_j) is calculated using the set of demand points (i) within the catchment area (threshold distance, d₀) of the park’s watershed. This ratio represents the ratio between the park’s area and the total demand population. The formula is as follows:

$$R_j={\displaystyle \frac{S_j}{{\displaystyle {\sum }_{i\in \left\{d_{ij}\le d_0\right\}}^k{D}_i\times G(d_{ij})}}}$$

(1)

S_j is the service capacity of service point j, which here represents the area of the park; D_i is the demand of demand point i, representing the population size, with the centroid located in the watershed area (j) where (d_ij ≤ d₀); d_ij is the distance from demand point i to service point j [77]; and G(d_ij) is the distance friction estimate, which is calculated as follows:

$$G(d_{ij})=\left\{{\displaystyle \frac{e^{-\frac{1}{2}{(\frac{d_{ij}}{d_0})}^2}-e^{-\frac{1}{2}}}{\begin{array}{cc}1-e^{-\frac{1}{2}}& \\ \hfill 0,& d_{ij}>d_0\hfill \end{array}}}\right.,d_{\mathrm{ij}}\le d_0$$

(2)

In the second step, a service catchment area is established around each residential point, and the weighted sum of the supply–demand ratios of all facilities within this area is calculated to determine the service accessibility level of the residential point. This process is implemented using a combination of the Python3.9 programming language and the ArcGIS 10.7 platform. Path and distance calculations are conducted using the Python libraries osmnx and networkx, which access OpenStreetMap (OSM) data to construct an urban road network model and compute realistic travel paths and distances. Subsequently, ArcGIS is used to generate service accessibility level maps, perform spatial visualization, and validate spatial boundaries, thereby enhancing the interpretability and spatial accuracy of the results.

It is important to note that the “percentage” indicator mentioned in the text does not refer to error percentages from regression analysis but rather represents the distribution of residential points across different levels of accessibility. Therefore, the core of this study lies in evaluating the spatial equity of service provision rather than predictive modeling, and traditional regression error metrics such as mean squared error (MSE) or root mean squared error (RMSE) are not involved in the calculation as follows:

$$A_i={\displaystyle \sum _{i\in \left\{d_{ij}\le d_0\right\}}^m{R}_j\times G}\left(d_{ij}\right)$$

(3)

Here, d₀ represents each location within the threshold distance [78]. The accessibility A_i of the watershed area is calculated by summing the supply–demand ratios (R_j) multiplied by the distance friction estimate.

To explore whether spatial inequity of park provisions exists across communities, a normalized and absolute method for spatial equity measurements [79] is introduced to analyze equity in park accessibility (integrated spatial equity evaluation (ISEE)). An equity score can be calculated from the accessibility scores as:

$$E_i={\displaystyle \frac{\max (R_j)}{\max (A_i)}}\times A_i$$

(4)

where E_i is the normalized equity score of community i, representing the balance between supply and demand; max(Rj) indicates the park with the highest supply-to-demand ratio across all service points; and max(Ai) indicates the residential location with the highest level of accessibility among all demand points.

Based on the supply–demand ratio (E_i) and the 15-min living circle development goal, park service equity is divided into six levels as follows:

A Level (E_i > 1): highly redundant resources and the entire area meets the 15-min walking accessibility standard.

B Level (0.75 < E_i ≤ 1): balanced supply and demand, with more than 90% of residents able to reach a park within 15 min.

C Level (0.5 < E_i ≤ 0.75): good service capacity but some areas require a 20-min walking distance.

D Level (0.25 < E_i ≤ 0.5): weak facilities, with over 25% of residents unable to reach a park within 15 min.

E Level (0 < E_i ≤ 0.25): severe resource shortage, with more than half of residents unable to meet basic recreational needs.

F Level (E_i = 0): no park coverage, completely outside the 15-min living circle.

This system, which combines spatiotemporal accessibility differences, accurately identifies the service gaps in parks in Kunming’s central urban areas and supports the optimization of the “15-min Convenient Living Circle” policy (

Table 3. Classification criteria for park accessibility equity.

Level	Supply–Demand Ratio (Ei) Range	Service Equity Description	Accessibility Standard
A	Ei > 1	Highly redundant resources	Full area meets 15-min walking accessibility
B	0.75 < Ei ≤ 1	Supply–demand balance	Over 90% of residents can reach a park within 15 min
C	0.5 < Ei ≤ 0.75	Good service capacity but partial insufficiency	Some areas require a 20-min walking distance
D	0.25 < Ei ≤ 0.5	Weak facilities	Over 25% of residents exceed the 15-min accessibility range
E	0 < Ei ≤ 0.25	Severe resource shortage	More than half of residents cannot meet basic recreational needs
F	Ei = 0	No park coverage	Completely outside the 15-min living circle

).

3.4. TB-G2SFCA Method

When measuring spatial accessibility, the travel behavior of residents is an important consideration. Based on the G2SFCA method mentioned earlier, this study further adopts the TB-G2SFCA method to integrate four different travel modes in order to assess park accessibility.

To implement the TB-G2SFCA method, this study employs ArcGIS 10.7 along with the Python programming language for spatial analysis and accessibility calculations. The Network Analyst extension in ArcGIS is used to process the urban road network and to define the catchment areas based on travel time thresholds (t_n) and travel speeds (v_n) for each of the four travel modes: walking, cycling, public transit, and driving. Custom scripts developed using ArcPy are applied to extend the traditional G2SFCA method by incorporating travel behavior weights, allowing for a more realistic representation of residents’ travel preferences. This technical framework enables accurate assessment of park accessibility under multimodal travel scenarios as follows:

$$d_0(Mn)=t_n\times v_n$$

(5)

where d₀(Mn) represents the threshold travel distance for travel mode Mn; t_n represents travel time threshold) [80]; and v_n represents travel speed.

$$A_{i,Mn}={\displaystyle \sum _{i\in \left\{d_{ij}\le d_0(Mn)\right\}}^m{R}_j\times G(}{d}_{ij})$$

(6)

A_i,Mn represents the spatial accessibility of community i for travel mode Mn.

$$E_{i,Mn}={\displaystyle \frac{\max (R_{j,Mn})}{\max (A_{i,Mn})}}\times A_{i,Mn}$$

(7)

E_i,Mn represents the equity score of travel mode M_n in community i.

The comprehensive equity score is based on a weighted linear combination (WLC) procedure as follows:

$$E_f={\displaystyle \sum _{i=1}^n{W}_{M_n}\times E_{i,Mn}}$$

(8)

where E_f is the comprehensive equity score for residents with travel behavior and W_Mn represents the travel mode choice of urban residents in the corresponding area, calculated based on travel mode M_n.

3.5. Local Indicators of Spatial Autocorrelation

A comprehensive spatial cluster analysis of park accessibility equity in 71 neighborhoods in the central urban area of Kunming was conducted using the Local Moran’s I. The analysis was conducted using GeoDa1.22 software and included multiple detailed steps to ensure the robustness and interpretability of the results.

(1) Spatial data preparation:

The analysis began by calculating the park accessibility index for each community based on the 15-min walking distance principle. To enhance comparability across communities and reduce the influence of scale differences, the accessibility values were standardized using Z-score normalization.

(2) Spatial weights matrix construction:

To define the spatial relationships among communities, a spatial weights matrix was constructed using the queen contiguity (first-order) method, which considers all neighboring units that share either a boundary or a vertex. This approach effectively captures the spatial structure of urban communities and provides a solid basis for spatial autocorrelation analysis.

(3) Local Moran’s I calculation:

Local Moran’s I was calculated for each community to assess local spatial autocorrelation, measuring how similar a unit’s accessibility score is to that of its neighbors [81]. The formula used was as follows:

$$Local Moran’s I={\displaystyle \frac{n\left({\gamma }_i-\overline{\gamma }\right){\displaystyle {\sum }_{j=1}^m{W}_{ij}\left({\gamma }_j-\overline{\gamma }\right)}}{{\displaystyle {\sum }_{n=1}^n{\left({\gamma }_j-\overline{\gamma }\right)}^2}}}$$

(9)

where ${\gamma }_i$ and ${\gamma }_j$ are the equity scores of community i and j; $\gamma$ is the mean of equity scores; n is the number of communities; m is the number of communities around community I; and W_ij is the spatial weight matrix.

(4) Significance testing:

To determine the statistical significance of the observed clustering patterns, a permutation test with 999 Monte Carlo simulations was performed. A significance level of 0.05 was adopted. Communities with statistically significant Local Moran’s I values were classified into distinct cluster types.

4. Results

4.1. Comparative Analysis of Park Accessibility Equity Based on G2SFCA Method for Single Travel Mode

The accessibility equity of urban parks in the Kunming Dianchi Lake Circle area shows notable disparities across different travel modes. The frequency distribution (Figure 3) clearly reveals a polarization in equity scores for walking and cycling modes as follows:

(1) Walking mode: A substantial 57.7% of street communities are unable to access parks within the 15-min living circle (score 0), indicating significant park accessibility issues, particularly for residents in marginal areas. These residents often struggle to benefit from public green spaces. Conversely, only 5.6% of street communities can easily access parks (score > 1), highlighting the uneven distribution of park resources. This disparity is especially pronounced in central urban areas and regions with convenient transportation, where residents enjoy better park accessibility.

(2) Cycling mode: Although cycling extends residents’ mobility range, its effectiveness remains constrained by geographic conditions, particularly in peripheral areas. About 33.8% of street communities still cannot access parks within the 15-min living circle (score 0), while 11.3% can more easily reach park services (score > 1). This indicates that while cycling provides greater flexibility than walking, it still cannot fully address the equity gaps caused by geographic distance. In areas with complex terrain or uneven urban structures, the benefits of cycling are often not fully realized.

In contrast, driving and public transportation modes significantly enhance the equity of park accessibility:

(3) Driving mode: A total of 55.0% of street communities can access parks within a reasonable range (score 0.5–1), with 14.1% of communities surpassing both walking and cycling modes in accessibility (score > 1). This shows that motorized travel allows even more distant communities to overcome geographical constraints and reach parks, resulting in more equitable service distribution.

(4) Public transportation mode: A total of 43.6% of street communities fall within a relatively equitable accessibility range (score 0.5–1), with 14.1% achieving an excellent accessibility (score > 1). This further underscores the advantages of motorized travel in overcoming geographical barriers and expanding park accessibility. However, this convenience may also lead to the concentration of park resources in areas with better transportation access, leaving peripheral areas disadvantaged. Particularly in suburban regions with limited transportation options, issues such as uneven service quality and frequency persist, preventing residents from accessing parks within a reasonable time frame.

Although different travel modes significantly influence park accessibility equity, the limitations of walking and cycling modes remain difficult to overcome, especially in areas with imbalanced geographic layouts and resource distribution, such as remote or less accessible regions.

The spatial distribution (Figure 4) highlights the significance of regional differences in park accessibility. For walking and cycling modes, central urban areas exhibit a clear clustering effect of high-accessibility street communities due to the dense distribution of parks and well-established infrastructure. These areas, with higher park densities and favorable transportation conditions, allow residents to more easily access parks within the 15-min living circle. However, in some high-density communities, increased population pressure leads to intensified resource competition, causing a decline in equity scores. Despite abundant park resources, the excess demand and concentration of resources make it difficult for some residents to equally benefit from park services, reducing overall equity.

In contrast, suburban areas display a “proximity advantage, remoteness disadvantage” pattern. Minority ethnic street communities located near parks have relatively higher equity scores due to their advantageous geographic locations and better park resource allocation. On the other hand, remote communities, located farther from parks, face lower equity scores, as they cannot easily access parks within the 15-min living circle, significantly limiting their residents’ access to park resources.

For driving and public transportation modes, central and northeastern street communities—especially those north of Dianchi Lake—generally exhibit better overall equity compared to the southern areas. This is closely related to the more developed transportation networks and better park distribution in the northeast. With a more developed transportation network and broader park coverage, residents in these areas enjoy more equitable access to park resources. Motorized travel significantly extends the 15-min living circle, improving both park accessibility and equity.

Additionally, motorized travel has reduced the number of “no supply” street communities (light gray areas), mitigating some of the issues caused by uneven resource distribution. By broadening the service range, driving and public transportation modes help distant communities access park resources, improving accessibility in less reachable regions. However, despite these improvements, the equity gap between the northern and southern regions and the resource gap between central and peripheral areas remain. This indicates that while transportation conditions have improved, further optimization of regional resource allocation and transportation network layouts is necessary to achieve more balanced and sustainable park accessibility.

Overall, the equity of park accessibility is influenced not only by the mode of transportation but also by geographical planning and population distribution. The accessibility differences between the four modes highlight the imbalance between central and peripheral areas. While motorized transportation can expand the accessibility range, it may also intensify the concentration of resources in areas with convenient transportation.

To reduce the equity gap between different travel modes and regions, multilevel optimization strategies should be adopted in the future. These include reasonable park planning in suburban areas to ensure that residents in peripheral regions can enjoy more balanced access to green space resources; strengthening the connection between public transportation and parks to improve accessibility for noncar users; and implementing dynamic resource regulation in high-density areas to optimize the supply–demand balance for park services and prevent intensified resource competition due to population pressure.

Through these comprehensive measures, the equitable sharing of urban green spaces can be promoted, allowing different groups to enjoy higher-quality ecological welfare.

4.2. Park Accessibility Equity Spatial Distribution Based on the TB-G2SFCA Method

After further testing with the TB-G2SFCA method, significant differences were found in the spatial distribution of park accessibility equity. Figure 5 shows that most street communities north of Dianchi Lake have relatively superior access to park services, while the southern regions, due to their remote geographic location and relatively poor transportation conditions, result in most communities being unable to reach parks within 15 min. This creates a clear regional imbalance.

Further analysis reveals that different spatial clusters are not isolated but interconnected through transportation accessibility and the distribution of public resources. Particularly in transitional boundary areas, improvements in one cluster can generate spillover effects that enhance park accessibility in adjacent regions. This spatial interplay suggests that localized interventions may yield benefits beyond their immediate zones, contributing to broader regional equity by mitigating disparities across urban–rural and central–peripheral divides.

Specifically, the area north of Dianchi Lake has a higher park density and a more developed transportation network, resulting in better park accessibility equity. Most residents can easily reach parks within the 15-min living circle and enjoy park resources. However, in the southern area, particularly in peripheral street communities far from major roads, park resources are more limited, and even “park supply blank zones” have emerged. This spatial distribution further highlights the limitations of walking and cycling modes. In nonmotorized travel modes, park accessibility is constrained by geographic layout, leading to some communities being unable to fairly enjoy green space services. Especially in the distant suburban communities in the south, residents cannot easily reach parks within the 15-min living circle, leading to an uneven distribution of resources.

Importantly, these imbalanced patterns are not solely attributed to local limitations but are also shaped by the spatial layout and transport configuration of adjacent clusters. In the northern cluster, improvements such as optimized traffic nodes and increased park supply not only reduce internal pressure on public spaces but also radiate outward to enhance accessibility in neighboring southern zones. This compensatory spatial mechanism demonstrates that upgrading one cluster can drive positive changes across cluster boundaries, creating a networked model of regional resource sharing.

The distribution patterns of park accessibility equity in different regions around Dianchi Lake show a strong correlation with the spatial distribution trends of cycling and public transportation modes (as shown in Figure 4b,c). This similarity reflects the profound impact that transportation modes have on the spatial distribution and accessibility of park resources. In areas with well-developed transportation networks, both cycling and public transportation can effectively support residents in quickly reaching parks, promoting more balanced resource allocation and equitable distribution.

What is particularly notable is that transportation infrastructure not only enhances intracluster mobility but also establishes intercluster connections, enabling cross-boundary access to public parks. For communities located at the periphery or in functional transition zones, features such as transit interchanges and greenway corridors help extend the effective service range of parks. This “cross-cluster accessibility enhancement” mechanism reduces spatial fragmentation and promotes shared use of green spaces across administrative divisions, reinforcing the resilience and fairness of the urban system.

This spatial distribution similarity indicates that the choice of transportation mode is not only a tool for reducing physical distance but also plays a key role in enhancing the service radius of parks and reducing the accessibility disparity within regions. Cycling and public transportation show similar trends in resource distribution, revealing the effectiveness of transportation modes in different regions and their impact on the equitable distribution of park resources. Especially in areas with smooth transportation networks, residents’ choice of travel mode can be more flexible, improving park accessibility and effectively enhancing the spatial distribution of social resources, particularly promoting regional social equity in urban planning.

From a systems perspective, the integration of transportation functionality across clusters is essential for promoting spatial justice. The analysis shows that the segmentation of the north–south clusters in terms of transport infrastructure is a major factor contributing to uneven resource access. It is therefore critical to enhance strategic planning at the interfaces between clusters—by building interlinked cycling networks, optimizing transit hubs, and increasing the density of public service facilities in marginal areas—to support the proactive flow of resources and enable functional coordination across clusters.

Moreover, the consistent spatial distribution trend suggests that a single transportation mode cannot fully address the equity challenges in optimizing park accessibility across different regions. Therefore, further integration of various travel modes, especially the effective connection between cycling and public transportation, along with more scientific spatial planning, will be key strategies for enhancing the equity of park accessibility.

Moving forward, a “regional linkage and cluster collaboration” strategy should be adopted to shift from point-based interventions toward a networked system of public space provision. The joint planning of integrated transportation systems and distributed park facilities can not only maximize resource efficiency but also establish a green public space system that bridges urban–rural gaps, ensuring equitable and sustainable development across the city.

4.3. Spatial Cluster Analysis of Park Accessibility in Central Kunming

Based on the results, four types of spatial patterns were detected: high–high (HH) clusters indicating areas with high accessibility surrounded by similar high-access areas; low–low (LL) clusters representing low-accessibility areas adjacent to similarly low areas; high–low (HL) and low–high (LH) outliers, reflecting spatial inconsistencies; and nonsignificant areas where clustering was not statistically detectable (

Table 4. Cluster type value for spatial fairness.

Clustering Type	Frequency	Proportion	Color
HH	28	39.5%	Pink
HL	1	1.3%	Red
LL	3	4.2%	Blue-hrey
LH	13	18.5%	Dark blue
NS	26	36.6%	Grey

and Figure 6). These spatial clusters reveal notable disparities in park accessibility equity across different communities.

HH cluster (pink, 28 street communities, 39.5%): This cluster represents communities with relatively high fairness in park accessibility, surrounded by other communities with similarly high accessibility, forming a distinct high-value concentration area. A total of 28 street communities belong to this cluster, accounting for 39.5% of all observed communities. These areas are primarily located north of Dianchi Lake and in parts of the city center, where parks are densely and evenly distributed, the transportation network is well-developed, and walking and cycling conditions are favorable. Residents can reliably access parks within a 15-min living circle and enjoy high-quality green spaces. These communities also benefit from mature public infrastructure and comprehensive functional development, further enhancing the fairness of accessibility. In daily life, residents continuously enjoy the benefits of convenient travel options and a high level of spatial comfort.

A deeper analysis shows that the high park accessibility in HH clusters not only positively impacts the quality of life for residents in these areas but can also directly enhance park accessibility in neighboring regions. By improving the connectivity and optimization of park resources within the region, areas with lower accessibility (such as the LL or HL clusters) can also benefit, especially when transportation and infrastructure are upgraded. For instance, interregional walking and cycling networks, along with the integration of bus and rail transit routes, can significantly improve park accessibility for surrounding communities in the short term, helping to reduce spatial imbalances between regions.

Optimizing the interaction effects between clusters can effectively promote fairness across regions, facilitating the sharing and balanced distribution of urban green space resources. Furthermore, transportation connectivity plays a crucial role in this process. A well-connected transportation system makes travel more convenient for residents, particularly those in low-income or low-accessibility areas, thereby shortening travel times and improving their quality of life. Therefore, enhancing the transportation network not only promotes fairness in park resource allocation but also helps reduce social inequality, fostering overall social harmony and equity.

LL cluster (bluish fray, 3 street communities, 4.2%): This cluster consists of communities with low fairness in park accessibility, surrounded by other low-accessibility communities, forming a distinct low-value concentration zone. It includes 3 street communities, accounting for 4.2% of the total, primarily located south of Dianchi Lake and in some suburban areas. These regions face significant challenges, such as a scarcity of park resources and poor accessibility. In the southern areas, complex terrain, high population density, and a limited number of parks contribute to low accessibility within the 15-min living circle. Suburban communities also lack efficient public transportation connections, making it difficult for residents to reach parks within a reasonable time, further exacerbating inequities in accessibility.

Further spatial analysis suggests that improving the LL cluster requires not only optimizing the layout of parks within the area but also considering the synergistic effects with nearby high-fairness regions (such as the HH cluster). By implementing interregional infrastructure integration measures—such as efficient public transport, shared bicycles, and walking paths—accessibility for LL areas can be directly enhanced while promoting the joint development of adjacent high-fairness regions. For example, improving park resources and transport accessibility in LL areas can attract external resources and increase internal mobility, thereby improving the quality of life and overall accessibility fairness in surrounding communities. In this process, transportation connectivity directly affects residents’ social mobility and quality of life. A lack of efficient transport links exacerbates social inequality, especially in low-income and poorly connected areas where residents’ mobility is limited, thus restricting their access to public resources. Therefore, improving the transportation network can effectively break the vicious cycle of poverty and resource scarcity, offering residents in low-accessibility areas greater opportunities and a higher quality of life.

HL and LH outliers (HL: red, 1 street community, 1.3%; LH: dark blue, 13 street communities, 18.5%): These outliers highlight the spatial heterogeneity of park accessibility fairness. HL areas (1.3%) represent high-fairness communities surrounded by low-fairness ones, while LH areas (18.5%) are low-fairness communities surrounded by high-fairness areas. These spatial patterns often appear in transitional zones where park resources are concentrated but service coverage is uneven or where geographical features, administrative boundaries, or transport barriers create accessibility gaps. For example, some park-rich communities directly benefit from nearby green spaces but due to fragmented street networks or poor connectivity, adjacent communities cannot access the same level of service within a 15-min living circle. Conversely, in LH areas, large parks may exist nearby but only a small portion of residents can benefit, while the surrounding communities still lack equal access and must travel longer distances.

The unique spatial layout and distribution of park resources in HL and LH areas expose the imbalance in resource allocation within urban regions. Improving the transportation network and street connectivity in HL areas can directly enhance the accessibility of neighboring LH areas and reduce the disparities between them. Meanwhile, the high-quality park resources in HL areas can promote balanced distribution through sharing and interaction mechanisms, such as interconnected public spaces and greenway networks, thereby improving overall citywide accessibility fairness and addressing spatial segregation issues. Moreover, transportation connectivity plays an especially significant role in these outlier regions. If HL areas can be linked with LH areas through well-developed transport networks, not only can the disparity in park accessibility be balanced, but overall urban social inclusiveness can also be enhanced. For residents in LH areas, improved transport accessibility brings greater social mobility, enabling them to access more equitable resources and opportunities, ultimately improving their quality of life.

Nonsignificant areas (gray, 26 street communities, 36.6%): This category includes 26 street communities, accounting for 36.6% of the total, where park accessibility fairness does not show a clear pattern of clustering or dispersion. These areas do not belong to any distinctly high- or low-value clusters and exhibit relatively balanced accessibility results. Some residents can access parks within the 15-min living circle but overall park resource distribution remains uneven. Although these communities are not as extreme as the HH or LL clusters, they still require further spatial optimization and improved transport connectivity to enhance accessibility and ensure equitable park use.

Improvements in these nonsignificant areas should not be limited to internal optimization within a single cluster, but rather focus on reallocating citywide green space resources and promoting interregional coordination effects. By integrating transportation and park resources from surrounding high-fairness areas (such as HH regions), the park accessibility of nonsignificant areas can be significantly improved, thereby alleviating overall imbalances in urban green space distribution. Optimizing public transport and green mobility systems, especially in areas with poor transport, will improve park accessibility for residents in a short time and promote fair sharing across regions. Through enhanced transportation connectivity, these nonsignificant areas can achieve better resource sharing with other regions, narrowing the quality-of-life gap between areas and improving overall social equity and residents’ well-being.

By introducing the perspective of the 15-min living circle, the differences in park accessibility equity across different types of communities in daily life become clear. Further optimization efforts should focus on the reasonable layout of park resources and the improvement in transport infrastructure, especially in low-value clusters and poorly connected areas, to enhance accessibility and ensure that all residents can access parks within 15 min—thereby achieving more inclusive urban green space sharing. At the same time, policies should emphasize cooperation and resource sharing between different clusters, optimizing the imbalance in park accessibility, eliminating spatial segregation and inequality, and improving the overall accessibility of the city. Transportation connectivity, as a key factor, not only directly affects park accessibility but also determines residents’ social mobility and quality of life. Therefore, improving transport networks—especially in low-accessibility areas—is a crucial step toward achieving social equity and enhancing residents’ quality of life.

5. Discussion

5.1. Methodological Contributions

This study employs the TB-G2SFCA method to assess park accessibility equity in the Kunming city area around Dianchi Lake and compares it with the traditional G2SFCA method. By incorporating multiple travel modes into the analysis, the TB-G2SFCA method allows for a more granular evaluation of accessibility across different urban regions.

In the case of walking and cycling modes, the results show that 57.7% of street communities cannot access parks within the 15-min walking radius and 33.8% of street communities remain underserved in terms of cycling access. These figures highlight the stark contrast between different travel modes. Walking and cycling, while environmentally friendly, are highly dependent on urban infrastructure, which in some peripheral areas is underdeveloped. This significant disparity underscores the limitations of using traditional G2SFCA methods, which fail to differentiate between these subtle differences in accessibility that are shaped by varying levels of infrastructure and geography.

For example, the TB-G2SFCA method reveals that in walking mode, communities located in areas farther from transportation hubs or main roads face considerable barriers to accessing parks. This is particularly evident in the south of Dianchi Lake, where geographical constraints and lower park density combine to reduce accessibility, especially for walking and cycling users. This analysis reveals a more nuanced understanding of park accessibility, as traditional methods would overlook such disparities, leading to a less accurate assessment of accessibility equity.

In contrast, public transportation and driving modes show a more equitable distribution of park access, with 55.0% of street communities in driving mode and 43.6% in public transportation mode experiencing relatively balanced access. These results indicate that motorized transport systems, while more effective in overcoming geographical barriers, tend to concentrate resources in areas with better transportation infrastructure, thereby exacerbating accessibility disadvantages in peripheral regions. This imbalance suggests that while motorized modes provide equitable access to some residents, they also contribute to a cycle of resource concentration in more developed areas, leaving suburban and rural communities at a disadvantage.

A deeper examination of the data indicates that in areas north of Dianchi Lake, accessibility is generally good due to a higher density of parks and more robust transportation networks. Conversely, the southern region experiences greater inequities, which are particularly evident in the walking and cycling modes. This analysis underlines the importance of considering transportation infrastructure alongside park density when assessing park accessibility equity. The TB-G2SFCA method, with its refined ability to incorporate travel mode data and geographic considerations, provides a clearer picture of these inequities than traditional methods.

Although the TB-G2SFCA method provides more precise results, it also requires high-quality, detailed data on transportation networks, travel behavior, and regional spatial layouts [82]. For instance, a more granular breakdown of transportation usage across different modes (such as peak versus off-peak times) could further refine the analysis. This would allow policymakers to target interventions more effectively, such as optimizing bus routes during high-traffic periods or improving cycling infrastructure in underserved areas.

In conclusion, the TB-G2SFCA method’s ability to differentiate park accessibility based on diverse travel modes leads to a more accurate understanding of equity in urban green spaces. The method not only identifies regions with high levels of accessibility but also highlights the underlying factors—such as transportation infrastructure and park density—that contribute to disparities in access. These findings are crucial for future research and urban planning, as they offer deeper insights into how cities can optimize their park accessibility and resource distribution to ensure fairness across all urban communities.

5.2. Implications for Urban Development and Planning

This study, through the application of the TB-G2SFCA method, reveals significant disparities in park accessibility across the Kunming city area around Dianchi Lake, influenced by factors such as geography, transportation networks, and travel modes. These findings point to the need for comprehensive strategies to enhance equity in park accessibility, with a focus on specific, actionable policy measures.

(1) Optimizing park resource layout

One of the key findings of the study is the disparity in park accessibility between the city center and peripheral areas, particularly in the regions south of Dianchi Lake. To address this, urban planners should focus on redistributing park resources more equitably [83,84]. A systematic approach can be adopted, starting with conducting detailed needs assessments in underserved areas to identify the most pressing gaps in park access. Based on these findings, planners can prioritize park development in those regions, ensuring that every community, especially in peripheral and suburban areas, has fair access to green spaces [85]. Additionally, integrating parks with new residential and commercial developments in these areas can ensure a more even distribution of green spaces throughout the city.

(2) Enhancing transportation and infrastructure connectivity

The study also highlights the limitations of walking and cycling modes due to insufficient infrastructure, particularly in geographically constrained areas. To improve accessibility, it is essential to enhance the transportation network, particularly for walking and cycling [86]. Expanding pedestrian pathways and cycling lanes in areas with low park access can help ensure that these modes become viable options for more residents [87]. Moreover, strengthening the interconnection between public transportation and park locations will ensure that residents in peripheral areas can easily reach parks via public transportation. Improving the coverage and frequency of buses or other forms of mass transit in underserved areas will further facilitate equitable access to parks across the city.

(3) Addressing resource concentration in well-served areas

Another issue identified by the study is the over-concentration of park resources in areas with better transportation access, which exacerbates inequities. To mitigate this, planners should implement policies aimed at distributing park resources more evenly. This could include using zoning regulations to prevent excessive development in well-served areas while encouraging the construction of parks in underserved regions [88]. Public–private partnerships could also play a key role in financing park development in these areas [89]. Furthermore, planners should ensure that new urban developments incorporate green spaces, avoiding the concentration of parks in already well-served neighborhoods and promoting a more balanced distribution.

(4) Developing comprehensive green spaces

Looking beyond traditional park spaces, urban planning should focus on the development of multifunctional green spaces that serve a variety of purposes. These spaces should not only provide recreational areas but also support environmental sustainability and integrate transportation functions. By creating green spaces that serve multiple purposes, cities can meet residents’ recreational needs while also contributing to environmental goals and urban livability [90]. The development of comprehensive green spaces should prioritize easy accessibility, connectivity to transportation networks, and integration into the urban fabric, ensuring that these spaces are fully utilized and serve the broader community.

Through these strategic recommendations, urban planners can make meaningful progress in reducing the disparities in park accessibility, creating more equitable, sustainable, and livable urban environments for all residents.

6. Conclusions

This study uses the TB-G2SFCA method to assess park accessibility equity in the Kunming city area around Dianchi Lake. Compared to the traditional G2SFCA method, the TB-G2SFCA method more comprehensively accounts for the complexity of transportation networks and spatial aggregation effects, providing a more precise analysis of park accessibility equity. The results reveal significant differences in park accessibility across different travel modes, particularly in walking and cycling modes, where park accessibility shows clear polarization. Within the 15-min living circle framework, high-scoring street communities in walking and cycling modes are concentrated in park-rich areas, while some peripheral communities, far from parks, are unable to access the same level of green space services.

In driving and public transportation modes, motorized travel significantly improves park accessibility equity, particularly in areas with well-developed transportation networks, where the accessible range of park services is effectively expanded. However, this convenience also intensifies the concentration of resources in transportation-convenient areas, further highlighting the resource gap between the northern and southern regions and between central and peripheral areas. Specifically, peripheral street communities south of Dianchi Lake, constrained by poor transportation options, still face disadvantages in park accessibility.

The findings of this study offer valuable insights for optimizing urban planning and public policies. Future efforts should focus on the rational distribution of park resources and improving the connectivity of transportation networks, especially in suburban and underserved areas, to promote more equitable access to urban green spaces. A more detailed analysis of local population structures—such as socioeconomic status, age, and mobility constraints—will enable urban planners to better understand the specific needs of different groups. This will facilitate the design of targeted interventions to address the inequities identified by the TB-G2SFCA method.

Additionally, future research should consider individual and group differences by incorporating factors like demographic structure and socioeconomic status, as these elements can significantly affect accessibility outcomes. Understanding the variations in accessibility needs and barriers between different social groups can help tailor urban policies to be more inclusive. For instance, elderly and low-income populations may face unique challenges in accessing parks, and addressing these needs could improve equity in urban green space access. Acknowledging these variations will lead to a more nuanced understanding of how park accessibility impacts different social groups and will inform more targeted policy interventions.

This study also provides new directions for future research on green space, particularly in how fine-grained spatial analysis and multilayered travel mode integration can improve park accessibility equity. However, the study has some limitations, mainly due to the static nature of the data and individual differences in travel behavior. Future research could integrate dynamic data, microenvironmental factors, and residents’ behavioral habits, employing more refined models to comprehensively assess the equity of urban green spaces. By incorporating real-time mobility patterns, land-use changes, and evolving resident needs, researchers could develop more adaptive models that reflect the changing dynamics of urban environments. These improvements would make future urban green space planning more scientific, accurate, and effective in enhancing residents’ quality of life and promoting sustainable urban development.