A Case Study on Children’s Accessibility in Urban Parks in Changsha City, China: Developing an Improved 2SFCA Method

Abstract

As countries develop, the challenge of providing access to the outdoors and nature increases. Consequently, recent environmental justice research has focused on measuring children’s access to parks. The results of these analyses better reflect differences in accessibility, but there are discrepancies between different accessibility models. This study aims to explore child-friendly accessibility measures and proposes a supply–demand-improved two-step floating catchment area (2SFCA) method for estimating urban park accessibility based on children’s needs. The application of this improved 2SFCA method in Changsha City successfully identified areas with unequal park accessibility, offering valuable insights for urban planners, that can be used to promote equitable access to green spaces for all residents, especially children. The results demonstrate that park accessibility in Changsha City exhibits significant differences across various areas, with the lowest accessibility in the western part of Furong District, the northwestern part of Yuhua District, and the southern part of Tianxin District; while the highest accessibility is found in Yuelu District. The limited green space in the central business district of Changsha City, coupled with high population density, indicates a tension between green space planning and population density in the city’s central area. The study proposes that the primary challenge in current green space planning in Changsha is the rational allocation of green spaces to meet the needs of high-density populations within limited urban space. It provides a comprehensive and realistic perspective for understanding the accessibility and availability of green spaces for children, which can help urban planners develop effective policies to support children’s outdoor mobility, while considering equity.

1. Introduction

As the process of urbanization accelerates, the urban population has significantly increased and it is projected that, by 2030, China’s urbanization rate will exceed 80% [1]. In China, 62.7% of children live in cities [2]. This paper adopts the United Nations Convention on the Rights of the Child (CRC) definition, which defines a child as a person under 18 [3]. However, the current urban environment may not be the most conducive to their healthy development. Research indicates a concerning trend: children’s physical activity levels are declining [4], particularly in highly urbanized areas. Research from the United States and Australia shows that children’s outdoor activity time is decreasing [5,6], and this trend is also evident in China [7]. Children’s development is increasingly challenged by the decline in opportunities for outdoor play [8,9], physical activity [10], and connection with nature [11,12]. Relatedly, the per capita green space in China is only 6.52 m² [13], while in the United States, the national average per capita green space is approximately 50.18 m² [14]. This indicates that the decline in physical activity levels and inadequate use of green space pose significant challenges to the physical and mental health of children in China. To address this issue and ensure that children have sufficient time for rest and outdoor activities, the Chinese government issued the “Opinions on Further Reducing the Burden of Homework and Off-Campus Training for Students in Compulsory Education” in 2021 [15]. The document aims to reduce academic pressure on students and increase their opportunities to participate in outdoor activities.

Urban parks are crucial in mitigating the challenges posed by inadequate green space and declining physical activity levels. These parks offer essential benefits, including spaces for children to engage in play [16], social interactions [17], and experiences with nature; elements that are fundamental for their overall development [18]. Additionally, parks contribute to enhancing residents’ quality of life and fostering social cohesion within communities [19,20,21,22]. Despite the recognized benefits of urban green spaces, there remains a significant gap in research on how children use parks and green spaces. Existing studies primarily focus on accessibility to schools [23,24], but there is a notable lack of research considering accessibility to different types of destinations, such as parks and green spaces, or simultaneously assessing multiple opportunities [25], highlighting the need for more research in these critical public spaces. Some researchers have begun addressing this issue by using an improved Two-Step Floating Catchment Area (2SFCA) method to capture children’s preferences and characteristics regarding parks [14]. However, these studies still have room for improvement, particularly in refining quality indices that measure park attractiveness.

Additionally, traditional accessibility measurement methods often fail to adequately reflect the unique needs and behaviors of children [26]. For example, current methods may not fully consider factors such as park attractiveness, the safety of walking and biking routes, or the specific needs of different age groups [25]. Addressing these shortcomings is crucial for promoting equitable use of urban green spaces, supporting children’s active travel, and enhancing overall health.

In this context, studying Changsha is particularly significant. As a rapidly growing urban center in China, Changsha offers a unique opportunity to explore the challenges and opportunities related to park accessibility for children. The city’s high urbanization rate and increasing population density have intensified pressures on existing green spaces, making it an ideal case study for evaluating current accessibility measures and identifying specific needs and gaps. Moreover, the diverse socio-economic landscape of Changsha provides a comprehensive view of how various factors influence park accessibility and children’s well-being. By focusing on Changsha, this study aims to generate insights that are directly relevant to similar urban areas in China, contributing to more effective urban planning and policy development tailored to improving children’s access to quality green spaces.

This study aims to provide a more accurate assessment of park accessibility for children by presenting an improved 2SFCA method tailored to their unique needs and preferences. Specifically, the research objectives are: (1) to develop a new 2SFCA method that incorporates quality indicators oriented toward children’s needs, enhancing the accuracy of park accessibility assessments; (2) to provide a detailed analysis of park accessibility in Changsha City, considering spatial differences and park quality; (3) to offer policy recommendations to improve park accessibility and enhance the well-being of children, thus promoting the equity and sustainability of urban green space systems.

The rest of the paper is organized as follows. Section 2 reviews the literature on children’s accessibility and reachability measures. Section 3 describes the improved 2SFCA method. Section 4 details the study area and the sources of data. Section 5 presents the results of the study and analysis. Section 6 discusses the strengths, weaknesses, and limitations of the research method and summarizes the findings.

2. Literature Review

2.1. Children’s Accessibility

Research indicates that participating in activities within green spaces such as parks is associated with increased physical activity levels in children [27], as well as improvements in their mental well-being [28], while also lowering the risks of obesity [29], sedentary lifestyles [30], and psychological problems [31]. Moreover, parks offer valuable chances for social interaction and emotional regulation [32], which contribute positively to children’s overall well-being. However, for children to fully benefit from these opportunities, it is essential that parks are easily accessible. Without easy access, these beneficial activities may become difficult or impossible for children to participate in.

Accessibility refers to how effectively land use and transport systems allow individuals or groups to reach and participate in activities or visit destinations using different modes of transportation [25]. It includes both the capacity to visually perceive these areas and the right to enter and navigate them freely [33]. Children’s travel patterns differ significantly from those of adults. A major difference is that children are legally prohibited from driving motor vehicles on the roads, which directly affects their travel patterns and accessibility [25]. In addition, children frequently travel to different destinations than adults. Because of these differences, children are often marginalized in transport planning [34,35]. The lack of consideration for children in transport planning partly responds to findings from several studies that point to low levels of child-independent travel (CIM) [36]. The term CIM was coined by Hillman et al. in 1990 [37] and is defined as “children traveling freely in their neighborhoods or cities without adult supervision” [38]. CIM has been defined differently by various scholars as “independent travel to a range of destinations,” [39] “independent walking to and from school,” and [40] “independent outdoor play” [41]. Walking is an important component of children’s independent mobility, and children must be able to walk safely for their personal development, community participation, and environmental sustainability [38]. The declining importance of walking may harm children’s independence and community vitality [39]. Consideration of unorganized leisure time physical activity, including CIM, is therefore important.

Research has demonstrated that quality (including park character and condition) significantly impacts children’s park visits [42]. Two recent studies have made important advances in assessing children’s accessibility to parks [5,43] by conceptualizing park accessibility and modeling children’s use of parks using an integrated geospatial approach.

2.2. Accessibility Assessment

When studying park accessibility, traditional methods of accessibility assessment usually use a fixed distance method to calculate the number of destinations that can be reached from a hypothetical starting point. For example, Talen and Anselin (1998) [44] calculated the average distance from all starting points to destinations, while Smoyer-Tomic (2004) [45] and Nicholls (2001) [46] used a fixed distance of 0.8 km as a reasonable walking threshold. Another common approach is to calculate the distance to the nearest facility, as in the studies by Hewko (2002) [47], Higgs et al. (2012) [48], and Lu (2014) [49]. These methods focus on simple physical distances and the number of facilities.

However, these basic assessment methods have limitations in explaining differences in mobility across user groups. As a result, later studies have introduced more sophisticated measurement models such as container approaches [50], buffer analyses [46], kernel density estimations [51], and network-constrained service area methods [52]. These models provide a more detailed picture for accessibility assessment by analyzing the regional population within a specific distance. In addition, the Thiessen polygon method identifies underserved and potentially congested areas by assuming that all people choose the nearest facility [53]. To overcome the limitations of these models, the gravity model and the 2SFCA approach introduce factors such as facility attractiveness and access difficulties to provide a more dynamic and realistic assessment of accessibility [54]. This research method is currently used in healthcare access studies [55], public transport [56], and green space access studies [57] (

Table 1. Accessibility measurement methods.

Method	Author(s)	Characteristics	Advantages	Disadvantages	Application Area
Buffer Analysis	Bertram et al. (2015) [58]	Simple calculation	Simple calculation	Ignores the suppressive effects of spatial road networks	Green space accessibility
Gravity Model	Xia et al.(2018) [59]; Xia et al. (2022) [60]; Chang et al. (2011) [61]	Provides a comprehensive method for calculating park area and distance	Considers both distance and park area	Different metrics are chosen	Green space accessibility
Network Analysis	Wolff(2021) [62]	Depends on a complete traffic system network with complex calculations	Accurate results	Depends on a complete traffic system network, complex calculations	Transportation accessibility
2SFCA	Radke et al. (2000) [63]; Wu et al. (2020) [64]; Luo et al. (2003) [54]	Considers facility supply capacity, resident demand, and spatial impedance, facilitating accessibility calculation	Considers facility supply capacity, resident demand, and spatial impedance, facilitating accessibility calculation	Ignores distance decay effects and actual resistance within the catchment area	Green space accessibility, medical facility accessibility, commercial facility accessibility
Gaussian Two-Step Floating Catchment Area (G2SFCA)	Dai (2010) [55]; Xiao et al. (2021) [65]; Dong et al. (2022) [66]	Introduces Gaussian function, optimizes the 2SFCA method, considers different spatial decay rules and diverse transportation modes	Improves accessibility calculation accuracy, considers different spatial decay rules and diverse transportation modes	Requires consideration of different spatial decay rules and diverse transportation modes	Medical facility accessibility, commercial facility accessibility, urban open space accessibility

).

2.3. Enhancements to the 2SFCA Method

The 2SFCA method for assessing service facility accessibility identifies underserved areas by considering both supply and demand, as well as the distance between them. Scholars have made several improvements to this method to obtain better results. These improvements focus mainly on four areas [14]: distance decay [67,68], search radius [69], quantifying supply or demand [57,70], and multiple travel patterns [71,72]. Therefore, enhancing supply and demand calculations and considering the characteristics of different populations may be a meaningful research direction for assessing spatial accessibility, better identifying underserved areas, and designing effective planning and policies (

Table 2. Improved 2SFCA methods.

Improvement Aspect	Method	Proposer(s)	Characteristics	Description
Distance Decay Problem	E2SFCA	Luo et al. (2009) [67]	Time Interval Segmentation	Divides service areas into multiple time intervals and assigns different weights.
	G2SFCA	Dai (2010) [55]	Gaussian Function	Uses a Gaussian function to assess potential accessibility, with a continuously decreasing supply–demand ratio based on distance.
	KD2SFCA	Dai (2011) [68]	Kernel Density Function	Uses a kernel density function to assess potential accessibility, with a continuously decreasing supply-demand ratio based on distance.
	E2SFCA	Kanuganti et al. (2016) [73]	Distance Decay Impedance Function	Introduces a distance decay impedance function to distinguish the accessibility of medical services in rural areas.
Search Radius Extension	V2SFCA	Luo et al. (2012) [69]	Variable Search Radius	Adjusts the search radius to cover a sufficient supply–demand scale.
	D2SFCA	McGrail et al. (2014) [74]	Dynamic Search Radius	Sets different search radii based on regional population density.
	MC2SFCA	Tao et al. (2014) [75]	Multiple Service Area Radii	Sets different search radii based on the scale of public facilities.
Supply and Demand Quantification	VFCA	Dony et al. (2015) [57]	Variable Width FCA	Calculates attraction coefficients based on park size and facilities but does not consider competition between service sites.
	3SFCA	Wan et al. (2012) [76]	Three-Step Floating Catchment Area Method	Reduces the overestimation of population demand by dividing service areas into several sub-zones and assigning Gaussian weights.
	Huff Model FCA	Luo (2014) [77]	Huff Model	Quantifies demand by expressing the probability of service choice.
	i2SFCA	Wang (2018) [70]	Inverse 2SFCA	Extends from the Huff model to capture the “congestion” of facilities.
Multiple Transportation Modes	MM2SFCA	Mao et al. (2013) [71]; Xing et al. (2018) [72]	Multiple Transportation Modes	Traditional 2SFCA methods consider multiple transportation modes using weighted average travel time.
	CB2SFCA	Fransen et al. (2015) [78]	Commuting-Based 2SFCA	Assumes that demand is not fixed but varies according to commuting behavior.

).

3. Methodology: The Enhanced 2SFCA Method Based on Supply and Demand

In this section, we present an enhanced approach to the traditional 2SFCA method; we have improved the calculation of the supply and demand coefficients $S_j$ and Pi to assess park spatial accessibility for children. To calculate $S_j$, park size and quality are considered by introducing a quality index related to children’s outdoor activities. The demand coefficient Pi incorporates the probability choices, Prob_i, by utilizing the Huff model. The specific enhancements in assessing spatial accessibility, including the coefficients and their equations, are as follows.

3.1. Enhancement of Supply

The scale of a park is an important part of its service capacity. In the traditional 2SFCA calculation, $S_j$ is often directly multiplied by the park area [14]. To better align with the actual situation in the study area, this research replaces the park area with park capacity to better reflect the park’s supply capacity. The calculation method for park capacity is the park area divided by the per capita park green space area. According to the Changsha Urban Green Space System Planning (2021–2035) released in 2023 [79], the per capita park green space area is 12 square meters, which was used to determine the park’s capacity. The per capita park green space area is drawn from an official urban planning document issued by the Changsha city government. As a key benchmark for local green space allocation, this figure ensures that our calculation aligns with the city’s planning standards and urban development policies.

To better consider the attractiveness of parks to children, this study designed a new scale to assess the spatial quality of parks based on the QUINPY [14,43], Woolley and Lowe Tool [80,81], and a literature review on the current situation of Changsha urban parks, current child-friendly policies, and interviews with local city research offices. The scale includes two categories (play diversity and enticing factors), comprising 12 variables with assigned maximum scores (

Table 3. Park quality indicator values associated with child characteristics.

Categories	Variables	Maximum Value
Play diversity	Playground number	2
	Sport fields	2
	Sport courts	2
	Hiking and walking trails	1
	Public swimming pool	1
	Supporting facilities	2
	Ornamental water features	1
	Recreational water features	1
Environmental characteristic	Entrances	1
	Boundaries	1
	Supervision areas	1
	Available for all age groups	1

). The first eight variables assess environmental quality, and the last four evaluate environmental attractiveness. The highest weighted play diversity emphasizes structural facilities suitable for and attracting children’s play, as well as the inclusivity of urban parks, reflecting the unique needs of children. The maximum values assigned to each variable in Table 3 are adapted from established evaluation frameworks, particularly the QUINPY [14,43] and Woolley and Lowe Tool [80,81], which are widely used in similar studies. These values provide a reference to quantify aspects of park quality related to children’s needs, but they are not intended as absolute measures. Adjustments could be made based on specific study areas.

The attractiveness index is calculated from the mass Sj and the attractiveness index is calculated from the mass Sj by the following procedure:

$$S_j=S_j^A\times {\mathrm{q}}_w$$

(1)

where $S_j$ is the attractiveness index, $S_j^A$ is the park capacity, and ${\mathrm{q}}_w$is the park score.

3.2. Enhancement of Demand

To avoid errors arising from ignoring competition between multiple available public sites, Luo (2014) [77] incorporated the Huff model into the FCA method to account for the impact of capacity variations among public sites and adjusted the selection weights to address the issue. The quality and size of a park affect people’s choice probability, as they may be more inclined to choose larger parks with more recreational facilities. Therefore, in this study, the Huff model was introduced in the 2SFCA, which calculates the choice probability ${Prob}_i$ for child, based on park size and park quality.

The Huff function is commonly used in business analysis to predict the probability of a consumer choosing one location over another [82]. The output of the Huff model depends on the choice of key parameters in the model, i.e., attractiveness index and distance decay index. Studies utilizing the Huff model usually set these two parameters based on previous recommendations and do not calibrate them for specific settings [83]. Replace the distance D in the Huff function with a Gaussian decay function $G\left(d_{ij}\right)$ that accounts for spatial friction, which can more accurately reflect the distance decay effect in real situations. The calculation process is as follows:

$${Prob}_i={\displaystyle \frac{S_{j}G\left(d_{ij}\right)}{{\sum }_{k\in \left\{d_{ij}\le d_0\right\}}{S}_{j}G\left(d_{ij}\right)}}$$

(2)

$$G(d_{ij})={\displaystyle \frac{{\mathrm{e}}^{-\frac{1}{2}\times {\left(\frac{d_{ij}}{d_0}\right)}^2}-e^{-\frac{1}{2}}}{{1-e}^{-\frac{1}{2}}}} \times \left(d_{ij}<d_0\right)$$

(3)

where, ${Prob}_i$ is the probability of visiting zone j by the population of zone i in the Huff model based on the model, and $d_{ij}$ is the road network distance between locations i and j. For parks with multiple entrances, the road network distance from the demand unit to the nearest entrance is selected, and unit k needs to fall within the search domain (i.e., $d_{ij}$ ≤ $d_0$). The Gaussian function $G\left(d_{ij}\right)$ denotes the distance impedance coefficient and $S_j$ denotes the attractiveness of zone j.

Using the 2SFCA method in conjunction with the Huff model, the ratio $R_j$ representing the balance between supply and demand for park j is computed as follows:

$$R_j={\displaystyle \frac{S_j}{{\sum }_{k\in \left\{d_{ij}<d_0\right\}}{Prob}_{ij}{P}_{i}G\left(d_{ij}\right)}}$$

(4)

where $P_i$ represents the population at location i.

3.3. Spatial Accessibility to Parks

Spatial accessibility is estimated by summing the supply–demand ratio of $R_j$ and weighted by the distance attenuation coefficient (G) and the selection probability, Prob_ij. Spatial accessibility to parks is defined as:

$$A_i=\sum _{l\in \left\{d_{ij}<d_0\right\}}{Prob}_{ij}{R}_{j}G\left(d_{ij}\right)$$

(5)

The improved method in this study enhances the accuracy of estimating the probability that children will select a particular park, by considering both park size and quality. This calculation method is not meant to replace other improved methods but rather to provide an alternative that more effectively captures how park size and environmental quality influence children’s park choices.

4. Materials

4.1. Study Area

Changsha City has six districts, one county, and two county-level cities: Furong District, Tianxin District, Yuhua District, Kaifu District, Yuelu District, Wangcheng District, Changsha County, Ningxiang County, and Liuyang County, as illustrated in Figure 1. The city has a total area of 11,816 square kilometers and a population of 10.05 million. Among the city’s permanent residents, the population aged 0–14 is 1,672,202, accounting for 16.64%. Compared to the sixth national census in 2010, the proportion of the 0–14 age group has increased by 3.07 percentage points [84]. According to the Changsha City Master Plan (2003–2020) [85], the central urban area of Changsha City includes all of Furong, Kaifu, Yuhua, and Tianxin Districts, the vast majority of Yuelu District, the Gold Township and Leifeng Township of Wangcheng District; and the towns of Xingsha, Hamuli, Huangxing, and Huanghua in Changsha County. However, with the release of the Changsha City Territorial Spatial Master Plan (2021–2035), Changsha City has set up a series of development high-tech zones centered on advanced manufacturing, and the jurisdiction to which many of the streets belonged during that period has changed. Based on the population and economic data released by each district government in March 2024, this study focuses on the main urban areas of Changsha City, including Furong, Kaifu, Yuhua, Tianxin, and all Yuelu Districts.

4.2. Data Sources and Data Processing

The focus of the study is on the urban parks in the central district of Changsha City. The AOI data of 211 parks in Changsha City were obtained from Baidu maps. The park distribution data in this study were obtained from OpenStreetMap (OSM, https://www.openstreetmap.org) and Baidu Maps (https://map.baidu.com); the data were collected between February and March 2024. Initially, we used the Geofabrik download tool to acquire the shapefiles format of OSM data for China, and the data were subsequently filtered according to the study area boundaries. We then extracted the park element data from the points of interest (abbreviated POI) feature classes within the OSM dataset. Following this, we searched for the corresponding area of interest (abbreviated to AOI) for each park POI in Baidu Maps and extracted the surface and point park feature data for further editing. Ultimately, a comprehensive set of park polygon features was obtained, from which 134 parks within the study area were selected for analysis (Figure 2a). Population and administrative area data were obtained from official government data and Worldpop (https://www.worldpop.org).

This research utilizes population data from Worldpop, which is obtained from credible sources such as national censuses and surveys and then refined using advanced geospatial methods to generate a detailed population map [86]. It was also adjusted with the 7th National Census Data published by the Changsha Municipal Government [87]. While Worldpop data provide spatially distributed population estimates, they are speculative and differ from government-published statistics, which are not spatially explicit. To address this, we employed two methods for adjusting the population data: the first method adjusted the total population based on the ratio of the Worldpop and census data, which did not correct for district-level discrepancies; the second method adjusted the population within each district, based on the 7th Census Data, thus reducing error within districts but resulting in a larger discrepancy in total population estimates. This method offers a more precise representation of population distribution within districts, although it may lead to larger discrepancies in the overall population estimates. While both methods have limitations, the second method significantly enhances district-level accuracy, making it more reliable for localized analyses despite some remaining uncertainty in the overall population distribution. Additionally, this approach allows for better reflection of the population distribution, by aligning the data more closely with census figures (Figure 2).

Five auditors were recruited to learn about the parks through field research, online information collection, and, finally, scoring the parks through a group meeting. All five auditors have a master’s degree in landscape architecture or urban planning and have more than two years of experience living in Changsha, which gives them a good understanding of the parks in Changsha City.

In this study, a 1000-metre fishing net was constructed as the Traffic Analysis Zone with reference to the 15-min living circle planning advocated in China [88,89,90]. As this study focuses on parks within children’s walking distance, the walking distance of children in different situations, 750 m, was selected as the search radius for this study [91]. Then, the data were incorporated into the supply–demand improved 2SFCA method, and a K-nearest neighbor analysis (KNN) was conducted, followed by an analysis of the results (Figure 3).

5. Results

5.1. Evaluation of Park Accessibility

As illustrated in Figure 4, the blue part with the lowest accessibility is mainly located in the western part of Furong District, the northwestern part of Yuhua District, and the southern part of Tianxin District. This area is the location of Changsha’s central business district, Wuyi Square (a commercial and leisure gathering place in Changsha and a famous tourist attraction in Changsha). The red part has the best accessibility, mainly in Yuelu District.

5.2. K-Nearest Neighbor Analysis

The KNN algorithm is an easily understandable and implementable non-parametric classification and regression method, which is widely applied for its intuitive nature, lack of a training phase, flexibility, and suitability for solving problems across various domains [92]. Using the KNN algorithm can efficiently process data and flexibly assess green space accessibility across different areas, providing practical information for optimizing urban green space layout.

Figure 5a illustrates the large size of the sky-blue Low-Low (LL) clusters, in GIS KNN analysis, L-L (“Low-Low”) and H-H (“High-High”) are types of spatial clustering patterns described in spatial autocorrelation analysis. L-L indicates a clustering of low values, while H-H indicates a clustering of high values within the data. From Figure 5a we can see that very few parks are accessible by foot within the area. Additionally, the area has low park accessibility, and its surrounding neighborhoods lack sufficient parks or green space resources. The High-High (H-H) Clusters are rich in green space resources, have high accessibility, and perform better overall. The H-H areas are mainly located within Yuelu District and are farther away from the core business districts.

Figure 5b illustrates that the L-L and neighboring areas exhibit low population densities, and the H-H and neighboring areas exhibit high population densities.

In Figure 6, blue areas are densely populated and have poor park accessibility. In other parts of Yuelu District, the HH-LL area and its neighboring areas have high green space accessibility, indicating that residents can easily access green spaces or parks and have low population densities.

When viewed in conjunction with the urban green space system planning structure map of the Changsha Urban Green Space System Plan (2021–2035) [79], the green spaces in Changsha City’s business districts are mainly in the form of parks and green spaces within park clusters. This means that with a certain core park or green space as the center, a park cluster of a certain scale and characteristics is formed by connecting the surrounding parks and green spaces through greenways and landscaped roads. Green spaces in urban commercial centers will remain the focus of future construction, focusing on enhancing the public’s sense of access to green space and happiness.

In conclusion, the number of green spaces in Changsha city center is currently low, while the population density in the area is high. This indicates that the relationship between the amount of green space and population density in the city center is problematic. On the one hand, for economic development, land is divided into economic construction and residential land; on the other hand, for the physical and mental health of urban residents, green space needs to be divided within a specific range. Therefore, finding a balance between economic development and residents’ health is crucial for the future of urban green space system planning in Changsha.

6. Discussions

6.1. Research Novelty

This study offers a novel approach to evaluating urban park accessibility by integrating both spatial differences and park quality considerations, with a particular focus on outdoor play spaces for children. The research introduces an enhanced version of the Two-Step Floating Catchment Area (2SFCA) method. This improved method incorporates quality indices specific to children’s needs, providing a more precise measure of park attractiveness by combining park capacity and quality.

By employing the Huff model, the study accounts for the likelihood of park selection based on factors such as park size, quality, and travel costs, offering a more accurate assessment of demand compared to traditional methods. Additionally, the study develops a new scale for assessing park spatial quality, focusing on variables that cater to youth, such as play diversity and natural environments. This scale, informed by the QUINPY and other relevant tools, ensures a comprehensive evaluation of park quality.

To enhance data accuracy, the study integrates multiple data sources, including official government data, open-source maps, and Worldpop population data, with refined geospatial techniques. Local auditors with relevant expertise and experience conducted field surveys and online data collection, further validating the results.

In essence, this study advances methodological approaches and addresses the specific needs of youth in urban park accessibility, offering valuable insights for improving park quality and accessibility, and providing a fresh perspective for equitable research on public facilities.

6.2. Research Implications

6.2.1. Relationship between Economic Development and Green Space

The problem of fewer green spaces in urban centers may be closely related to economic development. The concentration of high economic activity has led to an increased demand for commercial and residential building land, and this demand often results in the conversion of green space to building land, thus reducing the amount of green space. In addition, green space may also be limited due to the high density and compactness of city center areas [93]. This finding aligns with this study’s results, demonstrating that there is less green space in parks in urban central areas. Huang’s (2022) study indicated that urban green space in China decreased by 57.23% between 1990 and 2015. His study also revealed a significant decrease in urban green space accessibility (UGSA) and a significant negative correlation between urban space expansion and UGSA [94]. This aligns with the findings of this study, further supporting the conclusion that green spaces are scarce in urban central areas.

6.2.2. Multidimensional Assessment of Green Space

The issue of urban green space accessibility involves the spatial proximity of parks and should also be measured in terms of multiple dimensions, such as park area and park quality [5]. Factors influencing park performance and demand differ between areas with high and low population densities. Simply increasing the number of parks or expanding the size of parks within underserved neighborhoods while improving park accessibility to some extent is not the only or best way to solve the problem. Instead, more attention needs to be paid to improving the quality of parks and providing diverse facilities. For example, designing diversified play facilities for young people, ensuring routine maintenance and safety management of parks, and considering the park’s land use pattern are all important factors in enhancing the quality of park services [43]. Through this integrated perspective of planning, the needs of different communities can be met more effectively, and a more equitable distribution of public green space can be achieved.

6.2.3. Differences in Park Demand between High-Density and Low-Density Areas

Analyzing spatial accessibility factors helps urban planners and decision-makers distribute park space fairly and reasonably. Based on the results of this study and the urban green space system planning of Changsha, we believe that existing park green spaces will, to some extent, influence future urban policies and planning decisions. For example, in central areas such as Furong District and Kaifu District, future planning can improve public green space construction through linear green spaces and clustered small parks. Urban policies and planning significantly impact park accessibility [72,95,96]; while park accessibility, in turn, can also influence urban policies to some extent.

6.2.4. Enhancing Park Design for Chlidren Engagement and Activity

Urban park designers should enhance the diversity of play facilities for children, as a rich variety of amenities is significantly correlated with higher park usage frequency and increased physical activity levels among young people [97,98]. The inclusion of diverse play structures, interactive elements, and varied activity zones can cater to a broader range of interests and developmental needs, making parks more engaging and appealing. This diversity not only encourages more frequent visits but also promotes a more active lifestyle among children, by providing stimulating environments that cater to different forms of physical exercise and social interaction. Moreover, incorporating inclusive play features that accommodate various abilities can foster a sense of community and support equitable access to recreational opportunities for all children. By focusing on these aspects, urban park designs can play a pivotal role in enhancing the well-being and social development of young people.

Given the constraints of urban land and the limited space available for outdoor parks, it is advisable to adopt a strategy of “functional mixing and open sharing.” By redesigning and redeveloping areas with limited functionality to integrate multiple types of activities into the same space, it is possible to effectively improve the efficiency and inclusivity of the space. This approach aims to maximize the use of existing activity spaces, achieving “multiple uses in one space” and thereby enhancing the flexibility and efficiency of urban space.

6.3. Research Limitations

While our research method improves the assessment of park accessibility, it still has some limitations. Firstly, the calculation of the quality index, which focused on play diversity and certain environmental characteristics, is not exhaustive. For instance, important indicators such as green space coverage were not included. Additionally, although we emphasized the number of park facilities, we overlooked the maintenance level of these facilities, which can significantly influence park visitation decisions. That said, our evaluation did include more items related to the attractiveness of facilities, enhancing that aspect. Future studies could further refine this by developing a more comprehensive park quality scoring system.

Secondly, due to data collection limitations, we only included green spaces explicitly labeled as “parks” on maps. This excluded many green spaces, such as linear greenways, which were not named as parks. At the same time, adolescents’ perceptions and behaviors, which have a significant impact on park visits [99], were not fully addressed and should be explored in future research through factor analysis [100]. An additional area for further investigation is the influence of family dynamics, particularly parental willingness, on children’s park visitation. This factor plays a critical role in shaping children’s engagement with green spaces and should be examined in more detail in future studies.

The methodological approach presented in this study provides valuable insights for assessing urban park accessibility, but its application may require adaptation to local contexts. Variability in the quality and completeness of databases across different cities can impact the effectiveness of this method. Comprehensive databases should encompass population distribution, park locations, and basic park information. To enhance the applicability of this method in various urban areas, it is crucial to involve local stakeholders—including planners, designers, educators, parents, and children—in the evaluation process. This collaborative approach would facilitate the development of context-specific evaluation criteria and provide a more accurate reflection of park attractiveness and functionality in different urban settings.

7. Conclusions

This study assessed how effectively children use park spaces by applying the enhanced supply–demand 2SFCA method, with the goal of addressing the growing gap between child and nature, as well as urban health disparities. The results of the study demonstrate that park accessibility in Changsha City exhibits significant differences across different areas, with the western part of Furong District, the northwestern part of Yuhua District, and the southern part of Tianxin District having the lowest accessibility, while Yuelu District has the highest accessibility. The limited green space in the central business district of Changsha City and the high population density indicate a tension between green space planning and population density. Future urban and green space planning should consider the needs of high-density populations, mainly focusing on supporting children’s outdoor activities by thoughtfully allocating green space resources within limited areas.