Aligning Supply and Demand: The Evolution of Community Public Sports Facilities in Shanghai, China

Abstract

Community public sport facilities are core carriers of the national fitness public service system, with their supply–demand alignment directly linked to megacity governance efficiency and residents’ well-being. To address structural issues, such as “human–land imbalance” in facility layout, this study uses the 2010–2024 panel data from Shanghai’s 16 districts, applies supply–demand equilibrium theory, and integrates quantitative methods to analyze spatio-temporal supply–demand coupling and identify key influencing factors. The study yields four key findings: (1) The spatial distribution of facilities and population demonstrates a differentiated evolutionary trajectory marked by “central dispersion and suburban stability”. (2) Supply–demand alignment has continuously improved, as evidenced by the increase in coordinated administrative districts from six to thirteen. Nonetheless, the distance between sports facilities and population centers widened, suggesting that spatial adaptation remains incomplete. (3) Urban population growth exerts a significant positive impact on facility supply. Elasticity coefficients are generally high in suburban areas, while negative elasticity is detected in some central urban areas due to population outflow. (4) Facility construction intensity and residential activity intensity are core driving factors, with economic conditions, transportation infrastructure, and housing prices acting as key supporting factors. This study overcomes traditional aggregate-quantity research limitations, reveals megacity facility supply–demand “spatial mismatch” dynamics, and provides a scientific basis for targeted public sports facility layout and refined governance.

1. Introduction

Against the backdrop of rapid global urbanization, promoting residents’ physical activity has emerged as a pivotal public health strategy to address the prevalence of chronic diseases and enhance urban livability [1,2]. The United Nations Sustainable Development Goals (SDGs), particularly Goal 3 (Good Health and Well-being) and Goal 11 (Sustainable Cities and Communities), explicitly emphasize the significance of equitable access to green public spaces and sports facilities [3]. However, with the continuous expansion of megacities and drastic changes in population structure, the supply of public service facilities often fails to keep pace with population migration, leading to a severe “spatial mismatch” phenomenon [4]. This imbalance between supply and demand not only reduces the efficiency of urban governance but also exacerbates spatial social segregation and health inequalities [5]. Therefore, how to quantitatively evaluate the spatio-temporal coupling relationship between public sports facilities and population distribution in high-density urban environments with extremely scarce land resources, and identify the underlying driving mechanisms, has become a common urgent problem to be solved in the global academic circles of urban geography and planning.

Existing studies have extensively explored the accessibility and equity evaluation of public service facilities, mainly based on location-allocation models or the two-step floating catchment area method [6,7]. However, most studies still have two significant limitations: (1) Lack of a dynamic perspective with long-term time series. The existing literature mostly focuses on static evaluations at a certain time point, ignoring that cities are complex dynamic systems. In the process of rapid suburbanization or gentrification, the movement trajectories of the population center and the facility center are often inconsistent, and this “lag effect” can only be accurately captured through longitudinal panel data [8]. (2) Insufficient non-linear analysis of driving mechanisms. Traditional linear regression models struggle to reveal how complex urban elements interact to affect the layout of facilities. Especially in compact cities, high land costs and rigid planning systems are often intertwined, jointly distorting the supply–demand balance mechanism of the market [9].

This study adopts the supply–demand equilibrium theory and the spatial mismatch hypothesis as the theoretical framework [10]. We believe that, under ideal conditions, the spatial distribution of public sports facilities should be highly coupled with population density. However, due to the path dependence of financial investment and the lag of land development, there is often a structural contradiction of “population suburbanization and facility centralization” in reality [11]. To verify this hypothesis, this study selects Shanghai as an empirical case. Shanghai is committed to building a globally outstanding and modern international metropolis, and “Healthy Shanghai” is an important carrier to achieve its urban goals and promote Shanghai’s “four major brands”, including “Shanghai Services”, “Shanghai Manufacturing”, “Shanghai Shopping”, and “Shanghai Culture” [12]. In 2022, the total scale of Shanghai’s sports industry reached CNY 186.258 billion, among which the construction of sports venues and facilities management generated CNY 2.7 billion and CNY 1.523 billion, respectively. In addition, Shanghai has issued “several opinions on accelerating the innovative development of the city’s sports industry”, which clearly proposes to strengthen the supply of community sports facilities, build several sports activity centers, sports venues, and fitness trails, and create a “15-min sports life circle” [13].

As a global city with a population of 25 million, Shanghai represents a typical “high-density compact development model” [14]. Unlike sprawling cities, such as Los Angeles, Shanghai faces the most acute contradiction between supply and demand of public resources under extremely high population pressure and land constraints. Analyzing the evolutionary path of Shanghai during the transition period from 2010 to 2024 can provide a universally applicable reference sample for megacities in the Global South, such as Mumbai and São Paulo, which are experiencing similar rapid urbanization [15].

Based on the above background, this study aims to answer the following core questions through multi-dimensional spatio-temporal analysis:

(1)

Spatio-temporal evolution characteristics. During the 15-year urban expansion cycle, has the supply center of Shanghai’s sports facilities effectively responded to the changes in the population center? Is there a significant spatio-temporal lag?

(2)

Measurement of supply–demand matching. How has the degree of supply–demand matching in different administrative districts evolved? Are there persistent “supply depressions”?

(3)

Identification of driving mechanisms. Which key factors dominate the spatial allocation of facilities? Is there an enhanced interaction effect among these factors?

Considering that Shanghai, as a megacity, has experienced rapid urbanization and frequent adjustments of grassroots administrative divisions in the past 15 years, it is difficult to maintain consistency in the statistical caliber of micro-scale data. Therefore, this study takes 16 municipal districts as the basic spatial units. Although this scale smooths out intra-district differences to a certain extent, it ensures the coherence and comparability of panel data from 2010 to 2024, helps to reveal the “core-periphery” evolution law of public sports resource allocation from a macro perspective, and matches the fiscal powers of district-level governments, providing a decision-making basis for top-level planning. This study integrates multi-source panel data from 16 administrative districts in Shanghai from 2010 to 2024 and constructs an integrated analysis framework that includes the inconsistency index, gravity model, geodetector, and other components. The contribution of this study lies in breaking through the limitations of static analysis, revealing the deep logic of public resource allocation in megacities from a dynamic evolution perspective, and providing a scientific basis for achieving more inclusive urban spatial governance.

The following sections are expanded as follows: Section 2 introduces the research materials and methods. Section 3 analyzes the research results. Section 4 presents the discussion, and Section 5 concludes with the findings and future work.

2. Materials and Methods

2.1. Study Area

Shanghai, situated on the southern bank of the Yangtze River Estuary in the eastern coastal region of China, acts as a strategic hub for both the Belt and Road Initiative and the Yangtze River Economic Belt strategy (Figure 1). As one of China’s premier economic centers, the city comprises 16 districts spanning an area of approximately 6341 km² and has an urbanization rate of 89.3%. As of late 2023, Shanghai had a population of approximately 24.7 million and was home to 59,702 sport facilities with a total area of 64.72 million m².

The selection of Shanghai as the research subject in this study is based on considerations from the following three dimensions:

(1)

Representativeness. Shanghai is currently in a critical period of transformation from “incremental expansion” to “stock renewal”. The characteristics of supply–demand mismatch during this stage have early-warning significance for other emerging megacities that are about to enter the mature stage.

(2)

Data Availability. As a pioneer city in digital governance in China, Shanghai has relatively complete historical statistical data and geographic information records, enabling a longitudinal panel analysis spanning 15 years (2010–2024), which is relatively rare in urban studies in developing countries.

(3)

Spatial Heterogeneity. Shanghai encompasses a complete gradient from high-density central urban areas to rapidly urbanizing suburbs and then to rural areas. Such rich internal differences allow us to simulate the supply–demand relationships at different development stages within a single city.

2.2. Theoretical Framework and Research Hypotheses

2.2.1. Mechanism of Supply–Demand Equilibrium and Spatial Mismatch

This study is grounded in the supply–demand equilibrium theory of public goods. In an ideal state of equilibrium, the spatial distribution of public sports facilities should be positively correlated and coupled with the density of permanent residents, achieving Pareto optimality [16]. However, in the expansion model of megacities characterized by land finance, this equilibrium is highly vulnerable to disruption [17]. According to the spatial mismatch hypothesis, two asynchronous forces exist during urban restructuring, and this speed differential leads to a deviation between the centers of supply and demand.

(1)

Population decentralization. Driven by the high cost of living in central urban areas, the population migrates rapidly to the suburbs [18].

(2)

Fixity of facilities. As heavy-asset investments, sport facilities involve long cycles from planning and site selection to completion and are constrained by path dependency in central urban areas [19].

2.2.2. Research Hypotheses

Based on the above theoretical derivation, this study proposes two core hypotheses to guide subsequent empirical analysis:

H1 

(Spatio-temporal Lag Hypothesis). During the rapid urbanization cycle, the suburbanization speed of the population center is significantly faster than that of the sports facility center, resulting in an annual expansion of the mismatch distance, where “people move, but facilities remain”.

H2 

(Economic Driving Hypothesis). The supply of facilities is not solely determined by population demand but is significantly influenced by economic rent. Under market mechanisms, regions with high economic development levels (GDP) and high housing prices are more likely to attract the agglomeration of facilities, which may exacerbate spatial inequity.

Correspondingly, this study will focus on answering three key questions:

(1)

What is the spatio-temporal evolution trajectory of supply and demand?

(2)

How can the degree of this mismatch be quantified?

(3)

Which socio-economic factors drive this mismatch?

2.3. Research Data

The panel data used in this study covers Shanghai’s 16 administrative districts from 2010 to 2024. Temporally, it spans the critical implementation phase of China’s National Fitness Strategy, featuring strong data continuity that enables the capture of long-term evolutionary trends. Spatially, it includes Shanghai’s core urban areas, semi-core areas, and suburbs, covering regions at different development stages to ensure sample representativeness.

The supply of community public sports facilities is primarily led by government departments, providing public welfare sports venues and facilities to urban residents. In this paper, it is characterized by the quantity of community public sports facilities in Shanghai. The demand for community public sports facilities refers to residents’ needs for public sports facilities and venues in activities such as physical exercise, leisure, and entertainment. Due to data availability, it is characterized by the year-end permanent population of each district in Shanghai. In addition, the data in this paper also involves the land area of each district in Shanghai and the location of each district government. See

Table 1. Study data types and sources.

Data Type	Data Source	URL
Community Public Sports Facilities	Official Website of Shanghai Sports Bureau	http://www.shggty.com.cn/facilityMap.html (accessed on 16 November 2025)
Permanent Residents	Shanghai Statistical Yearbook	https://tjj.sh.gov.cn/tjnj/index.html (accessed on 16 November 2025)
Land Area of Shanghai Districts	Shanghai Statistical Communique on National Economic and Social Development	https://tjj.sh.gov.cn/tjgb/index.html (accessed on 16 November 2025)
Location of Shanghai District Government Offices
Shanghai Basic Geographic Data	Amap	https://lbs.amap.com/tools/picker (accessed on 16 November 2025)

for details of main data types and their sources.

This study takes Shanghai’s 16 administrative districts as the research objects. The choice of district-level rather than sub-district/town-level as the spatial analysis unit is mainly based on the following two considerations:

(1)

Longitudinal stability of data. From 2010 to 2024, Shanghai witnessed frequent adjustments to grassroots administrative divisions (such as the merger of Zhabei District and Jing’an District, and the transformation of multiple towns into sub-districts), resulting in severe spatial mismatch and fragmentation problems in street-level statistical data. In contrast, the boundaries of the 16 municipal districts are relatively stable, making them the only feasible scale for long-term panel analysis.

(2)

Subjectivity of policy implementation. In China’s urban governance system, district-level governments are the core responsible entities for implementing planning and guaranteeing the financing of public sport facilities. Analyzing the supply–demand match at the district level allows for a more direct evaluation of the public service supply performance of each district government.

Although this study focuses on “community-level” sports facilities, we emphasize the analysis of the cumulative effects and distribution equity of these facilities at the “regional level”. In addition, the research data were cleaned and preprocessed, including outlier handling, data standardization, and consistency checks. Most importantly, in response to Shanghai’s administrative division adjustments between 2010 and 2024 (including the merger of Luwan District into Huangpu District in 2011 and the merger of Zhabei District into Jing’an District in 2015), to ensure the spatial consistency of the longitudinal panel data, this study uniformly uses the 2025 administrative divisions as the benchmark. Statistical data for all historical years were re-consolidated and recalibrated according to the latest administrative boundaries, eliminating statistical errors caused by division changes and ensuring the comparability of time-series analysis. The sources of various types of study data are shown in Table 1.

2.4. Supply–Demand Spatial Matching Measurement Method

2.4.1. Geographic Concentration Index

The geographic concentration index (GCI) is a metric used to assess the degree of concentration or dispersion of an attribute value across spatial regions [20]. This study employs this index to calculate the GCIs of urban community public sports facilities and permanent residents. The formulas are as follows:

$${\mathit{RFAC}}_{\mathit{it}}={\displaystyle \frac{\mathit{FA}{\mathit{C}}_{\mathit{it}}/\sum \mathit{FA}{\mathit{C}}_{\mathit{t}}}{\mathit{TE}{\mathit{R}}_{\mathit{it}}/\sum \mathit{TE}{\mathit{R}}_{\mathit{t}}}}$$

(1)

$${\mathit{RPOP}}_{\mathit{it}}={\displaystyle \frac{\mathit{PO}{\mathit{P}}_{\mathit{it}}/\sum \mathit{PO}{\mathit{P}}_{\mathit{t}}}{\mathit{TE}{\mathit{R}}_{\mathit{it}}/\sum \mathit{TE}{\mathit{R}}_{\mathit{t}}}}$$

(2)

where RFAC_it represents the geographic concentration index of community public sports facilities in district i at time t. RPOP_it represents the geographic concentration index of permanent residents in district i at time t. FAC_it represents the number of community public sports facilities in district i at time t. ∑FAC_t represents the total number of community public sports facilities in Shanghai at time t. POP_it represents the number of permanent residents in district i at time t. ∑POP_t represents the total number of permanent residents in Shanghai at time t. TER_it represents the land area of district i at time t. ∑TER_t represents the total land area of Shanghai at time t.

2.4.2. Inconsistency Index

To reflect the imbalance more intuitively between the supply of community public sports facilities and the needs of permanent residents, this paper constructs an inconsistency index (I) to measure the spatial distribution mismatch between urban community public sports facilities and population demand [21]. The formula is as follows:

$$\mathit{I}={\displaystyle \frac{{\mathit{RFAC}}_{\mathit{it}}}{{\mathit{RPOP}}_{\mathit{it}}}}$$

(3)

where I represents Inconsistency Index, and RFAC_it represents the geographic concentration index of community public sports facilities in district i at time t. RPOP_it represents the geographic concentration index of population demand in district i at time t. Based on the value of I, the supply–demand status is categorized into three types: (1) supply-lag type, I ≤ 0.75 (facility supply fails to meet population demand); (2) supply–demand coordination type, 0.75 < I ≤ 1.25 (supply and demand are relatively balanced); and (3) supply-advance type, I > 1.25 (facility supply exceeds population demand).

2.4.3. Facility–Population Growth Elasticity

Facility–population growth elasticity refers to the ratio of the growth rate of sports facilities to the growth rate of the permanent population in a region over the same period [22]. It reflects the magnitude of the impact of changes in regional permanent population growth on changes in the scale of sports facilities from an overall perspective. The formula is as follows:

$${\mathit{E}}_{\mathit{i}}={\displaystyle \frac{\Delta \mathit{FAC}/\mathit{FAC}}{\Delta \mathit{POP}/\mathit{POP}}}$$

(4)

where E_i represents facility–population growth elasticity for district i. A larger E_i value indicates a stronger driving effect of permanent population growth on the growth of sports facilities. ΔFAC represents the change in the number of sports facilities in district i over a given period, and ΔFAC/FAC represents the growth rate of sports facilities in district i. ΔPOP represents the change in permanent population in district i over the same period, and ΔPOP/POP represents the permanent population growth rate in district i.

2.4.4. Spatial Gravity Center Method

The spatial gravity center method is applied to graphically visualize the spatial gravity centers of community public sports facilities and permanent residents, aiming to understand the spatial evolution characteristics of supply and demand for community public sports facilities [23]. The formulas are as follows:

$$\bar{\mathit{X}}={\displaystyle \frac{\sum _{i=1}^n{P}_i{X}_i}{\sum _{i=1}^n{P}_i}}$$

(5)

$$\bar{Y}={\displaystyle \frac{\sum _{i=1}^n{P}_i{Y}_i}{\sum _{i=1}^n{P}_i}}$$

(6)

where n denotes the number of districts in Shanghai; X_i and Y_i represent the longitude and latitude of the government seat of district i; P_i is the attribute value of the research object. When P_i respectively represents the land area, number of sports facilities, or permanent population of district i, then (X, Y) denotes Shanghai’s geometric center of land, spatial gravity center of facilities, and population distribution center, respectively.

2.5. Impact Factor Evaluation Method

2.5.1. Indicator System

Based on the supply–demand equilibrium theory and the spatial allocation logic of urban public services, this study selects influencing factors from three dimensions: natural base, social economy, and built environment.

In terms of natural base, topographic conditions restrict the feasibility of facility siting. Represented by the average altitude of each district and county in Shanghai, terrain relief directly affects the construction cost and layout feasibility of sports facilities. Plain areas are more conducive to dense facility layout and have higher adaptability to population demand.

In terms of social economy, the level of economic development determines the capacity for financial investment, and housing prices reflect the capitalization effect of public services. (1) Economic development level. Represented by GDP per unit of land, economic strength determines the financial investment capacity for facility construction, which is the core material guarantee for facility supply. It is expected to be positively correlated with the degree of supply–demand matching. (2) Housing prices. Represented by the average housing price in each district and county, housing prices are positively correlated with population attractiveness and community quality. Residents in high housing price areas have higher demands for the quality and quantity of sports facilities, forming a virtuous cycle of “facility supply—housing value appreciation”.

In terms of the built environment, traffic conditions and construction intensity directly affect the accessibility and available space for facilities, while residents’ activity intensity can capture the real demand density through POI data. (1) Traffic conditions. Represented by the average road network density, traffic convenience affects residents’ accessibility to facilities and indirectly acts on the efficiency of supply–demand matching. Areas with dense road networks have higher facility utilization efficiency. (2) Construction intensity. Represented by the proportion of construction land, it reflects the degree of urban development. Construction intensity is highly correlated with population agglomeration, directly driving the demand for facility supply, and is a core driving factor for supply–demand matching. (3) Residents’ activity intensity. Represented by POI density, POI data cover activity places, such as commerce and culture, directly reflecting the activity level of the population, and are a direct proxy variable for facility demand.

In addition, location characteristics can reflect the centrality of the regional city. Core areas have high population agglomeration and greater pressure on supply–demand matching of facilities, which is in line with the “core-periphery” theoretical framework. In this study, the spatial coordinates of each district government and the spatial coordinates of the central business district of each district in the location characteristics are obtained from the coordinate picking and positioning system open to the public by Amap.

Therefore, combining theoretical logic and practical scenarios, an influencing factor indicator system is constructed, including topographic conditions (X1), location characteristics (X2), economic development level (X3), traffic conditions (X4), construction intensity (X5), resident activity intensity (X6), and housing prices (X7), as shown in

Table 2. Selection of influencing factor indicators.

Variable Type	Variable Name	Variable Meaning
Dependent Variable	Spatial Matching Degree Coefficient (Y)	Matching degree coefficient between the community public sports facility system and the population system
Independent Variables	Topographic Conditions (X1)	Average elevation data of each district
	Location Characteristics (X2)	Euclidean distance between the spatial position of each district government and the spatial position of each district’s Central Business District
	Economic Development Level (X3)	Gross Domestic Product (GDP) of each district divided by the total construction land area of each district
	Traffic Conditions (X4)	Average road network density of the traffic network in each district
	Construction Intensity (X5)	Construction land area of each district divided by the administrative division area of each district
	Resident Activity Intensity (X6)	Number of Points of Interest (POI) in each district divided by the administrative division area of each district
	Housing Price (X7)	Average housing price of each district

.

2.5.2. Geodetector

Geodetector is a method used to detect spatial stratified heterogeneity (SSH) and reveal its underlying driving forces. It is a new statistical method currently employed to explore whether there is a causal relationship between pairwise variables, including four types: ecological detection, interaction detection, factor detection, and risk detection [24]. This study adopts factor detection and interaction detection to investigate the interaction between two individual factors. Its core idea is that if an independent variable X is the cause of changes in the dependent variable Y, and if the spatial distribution of X is similar to that of Y. This method measures this explanatory power by calculating the q-statistic:

$$q=(N{\sigma }^2-\sum _{h=1}^L{N}_h{\sigma }_h^2)/N{\sigma }^2$$

(7)

where N and σ² are the sample size and variance of sport facilities, respectively. N_h and ${\sigma }_h^2$ are the sample size and variance of the h-th type of influencing factor, and L is the number of categories of the h-th type of influencing factor. The value range of q is [0, 1], and a larger value indicates a stronger explanatory power of the factor index on the spatial distribution.

2.5.3. Two-Way Fixed Effects Panel Regression Model

To overcome the limitations of Geodetector, which can only handle cross-sectional data and cannot effectively control confounding variables, and to fully utilize the characteristics of the long-term panel data from 2010 to 2024, this study further constructs a two-way fixed effects model. This model can simultaneously control for unobservable factors that vary across individuals but not over time, and macro shocks that vary over time but not across individuals, thereby more accurately identifying the net effect of each driving factor on the supply–demand mismatch index [25]. The model is specified as follows:

$$Y_{it}={\beta }_0+\sum _{k=1}^n{\beta }_k{X}_{kit}+{\mu }_i+{\lambda }_t+{ϵ}_{it}$$

(8)

where i represents Shanghai’s 16 administrative districts, and t represents the year. Y_it is the dependent variable, which adopts the supply–demand inconsistency index calculated earlier. A larger value of this index indicates a more severe supply–demand mismatch; X_kit are core explanatory variables and control variables, covering seven indicators from three dimensions of natural environment, social economy, and urban construction. β_k is the regression coefficient of each variable, reflecting the marginal impact of the explanatory variable on the supply–demand mismatch while keeping other variables constant. μ_i is the individual fixed effect, λ_t is the time fixed effect, and ϵ_it is the random disturbance term.

Since this study uses a fixed effects model, variables that do not change over time (time-invariant variables), namely topographic conditions (X1) and location characteristics (X2), will be absorbed by the individual fixed effect (μ_i), so their coefficients cannot be estimated separately in the fixed effects (FE) model. This is in line with the basic principles of econometrics, as the impact of these geographical background factors on supply–demand mismatch is long-term and stable and was controlled in the intercept term differences of the model.

3. Result Analysis

3.1. Spatial Matching Characteristics Analysis

3.1.1. Geographic Concentration Index Analysis

From 2010 to 2024, the GCIs of both the supply of community public sports facilities and the demand of permanent residents in Shanghai exhibited a distinct trend. In central urban districts, GCIs were higher and tended to disperse over time. In outer suburban districts, GCIs were lower and remained relatively stable. Huangpu, Hongkou, Putuo, Jing’an, Changning, Yangpu, and Xuhui are central urban districts of Shanghai, characterized by mature urban infrastructure, developed commercial services, and dense residential communities. Historically, the GCIs of sport facilities and permanent residents in these districts were generally higher than those in outer suburban districts. However, in recent years, driven by factors including the continuous optimization of sports facility layout within the districts, urban function transformation and upgrading, and population outflow from central areas, the GCIs of sports facility supply and population demand in these districts have decreased significantly.

Notably, the GCI of sports facilities in Changning District dropped from 16.16 in 2010 to 5.05 in 2024, while the GCI of permanent residents in Huangpu District decreased from 23.84 in 2010 to 8.39 in 2024, both recording the largest declines across all districts. Meanwhile, the GCIs of sports facilities and permanent residents in suburban districts (Jiading, Baoshan, Songjiang, Qingpu, Fengxian, and Chongming) stayed below 1 between 2010 and 2024, with a variation of 0.5. This indicates that both permanent residents and community public sport facilities in these suburban districts are relatively dispersed in spatial distribution.

The natural breaks method was employed to classify the GCIs of community public sport facilities and permanent residents into hierarchical categories in the study’s base year (2010) and end year (2024), aiming to understand the spatial agglomeration characteristics of Shanghai’s urban community public sports facilities and permanent residents. Based on GCI magnitudes, five categories were defined: high agglomeration, moderately high agglomeration, moderate agglomeration, moderately dispersed, and dispersed.

As shown in Figure 2, from 2010 to 2024, the number of districts with high agglomeration and moderately high agglomeration of urban community public sport facilities in Shanghai decreased. Specifically, Hongkou and Changning districts dropped from high agglomeration in 2010 to moderate agglomeration in 2024. Putuo and Yangpu districts fell from a moderately high agglomeration in 2010 to moderately dispersed in 2024. Conversely, the number of districts with moderate agglomeration and moderately dispersed sport facilities increased, from one and two districts in 2010 to four and five districts in 2024, respectively.

Meanwhile, the spatial agglomeration types of Shanghai’s permanent residents are illustrated in Figure 3. In 2010, Hongkou and Huangpu districts were high agglomeration areas for permanent residents. In 2024, they shifted to moderate agglomeration areas. The outward dispersion of population from these central urban districts reduced the demand pressure on community public sports facilities within the districts to a certain extent. Additionally, from 2010 to 2024, the number of dispersed areas for permanent residents decreased from nine to six districts. In addition, the number of moderately dispersed areas increased from zero to four districts. Notably, Baoshan, Minhang, and Pudong New Area experienced a significant influx of migrant populations, leading to higher agglomeration of permanent residents. Changning District, due to population outflow, shifted from a moderate agglomeration to a moderately dispersed one.

3.1.2. Inconsistency Analysis

The number of districts with supply-advanced and supply-lagging community public sport facilities in Shanghai is decreasing, while the number of coordinated districts is gradually expanding (

Table 3. Growth rates of community sports facilities, permanent residents, and elasticity coefficients in Shanghai’s districts.

District Type	District Name	Annual Average Growth Rate of Community Sports Facilities (%)	Annual Average Growth Rate of Permanent Residents (%)	Sports Facility–Population Growth Elasticity
Central Urban Districts	Huangpu District	0.400	−0.020	−20.182
	Xuhui District	0.202	0.014	14.335
	Changning District	0.091	0.008	11.121
	Jing’an District	0.208	0.000	−39.98
	Putuo District	0.097	0.029	3.319
	Hongkou District	0.147	0.000	−46.26
	Yangpu District	0.162	0.012	13.431
Semi-Central Urban District	Pudong New Area	0.489	0.076	6.470
Suburban Districts	Minhang District	0.648	0.160	4.060
	Baoshan District	0.750	0.084	8.948
	Jiading District	0.653	0.125	5.216
	Jinshan District	0.769	0.028	27.040
	Songjiang District	0.941	0.141	6.681
	Qingpu District	1.669	0.090	18.469
	Fengxian District	7.986	0.072	111.13
	Chongming District	8.236	0.003	236.33
Average	Shanghai	0.413	0.046	8.954

). In 2010, the supply-lagging type included seven districts: Chongming, Baoshan, Qingpu, Songjiang, Jinshan, Fengxian, and Huangpu. By 2015, Huangpu and Jinshan districts shifted to the coordinated type, reducing the number of supply-lagging districts to five. By 2020 and 2024, this number further decreased to two and one, respectively. Jinshan, Songjiang, Qingpu, and Fengxian districts transitioned from supply-lagging to a coordinated type, indicating a continuous increase in the number of community public sports facilities and ongoing optimization of their layout. Huangpu and Chongming districts shifted to the supply-advanced type in terms of the relationship between sports facilities and population demand. Driven either by the urgent needs of community residents or by forward-looking urban planning, these two districts invested heavily in sports facility construction, becoming the only two supply-advanced districts in Shanghai.

In 2010, there were six coordinated districts for community public sport facilities in Shanghai: Jiading, Jing’an, Yangpu, Hongkou, Xuhui, and Pudong New Area. Except for Jiading District, all others were central or semi-central urban districts with a solid foundation in sports venue and facility construction, which could basically meet the needs of residents. The number of coordinated districts increased to eight in 2015 and twelve in 2020, reaching thirteen by 2024. These districts are distributed across central, semi-central, and suburban areas, indicating that the layout of sport facilities in Shanghai’s districts has become increasingly optimized in recent years, with a continuous improvement in the matching degree with the demand of local permanent residents. Meanwhile, although the Inconsistency Index of Baoshan District increased from 2010 to 2024, it remained in the supply-lagging type. In the future, this issue should be addressed by strengthening the construction of community public sports venues, diversifying the spatial types of sports facilities, and optimizing the supply–demand structure of sport facilities (Figure 4).

3.1.3. Growth Elasticity Analysis

In Shanghai, thirteen districts exhibit a positive sports facility–population growth elasticity, suggesting that the growth of permanent residents in these districts has, to some extent, driven an increase in the number of community public sport facilities (Table 3). Among them, the sports facility–population growth elasticity of Chongming District and Fengxian District exceeds 100, meaning the annual average growth rate of sports facilities in these two districts is far higher than that of their permanent resident population. In addition, both the annual average growth rate of permanent residents and the sports facility–population growth elasticity of Huangpu, Jing’an, and Hongkou districts are negative. As central urban districts of Shanghai, these three districts have annual average growth rates of sports facilities of 0.40%, 0.21%, and 0.15%, respectively. However, with industrial structure transformation and urban functional layout renewal, the permanent resident population in these districts has experienced an outflow in recent years, resulting in a negative sports facility–population growth elasticity.

From 2010 to 2024, the annual average growth rate of Shanghai’s urban community public sports facilities was 0.413%, the annual average growth rate of permanent residents was 0.046%, and the sports facility–population growth elasticity coefficient was 8.95. According to the index interpretation of the growth elasticity formula, for every 1% increase in Shanghai’s permanent resident population, the number of community public sports facilities increases by 8.95% accordingly. The growth of Shanghai’s population has a strong pulling effect on community public sports facilities, and the demand of community residents plays an important role in driving the increase in sports facility supply.

3.1.4. Spatial Gravity Center Evolution

The spatial gravity center method enables an analysis of the change trajectories of the spatial gravity centers of Shanghai’s urban community public sports facilities and permanent residents. The gravity center of community public sport facilities shows an offset trajectory that first moves southwest and then northwest, while the spatial gravity center of permanent residents exhibits an evolutionary process of shifting from northeast to southwest (Figure 5). Specifically, the gravity center of community public sports facilities shifted from (121.438° E, 31.213° N) in 2010 to (121.430° E, 31.198° N) in 2014, moving southwest by 1.867 km. From 2015 to 2024, it further shifted from southwest to northwest by 2.882 km, reaching the position of (121.421° E, 31.224° N). This movement trajectory is consistent with the construction history of sports facilities in Shanghai’s various districts. From 2010 to 2012, southwest suburban districts, such as Minhang, Fengxian, Jinshan, and Songjiang, entered a rapid urban development track, with intensive construction of sport facilities in these districts. After 2012, northwest suburban districts like Jiading and Qingpu continued to develop, with accelerated expansion of sports facilities to meet demand, thus shifting the gravity center of community public sport facilities to the northwest. In addition, in terms of the gravity center offset speed, the offset speed of the gravity center of Shanghai’s community public sports facilities has slowed down.

The spatial gravity center of Shanghai’s permanent residents shifted from (121.440° E, 31.219° N) in 2010 to (121.422° E, 31.204° N) in 2024, moving southwest by approximately 2.629 km. This indicates that in recent years, the gravity center of Shanghai’s permanent residents has shifted from central urban districts to suburban areas—specifically towards western districts like Songjiang and Qingpu, and southern districts like Minhang and Fengxian—leading to a continuous increase in demand for community public sports facilities among residents in these areas. Meanwhile, two concentrated periods of population gravity center offset have emerged in Shanghai: 2010–2016 and 2017–2024. On the other hand, calculations of the distance between the gravity center of community public sports facilities and the population gravity center show that the spatial distance between the two is continuously expanding. It expanded from 0.808 km in 2010 to 2.243 km in 2024. This widening spatial distance indicates that the current spatial supply–demand structure of Shanghai’s urban community public sports facilities is not yet fully matched.

3.2. Influencing Factors

3.2.1. Single Factor Detection Analysis

Discretization methods are used to classify and process the data [26]. Based on the characteristics of the independent variables, appropriate discretization methods are selected to divide the data into 5–7 categories (

Table 4. Processing of independent variable classification methods.

Independent Variables	Classification Methods	Number of Categories
Terrain Conditions (X1)	Jenks	6
Location Characteristics (X2)	Jenks	5
Economic Development Level (X3)	Quantile	7
Traffic Conditions (X4)	Jenks	6
Construction Intensity (X5)	Jenks	6
Resident Activity Intensity (X6)	Quantile	7
Housing Price (X7)	Quantile	7

). To balance measurement accuracy and efficiency, the spatial scale is set to 1000 m, and factor detection analysis is performed on the discretized data.

As shown in Figure 6, all seven selected influencing factors passed the 1% significance test with extremely high significance levels, indicating that these factors have a certain explanatory power for the spatial matching between community public sport facilities and the population, though their explanatory degrees vary. Ranked by explanatory power (Q value), the influencing factors are in the following order: resident activity intensity (0.743) > construction intensity (0.726) > housing price (0.660) > traffic conditions (0.623) > economic development level (0.612) > terrain conditions (0.493) > location characteristics (0.492). In current academic circles, a Q value >0.5 is generally defined as a high level of influence. Combining the actual context of this study, we define independent variables with Q > 0.7 as core influencing factors, those with Q values between 0.6 and 0.7 as important influencing factors, and those with Q < 0.6 as general influencing factors. Among them, construction intensity (X5) and resident activity intensity (X6) have Q values >0.7, indicating that these two factors exert a strong impact on the spatial differences in the spatial matching between community public sports facilities and the population, thus being core influencing factors. Economic development level (X3), traffic conditions (X4), and housing price (X7) have Q values in the 0.6–0.7 range in factor detection, so they are classified as important influencing factors. Terrain conditions (X1) and location characteristics (X2) have relatively low Q values, with a smaller impact on the spatial differentiation of the spatial matching between community public sports facilities and the population.

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Analysis of core influencing factors

Construction intensity and resident activity intensity, with Q values of 0.726 and 0.743, respectively, are core influencing factors—indicating that urban development plays a crucial role in the spatial matching between community public sports facilities and the population in Shanghai. This aligns with the long-standing indicator-oriented mindset in community public sports facility construction, which adheres to metrics such as “per capita venue area” and “public sport facility coverage rate” and prioritizes the coordinated development of community public sports facility space with urban built-up areas and population activities. This development logic implies that the larger the urban and population scales, the more significant the reverse driving effect on the layout of community public sports facilities. Specifically, the construction of community public sports facilities can advance urban construction. It not only lays the foundation for meeting residents’ diverse needs but also promotes urban scale expansion. Cultural and commercial activities hosted at community public sports facilities enhance urban influence, create more job opportunities, and drive the growth of related industries. Meanwhile, the expansion of urban and population scales also places higher demands on the construction of community public sports facilities.

(2)

Analysis of important influencing factors

Important influencing factors include economic development level, traffic conditions, and housing price, indicating that economic conditions support the effective supply of community public sports facilities. Districts vary in economic development levels, leading to differences in funds allocated to community public sports facility construction. Economically active districts often have sufficient fiscal resources, which provide strong financial guarantees for such construction. Moreover, these areas typically take on the responsibility of building urban brand identities, so fiscal resources tend to tilt toward community public sports service facilities and their supporting infrastructure. Shanghai has formulated targeted indicators for community public sports facility construction, such as the “15-min fitness circle”, which effectively fills gaps in facility provision. In this process, Traffic conditions have become a key factor affecting spatial matching between community public sports facilities and the population. An interactive relationship exists between housing price and community public sports facilities. Increasing fiscal expenditure on facilities drives up surrounding housing prices via the capitalization effect, and a sound community sports service system also attracts more residents, further stimulating housing demand and raising property prices. However, land occupation by facility construction forces higher fiscal spending, creating fiscal deficit pressure. Land scarcity pushes up land prices, which indirectly influences housing price trends. Notably, sustained housing price growth increases land finance revenue, providing more abundant funds for the government to expand facility provision and forming a virtuous cycle.

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Analysis of general influencing factors

Terrain conditions and location characteristics have relatively weak explanatory power, but still affect spatial matching between community public sports facilities and the population to some extent. Different terrains influence facility layout choices and the types of facilities suitable for construction, restricting the improvement of spatial matching. As the core of a city, distance from the central business district (CBD) reflects regional development levels and directly impacts spatial matching, which is linked to the historical urban planning model of prioritizing cities over towns. However, with China’s high emphasis on the national fitness program, the supply of fitness venues has been strengthened, and a special plan to address rural public sport facility shortcomings has been launched. These measures aim to narrow the urban-rural gap in public sports facilities and promote balanced layout across regions. Against this backdrop, the explanatory power of Location Characteristics for spatial matching has gradually weakened.

3.2.2. Bivariate Factor Interaction Detection Analysis

This study employs bivariate factor interaction detection to analyze the interaction effects of pairwise factors on the spatial matching between community public sports facilities and the population [27]. The analysis results cover five types: non-linear weakening, single-factor weakening, bivariate interaction enhancement, independent enhancement, and non-linear enhancement.

As shown in Figure 7, all pairwise interactions between the independent variables fall into the bivariate interaction enhancement category. Among these, the interactions between location characteristics (X2) and construction intensity (X5) (0.812), location characteristics (X2) and resident activity intensity (X6) (0.831), and location characteristics (X2) and housing price (X7) (0.810) exhibit stronger effects. This reaffirms that construction intensity and resident activity intensity are core influencing factors for the spatial matching between community public sports facilities and the population in Shanghai, indicating that the spatial differentiation of such matching is primarily driven by urban development scale and resident activity scale. Meanwhile, although terrain conditions (X1) and location characteristics (X2) have relatively weak individual explanatory power, their combined explanatory power with other factors is enhanced to a certain extent. This suggests that both are indeed factors affecting the spatial differentiation of spatial matching—albeit with limited influence.

3.2.3. Driving Mechanism Analysis Based on Panel Regression

Table 5. Panel regression analysis results of supply–demand inconsistency index (2010–2024).

Variables	Model 1 (OLS)	Model 2 (Individual Fixed)	Model 3 (Two-Way Fixed)
Constant	2.14 ***	1.89 ***	1.56 *
X3: Economic Development Level	−0.32 **	−0.41 ***	−0.45 *
X7: Housing Price	0.18 *	0.22 **	0.28 *
X4: Traffic Accessibility	−0.15 **	−0.12 *	−0.19
X5: Construction Intensity	−0.09	−0.11 *	−0.14
X6: Residents Activity Intensity	0.45 ***	0.38 ***	0.33 *
Controls	No	Yes	Yes
Year FE	No	No	Yes
District FE	No	Yes	Yes
Obs	64	64	64
Adj. R-squared	0.452	0.685	0.768

Note: *** p < 0.01, ** p < 0.05, * p < 0.1. The dependent variable is the inconsistency index (a larger value indicates worse matching). A negative coefficient for X3 indicates that economic growth helps reduce mismatch (improve matching), while a positive coefficient for X7 indicates that rising housing prices exacerbate mismatch.

presents the regression analysis results of the panel data. The Hausman test result (p < 0.01) rejects the random effects hypothesis, so the two-way fixed effects model (Model 3) is adopted as the benchmark explanatory model. The model’s goodness of fit (Adjusted R²) reaches 0.768, indicating that the explanatory variables can effectively explain the spatio-temporal variation in supply–demand mismatch.

Combined with the regression results of the two-way fixed effects model, we find that each driving factor presents a significantly differentiated mechanism of action, which deepens the findings of the influencing factor detection in the previous section.

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Corrective effect of economic input

The level of economic development has a significant negative impact on the supply–demand inconsistency index. This means that the more developed the regional economy is, the stronger the fiscal transfer payment capacity will be, and the more capable it is to “fill” the supply–demand gap by building more sports facilities. This verifies the classic theory that “fiscal capacity determines public service supply”, indicating that economic growth is the material basis for alleviating the “man-land contradiction”.

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Crowding-out effect of land rent

Notably, housing prices are significantly positively correlated with the supply–demand inconsistency index. This indicates that in areas with high housing prices, the opportunity cost of every land development is extremely high. Driven by profit-seeking capital, public welfare land such as sports facilities is often crowded out by commercial or high-end residential land, resulting in the supply of facilities failing to keep up with the speed of population agglomeration, thus exacerbating the supply–demand mismatch. This finding strongly supports the previous hypothesis about “spatial mismatch”.

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Pressure effect of population activity

The coefficient of residents’ activity intensity is significantly positive, indicating that in areas with high instantaneous population agglomeration (such as residential clusters during morning and evening peak hours), the existing supply capacity of facilities is facing huge challenges. This suggests that simply increasing supporting facilities for the registered population is not enough, and the dynamic needs of the actual active population must be considered.

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Auxiliary role of transportation and construction

The improvement of traffic accessibility significantly reduces the mismatch index, indicating that a convenient transportation network expands the service radius of sports facilities, which virtually increases the effective supply.

Compared with the geodetector, which focuses on identifying the interaction of factors, the panel regression model quantifies the net effect of a single factor after controlling for the historical background of each district and macro policy shocks. The two methods complement each other. The former reveals the “non-linear mechanism”, while the latter establishes the “causal robustness”, which together ensure the scientificity and reliability of the conclusions.

4. Discussion

This study, through long-term panel data, confirms the existence of a significant supply–demand mismatch in public sports facilities during Shanghai’s rapid suburbanization process. This finding verifies the applicability of the spatial mismatch hypothesis in compact megacities. Unlike the employment spatial mismatch caused by deindustrialization in Western cities, Shanghai’s mismatch is more manifested as a contradiction between “population decentralization” and “supply inertia of public goods”. The following discussion will be carried out from three dimensions: formation mechanism, planning paradigm transformation, and governance model.

4.1. Mechanisms: Institutional Inertia and Rent Gap Dynamics

The research results show that although the overall supply level is improving, the supply–demand gap in the suburbs is still expanding. This reveals the deep-seated logic of resource allocation in megacities.

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Fiscal fragmentation caused by administrative divisions. Different from the single-center urban model, the supply of public goods in Shanghai is highly dependent on the fiscal capacity of each district-level government. Although the population growth in the central districts has stagnated, they maintain a high level of facility services relying on mature financial foundations and stock space renewal, while suburban governments, bearing huge population inflow pressure, still mainly allocate financial resources to infrastructure construction, resulting in a significant “time-lag effect” in the construction of soft public services.

(2)

Crowding-out effect of the rent gap. Regression analysis shows that housing prices have a complex impact on facility supply. Against the background of high land commercialization, sport facilities, as low-yield public goods, are often at a disadvantage in competition with commercial or residential land, especially in sub-center areas, where land prices are soaring. This market-driven spatial exclusion is a common challenge faced by global high-density cities in the process of renewal.

4.2. Paradigm Shift: From “Static Blueprint” to “Dynamic Adaptation”

Existing urban planning often sets per capita indicators based on static census data, and this “static blueprint” planning is difficult to cope with the mobility of population flow. This study finds that the moving speed of the population center is much faster than that of the facility center, indicating that the traditional planning response mechanism has failed. Therefore, for Shanghai and similar global megacities, it is urgent to establish an “Adaptive Planning Mechanism Based on Dynamic Population Monitoring”.

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Reserving flexible zoning. In suburbs with obvious population inflow trends, planning departments should not only focus on the current permanent population but should predict based on the growth rate of historical panel data (Growth Elasticity) and reserve “White Sites” in advance for future public facility construction to prevent land from being completely locked by high-density housing.

(2)

Composite utilization of stock space. In the central urban area, in view of the depletion of new land, we should learn from the experience of Tokyo, New York, and use vertical zoning or time-sharing strategies to embed sports facilities on the roofs or idle spaces of schools and commercial complexes to cope with the high-density “man-land conflict”.

4.3. Governance Implications: Data-Driven Precision Allocation

The methodological framework of this study proves the value of multi-source data in urban diagnosis. For policy makers in international cities, this provides a new path beyond “empiricism”. It is recommended to shift from “top-down administrative instruction allocation” to “precision allocation based on big data of supply and demand”. Urban managers should use LBS data, such as mobile phone signaling and social media check-ins, to construct a “community vitality heatmap” to accurately identify “resource deserts” with high population density but low facility stock. This precision intervention can not only improve the efficiency of fiscal fund use, but also is a specific practice to achieve spatial justice and implement the United Nations Sustainable Development Goal.

4.4. Limitations

Although this study reveals the long-term evolution law of supply and demand of public sport facilities in Shanghai, there are still certain limitations. The main one is that due to the availability of statistical yearbook data, the analyses remain at the district level. This leads to the Modifiable Areal Unit Problem, which may mask the micro-spatial inequalities between streets and communities within the district. For example, a district may have an overall supply–demand balance, but there may be internal differences of “more in the south and less in the north”. In addition, this study mainly focuses on the quantity matching of facilities and does not fully consider the differentiated satisfaction of residents’ needs by the quality (such as maintenance status) and type (such as basketball courts, swimming pools) of facilities.

Future research will strive to break through data barriers and use mobile phone signaling big data, high-precision POI data and remote sensing images to carry out refined evaluations at the sub-district or even grid scale to verify the applicability of macro laws at the micro level.

5. Conclusions

This study, framed by the supply–demand equilibrium theory, integrates research methods, including the geographic concentration index, inconsistency index, facility–population growth elasticity, and spatial gravity center method to systematically analyze the spatio-temporal structural characteristics of supply and demand between Shanghai’s community public sports facilities and permanent residents from 2010 to 2024, and identify their core influencing factors and mechanism of action. The main research conclusions are as follows:

First, the spatial distribution of Shanghai’s community public sport facilities and permanent residents shows a differentiated evolutionary trend characterized by “central dispersion and suburban stability”. In central urban districts, the geographic concentration index of both facilities and population has declined significantly due to population outflow and optimized facility layout, accompanied by a reduction in high-agglomeration-level areas. In suburban districts, the agglomeration degree has remained low over the long term due to the relatively balanced distribution of population and facilities. The spatial differentiation pattern of these two elements aligns closely with Shanghai’s urban function decentralization and suburban urbanization processes.

Second, the supply–demand matching status between facilities and permanent residents has continued to optimize, but with local imbalances. The number of coordinated administrative districts increased from six in 2010 to thirteen in 2024, with coverage expanding from central urban areas to suburbs. Huangpu and Chongming districts transitioned from lagging/coordinated to leading status. However, Baoshan District has remained lagging for a long time, and the distance between the spatial gravity centers of facilities and population expanded from 0.808 km to 2.243 km, reflecting that spatial adaptation between supply and demand has not yet been fully coordinated.

Third, the facility–population growth elasticity and the system of influencing factors reveal the driving logic of supply–demand matching. At the city-wide level, population growth has a significant pulling effect on facility supply. Suburban districts, such as Chongming and Fengxian, have elasticity coefficients exceeding 100, while some central urban areas show negative elasticity due to population outflow. Among the influencing factors, construction intensity and resident activity intensity are core driving factors, with economic development, traffic conditions, and housing prices as important supporting factors. All factor combinations exhibit the “bivariate interaction enhancement” effect, highlighting the coupling role of urban development elements in supply–demand matching.

This study breaks through the limitations of overall research and reveals the law of “spatial mismatch” between supply and demand from a spatial perspective, deepening the understanding of the driving mechanism for the matching of supply and demand for facilities in megacities, and providing a basis for optimizing public service spaces and implementing policies. In the future, we can delve into micro-scale streets and communities, refine the correlation between population activities and facility use based on multi-source data, use quantitative models to analyze policy net effects and factor thresholds, conduct cross-regional comparative verification, and focus on exploring dynamic adaptation mechanisms in urban–rural fringe areas, further improving the accuracy and practical value of research.