Mismatch between infrastructure supply and demand within a 15-minute living circle evaluation in Fuzhou, China

Abstract

Rapid urbanization has gradually increased the contradiction between the demand and supply of urban resources. The quantitative optimization and adjustment of the infrastructure of the 15-min living circle is conducive to the scientific formulation of living circle planning guidelines, and also allows the evaluation of the effectiveness and practicality of policies. We investigate the spatial allocation pattern of infrastructure construction, the actual service capacity of facilities, and the spatial matching of facility service supply and residents' demand from the spatial dimension. Taking Fuzhou City as an example, this study uses multi-source network big data to accurately quantify the supply and demand, and constructs a 15-min living circle facility service supply evaluation system based on the kernel density analysis method, the network analysis method, and the supply and demand matching model. We propose infrastructure enhancement strategies in conjunction with the current status of Fuzhou's urban development. This study also further explores the factors influencing the spatial distribution of basic service facilities and the construction status of community living circles in China. The results show that (a) The distribution of infrastructure presents a different spatial distribution pattern from the actual service supply within the living circle. (b) The infrastructure service in the main area of Fuzhou can basically cope with the demand of residents, but there is still a mismatch between supply and demand. The areas with insufficient supply are mainly distributed in the periphery of the study area. (c) In order to further improve the construction of community living circle, we should first focus on the sub-districts with low service supply level and insufficient supply. According to the specific distribution of facilities, the number of public service facilities should be increased or decreased based on the demand of residents. This paper enriches the practical application of multi-source network big data in urban infrastructure construction, provides a guideline for the spatial layout and resource allocation of infrastructure in 15-min living circles in other cities.

1. Introduction

Since the reform and opening up, China's urbanization level has increased significantly, with the urbanization rate of the resident population increasing from 17.92% in 1978 to 65.22% in 2021 (http://www.stats.gov.cn/(accessed on 10 February 2022)). Rapid urbanization has brought new challenges and issues, and the contradiction between the demand and supply of resources has gradually come to the surface. Insufficient supply of resources such as urban public housing, medical care, education and transportation, while supply differences within cities lead to structural public resource shortages [1]. China's “New National Urbanization Plan” requires urban planning to be people-oriented, shifting from emphasizing only quantitative balance to focusing on both quality and quantity to meet the needs of residents, and from focusing only on economic production to meeting the needs of residents for a better life. The government has put in place requirements for rational allocation of social resources and matching supply and demand.

The 15min living circle is a community business circle formed by the clustering of various commercial forms, and its goal is to achieve equal and precise allocation of public service facilities. The construction of living circle is an essential part of urban system construction [2,3]. In 2021, Fuzhou City was officially identified as one of the first pilot urban quarter-hour convenience living circle areas in China. In December of the same year, the Fuzhou Municipal People's Government issued the “Fuzhou City Pilot Program for Promoting the Construction of Urban Quarter-Hour Convenient Living Circle”. Therefore, it is necessary to evaluate the construction of the 15-min living circle in Fuzhou City.

There is no consensus on how to more accurately assess the performance of the current infrastructure services within the living circle. Previous studies have focused on education resources [4], medical resources [[5], [6], [7]], sports facilities [8,9], and elderly facilities [10,11]. However, a high-quality community life circle should consistently and efficiently allocate social resources in concert with the diverse needs of residents. It requires a combination of services that are well configured. The comprehensiveness of the evaluation of public service facilities needs to be strengthened. Combined with government documents, it is pointed out that priority should be given to residents' needs for basic livelihood security, high-level leisure and cultural life, and the needs of the elderly and special groups in the community. Therefore, this study selects 14 kinds of basic service facilities, corresponding to three dimensions-vulnerable care, cultural and educational construction and convenient services-to evaluate the construction of 15min living circle.

The living circle planning practice in various places is mostly simplified to whether the whole residential area can be covered within 15 min walking distance of the current facilities. The supply is considered sufficient if it is covered [12]. Fewer studies have been conducted to integrate population needs. The new era of urban construction planning needs to be people-oriented planning to match supply and demand, so as to improve the happiness of residents' lives. And if the population is brought into areas with high concentrations of service facilities, that will further widen the mismatch between supply and demand [13]. Therefore, in order to improve the sharing of resources and the rational distribution of basic service facilities, it is necessary to assess the matching of supply and demand of service facilities [14]. Multi-source big data, characterized by large storage scale, high data precision and high timeliness, will further improve the accuracy of research and bring new opportunities for urban construction research.

Therefore, this study takes the 15-min living circle in the main urban area of Fuzhou as an example, and uses network big data to precisely explore the spatial allocation pattern of infrastructure construction, the actual service utility of facilities, and the spatial matching of facility service supply and population demand from the spatial dimension. In addition, we further discuss the factors influencing the spatial distribution of basic service facilities and the current situation of the construction of community living circles in China. This study solves three problems: (1) constructing facility service evaluation indexes based on the use of facilities within 15min walking distance to evaluate the actual service supply of facilities. (2) Using population to characterize demand, a spatial matching query is conducted for each of the three types of service facilities to analyze the matching of supply and demand in the study area. (3) Discussing the supply and demand matching results with the current situation of urban development, proposing strategies for upgrading the basic service facilities in Fuzhou, and providing scientific guidance for urban construction.

2. Materials and methods

2.1. Study area

Fuzhou is located at latitude 25°15′∼26°39′N and longitude 118°08′∼120°31′E. It is a prefecture-level city and the capital of Fujian Province, with a total area of 11,968 km2. By the end of 2021, the total resident population of Fuzhou City was 8.42 million, with a regional GDP of RMB 1132.448 billion; the urbanization rate was 73.0%; a per capita disposable income of urban residents is 53,421. Fuzhou was awarded the title of "China's Livable and Workable City in 2020″ and "China's Happiest City”. We focus on the main urban area of Fuzhou. It contains four districts, CangShan District, Jinan District, Gulou District, and Taijiang District (part of the built-up area), and the total area of the main urban area of Fuzhou is about 311.8km2 by the end of 2021 and a total of 39 sub-districts are included (Fig. 1.).

Fig. 1.

Location of the districts and sub-districts in the city center of Fuzhou.

2.2. Data sources and pre-processing

2.2.1. Residential zoning and population data

The population count can, to a certain extent, represent the demand for basic service facilities. Firstly, using ENVI 5.1 software, the remote sensing information extraction method is adopted, and the high-resolution remote sensing images are processed by geometric correction, alignment, false color synthesis, stitching and cropping. Referring to the existing LUCC classification system, visual interpretation and human-computer interaction interpretation are carried out to obtain the residential land vector data. Second, building outlines and building heights within the study area were crawled from Baidu Maps (https://lbsyun.baidu.com/, accessed on 10 November 2021) and overlaid with the vector boundary space of residential land to filter out residential buildings. Then, divide the building height by the average building height per floor to get the number of building floors and enter it into the attribute list. Based on the population data of each sub-district from the 7th National Census published by the Fuzhou City Bureau of Statistics (access on http://tjj.fuzhou.gov.cn/zz/zwgk/tjzl/tjxx/202111/t20211115_4242784.htm (accessed on 10 November 2021)), the population of each building was estimated. Matching the buildings to the crawled settlement contour, the residential population is the sum of the population of all buildings within the settlement area, calculated by equations (1), (2), (3). Finally, we combine poi data, satellite images and field surveys to mark the actual entrances and exits of residential areas and establish a population database of residential areas in Fuzhou (Fig. 2.). There are 2199 residential zones in this study (Table 1.). The calculation formula is as follows.

$$D_k=\frac{\left(R{A}_K\times n\right)}{R{A}_j}\times {SP}_j\text{,}$$ (1)

$$R{A}_j=\sum _{a=1}^K(R{A}_{jK}\times n)\text{,}$$ (2)

$$P_i=\sum D_{ik}\text{,}$$ (3)

where: D_k is the population of the kth building; RA_jk is the contour area of that building belonging to sub-district j and n is the number of building floors; RA_j is the total residential area of sub-district j; SP_j is the total population of that sub-district; a = 1,2 … K, is the total number of buildings in sub-district j; Pi is the population of the ith residential area and D_ik is the population of building k included in residential unit i. Thirty residential areas were randomly selected as samples, and the number of households in these residential areas was investigated through the survey. The calculation results of the above formula were tested according to the average household size of 3.5 persons (the results of the seven popular population statistics in Fuzhou City). The average accuracy rate is at 85%, so this estimation is considered to be a good response to the population of residential areas.

Fig. 2.

Population distribution map and road network.

Table 1.

Data display for residential.

Number	District	Sub-district	Population	Number	District	Sub-district	Population
A1	Cangshan	Jinshan	1697	A6	Jinan	Gushan	5243
A2	Jinan	Yuefeng	1871	A7	Taijiang	Yingzhou	1368
A3	Gulou	Huada	446	A8	Jinan	Gushan	1624
A4	Gulou	Hongshan	1799	A9	Taijiang	Aofeng	3489
A5	Cangshan	Jinshan	6224	A10	Gulou	Hongshan	1329
… …	… …	… …	… …	… …	… …	… …	… …

2.2.2. Basic service facilities

In this study, a total of 14 types of basic service facilities under three dimensions of disadvantaged care, cultural and educational construction, and convenient services were selected, and the poi data in Gaode Map was extracted using python (, https://lbs.amap.comaccessed on 4 May 2022). We also check the data with clear information published (e.g. education and medical data) with the data published by the Fuzhou Municipal Government. After screening, de-weighting, correcting and spatially matching the huge poi data, a total of 28599 pieces of data were obtained. Among them, 311 items are under the category of vulnerable care, 2523 items are under the category of cultural construction, and 26267 items are under the category of convenient service (Appendix 1). The crawled poi data are spatially matched with the study area to obtain the number of public facilities in each administrative sub-district.

2.2.3. Road network

Open Street Map platform was used to obtain open-source data (www.openstreetmap.org, accessed on 25 November 2021) to extract the road network data in and around the study area. The data was also corrected by combining Google Map high-precision images and the Fuzhou City Comprehensive Traffic Plan (2010–2020). Roads that are not suitable for pedestrian traffic, such as railroads and highways, are screened out, and finally a road network data that is more in line with the real situation is obtained (Fig. 3.) [15].

Fig. 3.

Methodological framework.

2.3. Methods

This study first explores the spatial distribution characteristics of 14 kinds of infrastructure using kernel density analysis. Afterwards, a service facility supply index is established based on the network analysis method to quantify the service supply of vulnerable care, culture and education, and convenient service facilities. Finally, the supply-demand mapping is conducted with the actual population demand to explore the supply-demand matching relationship between population and resources in the main urban area of Fuzhou（Fig. 3.）.

2.3.1. Kernel density method

The kernel density method is a commonly used method that directly reflects the density of point data distribution in a certain range, and can reflect the relative concentration of point data. It is widely used in the layout description of spatial point data [16]. In this paper, the poi point data of 14 kinds of infrastructures acquired in the main urban area of Fuzhou City at the end of 2021 were selected for kernel density analysis. The k-order nearest distance method with a search radius of 1000 m and an image element size of 50 m × 50 m is used to obtain the results of spatial pattern aggregation distribution of residential areas and 14 kinds of basic service facilities.

2.3.2. Service facilities supply index

Accessibility is one of the most effective tools to measure the rationality of spatial allocation of public service resources [17]. Based on the accessibility calculation, this study constructs a service facility supply index to quantify the supply of facilities and services within the 15-min living circle. The measurement of the service facility supply index is divided into three steps.

• 1)Facility accessibility based on network analysis method

The network analysis method is based on realistic urban road networks for simulation calculations, which can realistically simulate the actual road capacity [18].

In this study, we take 2199 residential areas as demand points and 14 types of poi data as service supply points, and measure the accessibility of each residential area based on the real road network. We statistically analyze the number of accessible basic services of certain types within the 15-min living circle of each residential area. According to previous literature, we set the 15-min activity range of residents in the walking mode as 1000 m [17].

• 2)Accessibility-based sub-district facility coverage

In this study, the sub-district facility coverage index Ii (i indicates the type of basic service facilities) was constructed based on the results of the accessibility. Xi is the number of a certain type of basic service facilities covered within the living circle of each residential area counted, and then Xi is standardized and aggregated to obtain the Sub-district facility coverage degree Ii for 14 types of basic service facilities. The calculation process for each type of basic service facilities is similar. The specific calculation process is introduced by taking the elderly facility's coverage degree of sub-district in the main area of Fuzhou as an example.

The number of elderly facilities within reach of all residential areas in the study area, Xi, was counted separately. Standardized intervals were determined based on the maximum, minimum and median values of Xi (Table 2.). The number of Ni ∼ N6 in each of the six standardized zones was counted separately, and the percentage of the number of residential zones in each standardized zone was calculated based on the total number of residential zones N in each sub-district. The corresponding score Si for each zone was multiplied with the respective percentage and summed to obtain the elderly facility sub-district facility coverage index I_i., which is calculated by equation (4).

$$I_i=\sum _{\mathrm{i}=1}^6\frac{S_i{N}_i}{N}=\left(-3\right)\frac{N_1}{N}+\left(-2\right)\frac{N_2}{N}+\left(-1\right)\frac{N_3}{N}+\frac{N_4}{N}+2\frac{N_5}{N}+3\frac{N_6}{N}\text{,}$$ (4)

Table 2.

Standardized calculation of facility coverage index.

Level	Standardization interval	Score
None	Min	−3
Low	(Min, 0.4Med+0.6Min]	−2
Lower	(0.4Med+0.6Min，0.8Med+0.2Min]	−1
Medium	(0.8Med+0.2Min, 0.8Med+0.2Max]	1
Higher	(0.8Med+0.2Max, 0.4Med+0.6Max]	2
High	(0.4Med+0.6Max, Max]	3

N is the total number of residential areas within the study area, and N1–N6 represent the number of residential areas located within sub-district i with classification results of “None”, “Low”, “Lower”, “Medium”, “Higher” and “High”, respectively.

• 3)Facility service supply index

The 14 types of basic service facilities correspond to the Vulnerable Care Facilities category, the Cultural and Educational Facilities category and the Convenient Service Facilities category. The facility service supply index, as shown in equation (5), is the average of the sub-district facility coverage of each sub-category of facilities. The formula is as follows:

$$Gi=\frac{\sum I_{Ai}}{A}=\frac{I_{1i}+I_{2i}+\dots +I_{Ai}}{A}\text{,}$$ (5)

Gi is the facility supply index. A represents the number of sub-category of infrastructure. I_Ai is the facility coverage of the basic service facilities of category A in sub-district i.

2.3.3. Supply and demand mapping

In this study, the demand side is characterized by the population on the sub-district, where a larger population represents a greater demand for infrastructure services. The higher the facility service supply index the higher the service supply. According to the natural breakpoint method, the supply of basic service facilities is classified into low supply, medium supply and high supply; and the demand of residents is classified into low demand, medium demand and high demand. And the results are mapped one by one to explore their supply and demand matching relationship (Table 3.) [19].

Table 3.

Supply and demand matching table.

Matching Supply and Demand	Low Supply	Medium Supply	High Supply
Low Demand	Low-Low	Mid-Low	High-Low
Medium Demand	Low-Mid	Mid-Mid	High-Mid
High Demand	Low-High	Mid-High	High-High

2.3.4. Pearson correlation coefficient

The Pearson correlation coefficient indicates the correlation between two variables and takes values in the range of (−1, 1) [20]. The closer the absolute value of the correlation coefficient is to 1, the stronger the correlation between the variables; the closer it is to 0, the weaker the correlation. Greater than 0 indicates a positive correlation between the two variables, and less than 0 is in a negative correlation. In this study, the Pearson correlation analysis tool of Arcgis system was used to calculate the spatial matching relationship between raster layers with equation (6).

$$C_g=\frac{\sum _{k=1}^n\left(z_{ik}-u_i\right)\left(z_{jk}-u_j\right)}{\left(n-1\right){\delta }_i{\delta }_j}\text{,}$$ (6)

where C_g is the correlation coefficient between raster layers i and j; z is the image element value; i and j are stacked raster layers; k is the specific image element; u is the average value of image elements in the layer; n is the total number of image elements; δ is the standard deviation.

3. Results

3.1. Distribution characteristics of 14 kinds of basic service facilities

3.1.1. Quantitative distribution characteristics

The number of basic service facilities distributed in different sub-districts varies widely (Fig. 4.). The cumulative distribution of the three different categories of facilities also varied. However, Jianxin Sub-district, Jinshan Sub-district, Xindian Sub-district and Gushan Sub-district all have an absolute advantage in the distribution of the number of the three categories of facilities, and all of the Sanchajie Sub-district is at a lower level.

Fig. 4.

Three types of facilities number statistics histogram, vulnerable care facilities (a), cultural and educational facilities (b), convenient service facilities (c).

Under the vulnerable care dimension (Fig. 4. (a)), elderly facilities are most distributed in Jinshan Sub-district, followed by Jianxin Sub-district. Both Jinshan Sub-district and Jianxin Sub-district are located in CangShan District. Children facilities are most distributed in Xindian Sub-district, followed by Jianxin Sub-district. From the cultural and educational construction dimension (Fig. 4. (b)), the sub-districts with the most facilities are Jianxin Sub-district, Gushan Sub-district and Jinshan Sub-district; the sub-districts with scarce resources are Sanchajie Sub-district, Dongsheng Sub-district and Xiangyuan Sub-district. Among them, public cultural facilities are most distributed in Jianxin Sub-district. Kindergarten, primary school and senior school are concentrated in Chengmen Sub-district, Jianxin Sub-district, Gushan Sub-district and Gaishan Sub-district; different from the situation of kindergartens and primary school, senior schools not distributed in nine sub-districts, including Cangshan Sub-district, Cangxia Sub-district and Chating Sub-district. The number of transportation facilities, catering facilities and convenience stores is dominating under convenience service dimension (Fig. 4. (c)). All types of facilities are relatively evenly distributed, but all 8 types of convenience facilities in Jianxin Sub-district are much higher than those in other sub-districts. In contrast, Dongsheng Sub-district and Sanchajie Sub-district have the least quantity of 8 types of convenience facilities.

3.1.2. Spatial distribution characteristics

Overall, the distribution of all 14 types of basic service facilities shows a trend of gradual decrease from the city center to the periphery (Fig. 5.).

Fig. 5.

Kernel density analysis for 14 types of public service facilities.

From the analysis of vulnerable care category, the nuclear density analysis of elderly facilities shows “a high-value center, multiple sub-centers” phenomenon. There are relatively few children's facilities, but they show a clear spatial divergence, with the core areas of aggregation concentrated in Guxi Sub-district, Wenquan Sub-district and Nanjie Sub-district in Gulou District.

From the analysis of cultural and educational facilities, all kinds of cultural facilities are distributed within the main urban area except for the undeveloped areas in Xindian Town, Gushan Town and Chengmen Town, while the spatial locations of the high-density areas formed are different. Public cultural facilities are relatively denser only in the junction of Nanjie Sub-district, Dongjie Sub-district and Antai Sub-district in Gulou District; kindergartens form a number of high-density areas in Cangshan District, Gulou District, Taijiang District and Jinan District; primary school show a “large-small” dual-core spatial distribution pattern; the senior schools form a linear core area of Antai Sub-district, Shanghai Sub-district, Houzhou Sub-district and Cangqian Sub-district. The spatial distribution characteristics of different kinds of facilitiy in the convenience service category vary significantly. Convenience stores and catering facilities are widely distributed and show very obvious aggregation characteristics. The spatial distribution of drugstores and hospitals shows a gradual decrease from the central city to the peripheral areas. The spatial distribution of commercial complexes is relatively even, but there are small-scale gatherings in Guxi Sub-district of Gulou District, Houzhou Sub-district of Taijiang District and Jinshan Sub-district of Cangshan District. Communication facilities show a spatial distribution pattern of “large-medium-small” trinuclear, with Southeast of Dongjie Sub-district, Xiangyuan Sub-district and Jianxin Town. Operaters show a spatial distribution pattern of “large-medium-small”. Fitness facilities show a more obvious divergence between the northwest and southeast sides, mainly concentrated in the northwest side. The overall spatial distribution of transportation facilities in the main urban area of Fuzhou City shows an outward expansion with the subway line.

3.2. Supply of services in 15-min living circle

By calculating the constructed service facility supply index, we present the service supply of 3 major categories and 14 types of basic service facilities visually, and analyze and evaluate the current situation of facility service supply. We also explored the similarities and differences in the spatial distribution of the comprehensive index and the various types of indices.

Overall, all three categories of basic service facility supply distribution have obvious spatial heterogeneity. The cultural and educational construction category (Fig. 6. (b)) and the convenience service category (Fig. 6. (c)) have similarity and both show the spatial distribution characteristics of high in the northwest and low in the southeast. The coverage of the three types of facilities has higher values in Gulou District and the sub-districts along Minjiang River, while the service supply is poor in the southeast side of the study area (Fig. 6.).

Fig. 6.

Spatial distribution pattern of facility service supply for vulnerable care facilities (a), cultural and educational facilities (b) and convenient service facilities (c).

The distribution of the provision of basic services for vulnerable care facilities is more balanced (Fig. 6. (a)). On the scale of districts, the overall ranking of vulnerable care services in the four districts is as follows: Gulou District > Taijiang District > Jin'an District > Cangshan District. On the scale of sub-districts, the highest level is distributed in the sub-districts at the border of each district and the central part of Gulou District; the sub-districts with the lowest level of supply are Chengmen Sub-district and Luozhou Sub-district. Compared with the vulnerable care category, the spatial clustering of basic service facility supply of cultural construction category is poor, and the overall level of service supply is lower. In terms of the district scale, Jinan District > Gulou District > Taijiang District > Cangshan District. In terms of the sub-district scale, the sub-districts with the highest level of supply of the cultural construction category include 10 sub-districts such as Jinshan, Chanshan, and Houzhou Sub-districts, and the distribution of services is more dispersed. Areas with low levels of service supply accounted for a larger proportion of the study area, including most sub-districts in CangShan District, Antai, Yizhou, Yangzhong, and Yingzhou Sub-districts.

In order to further search for the service supply pattern of different types of basic service facilities, we combined the service supply results of 14 types of basic service facilities with the results of three major categories to explore the similarities and differences in the spatial distribution of the comprehensive index and various types of indices. The service supply of elderly facilities in the vulnerable care category shows similar spatial distribution characteristics as that of children and service supply; while the service supply grade of children's facilities in Wenquan, Dongjie, Guxi, Antai, Chating and Gaishan Sub-districts is extremely low. The spatial distribution of service supply of various types of facilities and comprehensive service supply of culture and education showed large differences. The service supply of public cultural facilities, kindergartens and primary schools are scattered in all regions, with different high-value concentration areas; while high schools are mainly distributed in the central core of the study area, with extremely low peripheral. The eight sub-categories of facilities in the convenience service category are consistent with the comprehensive service supply. 14 categories of facility service supply show similar spatial distribution characteristics, with a general spatial distribution pattern of high in the northwest and low in the southeast, high in the middle and low in the surroundings, and low values for all facility categories in Luozhou Sub-district and Chengmen Sub-district (Fig. 7).

Fig. 7.

14 kinds of public service facilities service supply.

3.3. Spatial matching of basic service facility supply and resident demand

3.3.1. Spatial distribution pattern of residents’ demand in fuzhou

The population in the study area shows a gradual increase from the center to the periphery. Specifically, the high-demand areas are distributed in Xindian and Gushan Sub-districts in Jinan District and Jianxin and Jinshan Sub-districts in Cangshan District. The medium-demand areas are mosaically distributed with the high-demand areas, gathered in Gaishan and Chengmen Sub-districts in Cangshan District, Hongshan and Wufeng Sub-districts in Gulou District and Yuefeng Sub-district in Jinan District. Low-demand areas are clustered in the central part of the study area, covering most of the Sub-districts in the entire Taijiang and Gulou districts (Fig. 8.).

Fig. 8.

Spatial distribution pattern of demand.

3.3.2. Supply and demand matching

There is an obvious spatial mismatch between residents' demand and facility supply in the study area. In general, the supply-demand matching of the three types of facilities shows a similar spatial distribution pattern (Fig. 9.). The level of supply and demand matching shows a decreasing trend from the central to the periphery. The sub-districts in the study area where public facilities of vulnerable care, cultural and educational construction, and convenient services can meet the needs of residents account for 87.18%, 87.18%, and 82.05%, respectively. The construction of existing facilities in Fuzhou City can meet the needs of most residents (Fig. 10.).

Fig. 9.

Spatial distribution pattern of supply and demand matching situation for vulnerable care facilities (a), cultural and educational facilities (b) and convenient service facilities (c).

Fig. 10.

Statistical map of the spatial match between service supply and residents' demand.

Specifically, in terms of matching supply and demand in the vulnerable care category, 25.64% of the sub-districts achieved a balance between supply and demand, 61.54% of the streets had more supply than demand, and only 12.82% of the sub-districts had basic service facilities within them that could not meet the needs of residents. The sub-districts with insufficient supply are clustered in the periphery of the study area, such as Jianxin, Gaishan and Chengmen Sub-districts in Cangshan District, Xindian Sub-district in Jinan District, and Hongshan Sub-district in Gulou District. Among them, 2.56% of the sub-districts are in a Low-High situation, where the service supply is much smaller than the actual demand, such as Jianxin Sub-district in Cangshan District (Fig. 9. (a)).

The supply of cultural and educational construction facilities in most sub-districts can meet the demand. There are 74.36% of sub-districts where supply exceeds demand, gathered in the middle of the study area. 12.82% of the sub-districts reached the balance of supply and demand, distributed in Luzhou and Gaisan sub-districts in Cangshan, Hongshan and Wufeng sub-districts in Gulou district, and Yuefeng Sub-district in Jinan district. The supply of cultural facilities within the sub-districts located in Taijiang District is higher than the demand of residents. The supply of cultural facilities within 12.82% of the sub-districts in the study area cannot meet the demand of the residents, such as Jianxin, Jinshan, and Chengmen sub-districts in Cangshan District, and Xindian and Gushan sub-districts in Jinan District (Fig. 9. (b)).

The convenience service facilities show a good matching level of supply and demand, and the service supply of most sub-districts could meet the needs of residents. There are 71.79% of sub-districts with more supply than demand, gathered in the middle of the study area. The supply of convenience service facilities within the sub-districts located in Taijiang District are higher than the demand of residents. There are 10.26% of the sub-districts reached the balance of supply and demand. There are 17.95% of sub-districts within which the convenience service facilities cannot meet the demand of residents, distributed in the periphery of the study area, such as Jianxin, Jinshan, Gaishan and Chengmen sub-districts in Cangshan District, and Xindian Sub-district and Gushan Sub-district in Jinan District. There are no areas where the supply is much smaller than the actual demand (Fig. 9. (c)).

Compared with the supply-demand match in the vulnerable care and cultural and educational construction categories, there are more sub-districts in the study area where basic service facilities in the convenience service category are in an under-supply situation. In addition, the periphery of the study area, such as Jianxin, Chengmen, and Xindian sub-districts, showed an insufficient supply of the three types of facilities in terms of matching supply and demand with residents.

4. Discussion

4.1. Factors influencing the spatial distribution of basic service facilities

To investigate the factors influencing the spatial distribution of facilities, this study introduces Pearson correlation analysis to correlate population with facilities and the spatial distribution among various types of facilities.

The population distribution of the residential area was correlated with the spatial distribution of 14 types of basic service facilities (PCCs1), the population of the residential area and the number of 14 types of basic service facilities within 15-min living circle of the residential area (PCCs2) (Table 4.). The PCCs1 are all greater than 0.6, indicating that the spatial distribution of basic service facilities has a positive correlation with the distribution of population living in the study area. The correlation between the number of population and the number of facilities within the 15-min living circle does not exist, i.e., a residential area with a large population does not mean that more public facilities are available within the living circle. To a certain extent, this indicates a mismatch between the spatial distribution of facilities and population, and the incomplete construction of 15-min community living circles in Fuzhou.

Table 4.

Spatial distribution correlation analysis of residents and facilities.

	type	PCCs1	PCCs2
Vulnerable Care Facilities	Elderly facilities	0.364	−0.033
	Children facilities	0.337	0.007
Cultural and educational facilities	Public cultural facilities	0.338	−0.025
	Kindergarten	0.474	0.053
	Primary school	0.401	0.012
	Senior school	0.373	−0.053
Convenient service facilities	Convenience Stores	0.472	0.003
	Drugstores	0.448	0.010
	Commercial complexes	0.363	−0.013
	Restaurants	0.417	−0.011
	Operators	0.353	0.006
	Hospitals	0.439	0.011
	Fitness facilities	0.377	0.032
	Transportation	0.429	−0.012
Total	Total	0.456	−0.009

*PCCs1：Spatial correlation analysis of population and facility distribution; PCCs2：Correlation analysis between the population of a residential area and the number of facilities that can be accessed within a 15-min living circle of that residential area.

The spatial distribution of 14 types of basic service facilities was overlaid and Pearson correlation analysis was conducted to investigate whether there was correlation between the spatial distribution of each type of facility (Fig. 11.). There is almost no correlation between the vulnerable care category and there is a positive correlation between kindergartens and high schools in the culture and education category. There is a strong positive correlation between all the convenience facilities except for operators, and the distribution of various convenience facilities is more concentrated. The results show that there are correlations among different types of basic service facilities: convenience stores and restaurants are distributed more around public cultural facilities, kindergartens and high schools; hospitals show a strong positive correlation with the spatial distribution of children's facilities, kindergartens, high schools and public cultural facilities, especially the strongest correlation in high schools and public cultural facilities; transportation facilities, public cultural facilities and high schools have a positive correlation with the spatial distribution of all types of facilities except operators. The spatial distributions of transportation facilities, public cultural facilities and high schools are positively correlated with all types of facilities except operators, i.e., public cultural facilities and high schools will have relatively more convenience stores, drugstores, commercial complexes, restaurants and hospitals distributed around them; transportation facilities will be distributed near all types of facilities to facilitate residents' travel.

Fig. 11.

Correlation results between the various types of facilities.

There are few studies in the previous literature on the relationship between the spatial distribution of different types of facilities, and it is also a complex issue whether the correlation between the spatial distribution of facilities of the same category and different categories is inevitably linked. It needs more research in different regions to be further verified.

4.2. Strategy for upgrading basic service facilities in the 15-min living circle in fuzhou

There is an obvious spatial mismatch between residents' demand and facility supply in the study area. In order to scientifically build a 15-min convenient living circle and achieve the purpose of accurate matching of supply and demand, in view of the problems existing in the status quo of the allocation of basic service facilities in the main urban area of Fuzhou, this study mainly puts forward the following optimization suggestions with reference to previous studies [12,17,20].

First, we focus on sub-districts with low service supply levels, especially sub-districts with a high distribution of public facilities but low facility coverage, such as Jianxin Sub-district. The poor road network construction within Jianxin Sub-district prevents residents from benefiting from basic service facilities within 15min. The construction of the urban road system should be improved as soon as possible, and the number of basic service facilities accessible within 15-min living circles in residential areas should be increased by improving the road network structure in the study area to achieve efficient sharing of service facilities.

Second, focus on sub-districts where the supply is lower than the demand, and increase the number of corresponding public service facilities as needed according to the specific distribution of facilities. Xindian and Gushan sub-districts in Jinan District are located at the edge of the main urban area and have slow economic development; however, due to their vast territory, they accommodate a large number of residents, resulting in the supply of three types of facilities in the area being much lower than the demand of residents. In order to achieve social equity, the construction of facilities in the region should be increased as soon as possible to meet the needs of local residents. For example, we should increase the supply of public facilities for vulnerable people by adding children facilities in these areas; increase the supply of cultural and educational services by building new public cultural facilities and primary schools; and increase the supply of convenience stores, restaurants, and fitness facilities to ensure adequate supply of convenient services.

Third, the number of facilities in streets where the supply is much greater than the demand should be reduced. The supply of all three types of facilities in the Gulou District and the sub-districts along the Minjiang River is greater than the demand, and there is an excess of resources, which will cause unnecessary waste and increase the sense of inequality among urban residents and intensify social conflicts. Spatial facilities should be reasonably distributed in close relation to the location of the population. Transferring public cultural facilities, commercial facilities and medical facilities in areas with excess resources to the periphery of cities with lower development levels will also boost the economic development of backward areas.

Finally, urban planners should coordinate and unify with governments at all levels to consider the city and regional scale at the macro-level, the sub-strict and community scale at the meso-level, and the residential area and building level scale at the micro-level as a whole. Scientific consideration should be given to the matching degree of supply and demand between population and the supply of various service facilities, so that social public resources can be reasonably allocated to meet the specific needs of each region and a more effective construction management system can be formed.

4.3. Optimization strategy of 15-min living circle planning based on the perspective of matching supply and demand

The construction of Fuzhou's 15min living circle, where the supply is higher than the demand, is concentrated in the city center, and the insufficient supply is distributed in the periphery of the city, which is consistent with the previous studies of other cities [20,21]. The construction of facilities is not the pursuit of more quantity, too much will cause unnecessary waste, should be closely related to the location of community sites to achieve a reasonable distribution of spatial facilities. The number of basic service facilities in the urban center area should be appropriately reduced, and more attention should be paid to the urban fringe and urban-rural combination areas. At the same time, urban expansion and future road construction should also be considered, and the location of facilities should be reasonably distributed by paying attention to changes in population distribution.

The unreasonable spatial distribution of areas with a large distribution of facilities but insufficient service supply within 15min walking distance is caused by the low utilization rate of facilities. The number of facilities in these areas should be appropriately reduced or service facilities with the same function should be integrated to improve the quality of facility services, increase the efficiency of facility use, and avoid useless waste of space. Integrate the construction of supplementary pedestrian road networks to form a compact and highly walkable community to ensure that residents can reach the facility points more directly and efficiently within the living circle, and improve the matching of supply and demand.

At present, it is mainly to provide basic security construction such as living services, which is still stuck in the traditional functionalist construction idea. The optimization of 15-minite circle needs not only to focus on the stock to develop corresponding optimization strategies, but also needs the cooperation of various fields; to improve the spatial layout of basic service facilities, but also needs to cultivate proper construction, maintenance and management methods to ensure the planning and construction of 15-minite circle. From planning to governance, the government plays a leading and coordinating role in the construction of the community living circle, while the community should always be people-oriented. The government not only needs to further change its thinking about the allocation of service elements in community living circles, but also needs to further consider whether power should be returned to residents and enterprises proper, and encourage residents to actively participate in the living environment that is built to achieve community co-governance [22]. It will become increasingly important in the future and will further strengthen the residents' sense of belonging to the community.

4.4. Construction of 15-min living circle evaluation system

The concept of community living circle has been practiced in several cities in China, such as the “Shanghai 15-min Community Living Circle Planning Guidelines”, Shenzhen's “15-min Community Living Circle Preparation Method”, Guangzhou's four-level public service center system to create a 15-min quality community living circle, and Chengdu's concept of “Marketplace Living Circle”. However, the construction of community living circle is still in the primary stage, and no unified evaluation standard has been formed, which makes the construction level of living circle inconsistent around the country.

In previous studies, the evaluation system of 15-min living circle has shown incomprehensive service supply evaluation system, large error in demand-side evaluation method and failure to consider the supply and demand matching situation. Wu H. Y. et al. And Yue Y. et al. Both took Shanghai as an example to establish the evaluation system of 15-min community living circle. Wu H. Y. et al. [21]used the questionnaire method to determine the weights of various types of facilities and used the weighted Kernel density estimation method to judge the overall facility services; however, the roles of fitness, communication, and facilities for the elderly and young children were ignored in their evaluation system, and since the functions of different types of facilities are different, categorization for discussion seems to be a better. Yue Y. et al. [20]developed the walkability assessment tool to calculate the walk scores of communities, which did not consider facilities for the elderly and children, and the assessment of educational facilities ignored public cultural facilities. Zhong L. & Xu L. Q [23]. used “walkability” data to represent the population demand, which is only a relative comparison of the concentration of population distribution. It cannot accurately count the actual population in the living area, which may cause errors in the demand assessment. The demand side of the Wu H. P. et al.‘s [24] study was judged by the proportion of youth, middle-aged, and elderly measured by cell phone signaling data. The proportional difference can only reflect the potential degree of demand for public service facilities. Some data on the elderly and youth will be missing, so the demand-side representation is not accurate, and the proportional difference in age will also be affected. Moreover, cell phone signaling data is difficult and expensive to obtain and is not universal. Bai M. et al. [ [25]] and Chen L. F. et al. [26] only consider the supply side, and consider the distribution of facilities within 15 min walking distance as attaining the standard, without considering the match between supply and demand.

However, the new era requires both refined urban management to meet people's pursuit of a better life and to avoid the phenomenon of resource waste and unreasonable distribution of social resources. Therefore, this study creates a set of evaluation system for the construction of 15-min living circle based on network big data, which ensures accuracy while considering the accessibility of residents to facilities in time and space, the balance of service supply of different types of facilities and the balanced relationship between residents' demand and facility supply. It makes up for the lack of previous studies.

4.5. Contribution and limitation

This study proposes a population quantity metric model using network big data and population census data, which precisely locates the population to the residential area level, solving the problems of insufficient precision of previous studies and a single evaluation scale for public service facilities, and innovating the practical application of network big data. Secondly, based on the perspective of matching supply and demand, a set of evaluation framework for the construction of public service facilities in the 15min living circle is constructed in the main urban area of Fuzhou, providing scientific support for urban construction.

However, there are still some shortcomings in this study. This study uses the total population to characterize the population demand without distinguishing the social attributes of the population. Future research should consider the needs of different social groups and explore the spatial match between the needs of different social groups and the supply of public service facilities. Analyze the spatial distribution of social groups with different incomes, genders and education levels and the matching relationship between supply and demand, so as to supplement the theory of social justice.

5. Conclusions

This study focuses on the construction of 15min community living circle, taking the main urban area of Fuzhou City as an example, and uses the network big data to precisely explore the infrastructure construction, the actual service utility, and the spatial matching of facility service supply and population demand from the spatial dimension. Then we propose optimization strategies for the planning and construction of 15-min living circle. The main findings are as follows.

• (1)The distribution of basic service facilities in the study area shows a different spatial distribution pattern from the actual facility service supply in the 15-min living circle. The distribution of facilities in overall shows a spatial distribution pattern of high center and low surrounding, while the supply of facilities' services shows a spatial distribution pattern of high northwest and low southeast.
• (2)Infrastructure service facilities are generally able to meet the needs of residents within the main urban area of Fuzhou City, but there is still a mismatch between supply and demand. The areas with insufficient supply are mainly distributed in the periphery of the study area.
• (3)In order to further improve the construction of 15min living circle, attention should first be paid to areas with low service supply level and areas where supply is less than demand. According to the specific distribution of facilities, the number of public service facilities should be increased or decreased according to the needs. Urban designers should also consider the overall situation from macro to micro dimensions; consider future urban expansion and road construction, pay attention to changes in population distribution, and reasonably distribute the location of facilities; increase the efficiency of facility use, and integrate the construction of supplementary pedestrian road networks to form a compact and highly walkable community.

Funding

This study was supported by the National Natural Science Foundation of China (32071578) and the Science and Technology Innovation Project of Fujian Province (KY-090000-04-2021-012).

Institutional review board statement

Not applicable.

Informed consent statement

Not applicable.

Author contribution statement

Huili Xie: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper. Xinke Wang: Performed the experiments; Wrote the paper. Zhenfeng Wang: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data. Zhiyong Shi: Analyzed and interpreted the data. Xiaoting Hu; Hong Lin; Xiangqun Xie: Performed the experiments. Xingzhao Liu: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data.

Data availability statement

Data will be made available on request.

Additional information

No additional information is available for this paper.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix 1. Various basic service facilities statistics

Category	Type		Amount 1	Ratio 1	Amount 2	Ratio 2
Vulnerable Care Facilities	Elderly facilities	Including nursing homes, senior centers and elderly universities	147	0.4%	311	0.8%
	Children facilities	Including playgrounds and childcare	164	0.4%
Cultural and educational facilities	Public cultural facilities	Including libraries, bookstores, community cultural activity centers, art galleries and museums	1764	4.3%	2523	6.1%
	Kindergarten	It is based on the list published by the government	502	1.2%
	Primary school	It is based on the list published by the government	168	0.4%
	Senior school	Including middle and high schools. It is based on the list published by the government	89	0.2%
Convenient service facilities	Convenience Stores	Includes manual and unmanned convenience stores	4513	10.9%	38460	93.1%
	Drugstores	Including herbal and western pharmacies	1933	4.7%
	Commercial complexes	Dominated by shopping centers, it integrates commercial retail, catering, leisure and health, entertainment, culture, education and other major functional activities of the city	4093	9.9%
	Restaurants	Including Chinese restaurants, Western restaurants, fast food and other dining facilities	14283	34.6%
	Operators	Including Mobile, Unicom and Telecom operators	223	0.5%
	Hospitals	Including community hospitals, health clinics and large hospitals that meet public health and basic medical services	3041	7.4%
	Fitness facilities	Including basketball court, football field, badminton, tennis, swimming, gym and other fee-paying venues and free public sports facilities	935	2.3%
	Transportation	Include all bus stops and subway stations	9439	22.9%
Total	Total		41294	100.0%	41294	100.0%