A
Public Service Facility Configuration of Agricultural Counties in the Central and Western Regions Based on the Improved Optimal Supply-Demand Accessibility MethodA
Abstract
Influenced by Split Urbanization and Dependent Urban-rural Relations, agricultural counties in the central and western regions generally face issues such as imbalanced spatial distribution of public service facilities, declining service quality, and insufficient accessibility. This study takes Pucheng County in Weinan City as a case study and constructs a research framework of “demand analysis - accessibility measurement - allocation optimization”. First, the permanent population and its movement network are identified based on cell phone signaling data, and the complex network analysis method is used to determine the usage preference and actual demand. Secondly, the coverage and accessibility of public service facilities are quantitatively evaluated through the optimal supply-demand allocation model. Finally, considering factors such as supply-demand relationship, coverage rate, and accessibility within the county’s public service facilities, a systematic allocation optimization plan is proposed. The findings reveal: (1) According to the population flow characteristics, the administrative villages in Pucheng County are classified into high-inflow type (11 villages), inflow type (38 villages), balanced type (49 villages), outflow type (128 villages), and high-outflow type (129 villages), with corresponding supply adjustment coefficients of 1.58, 1.32, 1.03, 0.78, and 0.54, respectively. (2) Based on the 30-minute walking service radius, the optimized coverage rates for educational, medical, and elderly care facilities in the county improve from 71.44%, 54.75%, and 35.17–82.40%, 84.56%, and 79.13%, respectively. (3) For medical and elderly care facilities, an innovative allocation mode of “precise gap-filling + medical-care integration + mobile services” is proposed. This study not only provides practical guidance for the allocation of public service facilities in agricultural counties in the central and western regions, but also offers a theoretical basis for facility planning decisions in areas with frequent population flows and weak public services.
Keywords:
Agricultural Counties
Complex Network Analysis
Optimal Supply-demand Accessibility Method
Public Service Facilities
1 Introduction
In order to better achieve economic, efficient, and balanced urbanization, China proposed a policy focusing on county towns as key carriers of urban development in 2022 (Chen & Hu, 2024). Agricultural counties in central and western China are the most common county type in China, and are also the main areas of population outflow (Wu et al., 2020). In the process of urbanization, although the wealthier farmers can buy houses in county towns, most of them find it difficult to achieve stable employment. This situation gradually leads to a “half work and half tillage” livelihood model, known as Split Urbanization (Wei et al., 2022). In this context, the urban and rural population flows frequently, and the distribution of permanent population may change significantly in the short term. As a result, the spatial layout of existing public service facilities in the counties struggles to meet the adjusted demand, leading to spatial mismatch issues (Cui et al., 2022). If economic constraints prevent farmers from purchasing homes in county towns, the high-quality employment opportunities and public services in county towns will still attract them to work and consume in county towns, forming a dependent urban-rural relationship. Meanwhile, resources in areas such as economy, education, and healthcare gradually become concentrated in county towns, further exacerbating the problems of insufficient investment in rural public service facilities, declining service quality, and reduced accessibility (Cui et al., 2022; Pan et al., 2022). Consequently, there is a need for accurate assessment of the supply-demand relationship of public service facilities in counties to enhance supply efficiency and fairness and to optimize their spatial allocation (Chen, 2023). County public service facilities mainly include administrative, cultural, commercial, educational, medical, and elderly care (Gu et al., 2015). Among these, administrative, cultural, and commercial facilities have shorter service radii and higher usage frequencies, and are typically standardly equipped in each administrative village in accordance with relevant specifications. However, educational, medical, and elderly care facilities involve high construction costs, long periods to establish, and extended usage lifespans (Guo et al., 2023). If the supply capacity is not fully utilized after completion, it will cause serious waste of resources. Therefore, this paper focuses on the supply and demand relationship prediction, accessibility measurement and phased spatial allocation optimization of county education, medical and elderly care facilities.
Compared with county towns with developed economies or dominant industries, the prediction of the supply-demand relationship for public services in agricultural counties is more complex, which makes it difficult to accurately evaluate its accessibility. First, as a weak industry, agriculture has low economic benefits. Driven by comparative interests, a large number of rural laborers shift to non-agricultural employment in townships, county towns, and even larger cities (Wang et al., 2020). This change makes the primary users of rural public service facilities increasingly consist of left-behind elderly people, women, and children. As a result, the standard of per thousand people determined by norms often mismatches with the actual population structure and needs. Secondly, the main motivation for farmers moving to cities is to access high-quality public services, especially basic education and medical resources (Pan et al., 2022). Under the influence of usage preferences, the traditional public service configuration model based on the life circle is broken, and the capacity of some high-quality facilities is difficult to meet excess demand (Peng et al., 2023). Lastly, the economic level of central and western agricultural counties is far below the national average, and insufficient capital investment makes it difficult to achieve a balanced layout of public service facilities in the county, limiting the applicability of demand forecasting models based on service radii (Wei et al., 2022). To address these issues, it is necessary to clarify the usage preferences of county residents, the real supply-demand relationship, and the accessibility of facilities. The Optimal Supply-demand Accessibility Method (OSD) provides clear physical meaning and can flexibly calculate public service indicators like per capita service travel time, service travel time at various demand points, and service coverage within specific radii. It also evaluates the rationality of facility accessibility, service capacity, and spatial configuration (Zhai et al., 2022). In order to better elucidate the preferences of county-level residents regarding the utilization of public service facilities, this study improves the OSD model. First, a county population flow network is constructed based on cell phone signaling data. With complex network analysis, it identifies population flow preferences and the supply-demand relationship. After determining the supply adjustment parameters, the OSD model is used to measure and optimize the facility allocation. Compared with the traditional standardized configuration methods, life circle methods, and the nearest distance method, the improved OSD method better accommodates usage preferences and supply-demand relationships, effectively addressing spatial allocation problems of public service facilities (Wei et al., 2022).
This study takes Pucheng County, Weinan City, Shaanxi Province as the research object. Located in the central part of the Guanzhong Plain, Pucheng County has historically been an important agricultural county, with approximately 65% of its population residing in rural areas and more than 40% of the employed population working in agriculture (Dang, 2017). How to achieve efficient utilization of public service facilities under limited investment conditions and promote equal development of public services is a key issue in improving local living standards. The main objectives of this study include: (1) predicting the characteristics of population flow and the supply-demand relationship for public services in the county based on population flow network analysis; (2) determining the supply adjustment parameters for public service facilities in Pucheng County and calculating their coverage and accessibility indicators; (3) proposing the optimized allocation plan for educational, medical, and elderly care facilities in Pucheng County. The academic contributions of this study are primarily reflected in: (1) enhancing the accuracy of public service accessibility predictions through an improved Optimal Supply-demand Accessibility Method for counties with complex public service supply-demand relationships, such as agricultural product-producing counties, counties surrounding large cities, and counties experiencing population loss; (2) providing scientific evidence and practical references for the configuration of public service facilities in agricultural counties in the central and western regions through empirical research.
2 Literature review
As a spatial carrier for providing public products and services, the configuration level of public service facilities directly affects the quality of life of residents (Li et al., 2021). In the evaluation index system, accessibility is one of the most critical measurement criteria. The concept was first proposed by Hansen (1959) and defined as “the potential for interaction between network nodes”. It was originally used to explore the relationship between urban land use and accessibility. Subsequent studies have widely applied it to the spatial layout assessment of public service facilities such as education, medical care, and parks (Fajardo-Magraner et al., 2023; Fan & Cheng, 2022; Jin et al., 2019). Current methods for measuring the accessibility of county public service facilities primarily include the supply-demand ratio method, the nearest distance method, the potential model method, and the Two-step Floating Catchment Area Method (2SFCA). These methods have been applied to the supply and demand analysis of public service facilities in Longxi County, Gansu Province, the accessibility assessment of educational facilities in Jintang County, Sichuan Province, and the optimization of the layout of primary health facilities in Nanyao Autonomous County (Cui et al., 2022; Liu et al., 2024; Wang et al., 2021). However, these existing methods have obvious limitations. The supply-demand ratio method can only reflect regional differences. The nearest distance method neglects the service capacity of facilities. The potential model and 2SFCA are constrained by the preset spatial threshold and can only evaluate the local supply-demand relationship. Due to the failure to comprehensively consider the actual demand characteristics such as service capacity and quality, these methods often remain at the theoretical analysis level (Gao et al., 2021). In practice, the rural life circle model is often used for facility configuration. This model delineates geographical units based on the daily travel patterns of residents, and its radius is usually determined by the time cost required to obtain services (Peng et al., 2023). Related application cases include: village planning in developed regions of China, rural settlement transformation in Wuhan City, and assessments of rural healthcare facilities in South Africa (Tian et al., 2018; Wei et al., 2024). However, the life circle method only considers temporal and spatial costs and fails to capture residents’ preferences for high-quality facilities outside their life circles. This measurement dilemma regarding real demand and usage preferences poses significant challenges to the accessibility measurement of public service facilities in agricultural counties in central and western China.
The Optimal Supply-demand Accessibility Method (OSD) was introduced by Zhai et al. in 2022 (Zhai et al., 2022), and its theoretical foundation originates from the classical transportation problem model. This model analyzes the spatial distribution characteristics of facility supply and demand, takes minimizing travel costs as the objective function, solves the optimal supply-demand matching solution, and calculates the spatial accessibility index based on the allocation results, thereby comprehensively reflecting the regional supply and demand relationship. Compared to traditional methods, the OSD model offers significant advantages. Unlike the supply-demand ratio method and the nearest distance method, it simultaneously considers the characteristics of both supply and demand sides, and improve the accuracy of accessibility measurement through quantitative allocation results. In contrast to the potential model and the Two-step Floating Catchment Area Method (2SFCA), the OSD does not require predefined fixed thresholds, and its calculation results have clear physical meanings, which can more objectively reflect the relationship between facility supply and demand (Liu, Xu, et al., 2024). Additionally, the model can flexibly generate multiple service indicators, identify low-accessibility areas, and support the production of thematic maps, effectively making up for the shortcomings of demand analysis. The OSD model has been validated in several empirical studies, including the optimization of community health service station layouts in Zhengzhou, the assessment of community park accessibility in Suzhou, and the measurement of suburban rural facility accessibility in Lanzhou. Research results indicate that its measurement accuracy significantly surpasses that of the widely used 2SFCA method (Liu, Xu, et al., 2024; Zhai et al., 2022). Nevertheless, at the county scale, due to the influence of usage preferences, the supply-demand relationship of public service facilities exhibits notable spatio-temporal heterogeneity. Therefore, when applying the OSD model for accessibility measurements, it is necessary to identify usage preferences and key facilities in combination with population flow characteristics to accurately determine the actual demand. Zhou and Hou (2021) successfully implemented efficient configurations of rural public service facilities in urban influence zones by analyzing population flow characteristics among village settlements using complex network analysis methods. Based on the above research, this research proposes a comprehensive “network analysis + OSD” framework. With Pucheng County as a case study, it constructs a population flow network via extensive cell phone signaling big data. Through complex network analysis, it identifies the actual demand and usage preferences for county public service facilities. Then, the OSD model is applied for spatial accessibility measurement. Ultimately, a scientific and reasonable facility configuration plan is proposed.
3 Data and methods
3.1 Study Scope and Data Sources
This study selected Pucheng County, Weinan City, Shaanxi Province as the research object. The administrative area of the county covers 1,548 square kilometers, comprising 14 towns and 10 townships with a total of 355 administrative villages and a registered population of 760,000. According to the survey data, there are currently 92 educational facilities, 49 medical facilities, and 14 elderly care facilities in the county (Fig. 1, Fig. 2). The study found that the public service facilities in Pucheng County exhibited significant spatial distribution imbalances and substantial differences in facility quality. Against the background of continuous outflow of county population, the siphon effect generated by a few high-quality public service facilities has become increasingly prominent, resulting in an imbalance in the spatial allocation of public service resources This phenomenon further exacerbated issues such as spatial differentiation in public service quality and inadequate accessibility.
The research data primarily includes the following five categories: cell phone signaling data within the study area, administrative boundary vector data, land use data, Baidu Maps POI (Point of Interest) data, and questionnaire interview data. Among them, the cell phone signaling data, provided by Smart Steps, covers 28 days of 2G/3G/4G communication records of users in Pucheng County during April 2023. Administrative boundaries and land use data come from the third national land survey conducted by departments of natural resources and planning. POI data is obtained from the Baidu Maps Open Platform (https://lbsyun.baidu.com/). Questionnaire data is collected through interviews with village officials randomly selected from 71 administrative villages. The questionnaire covers 13 aspects, including facility usage habits, household consumption willingness, facility quality requirements, workplace and income. Furthermore, interview records with county and township government officials are referenced as supplementary evidence for future public service facility planning intentions.
Fig. 1
Study Area of This Paper
Fig. 2
Current Status of Public Service Facilities and Residential Areas in the Study Area
3.2 Research Methodology
As illustrated in Fig. 3, this study constructs a three-stage research framework of “demand analysis - accessibility measurement - allocation optimization”. First, based on cell phone signaling data, the population flow connection between administrative villages is extracted to construct a population flow network. Multiple linkage analysis is further employed to identify significant flows within the network, thereby analyzing the population flow preferences and supply-demand relationships between settlement nodes. Secondly, by calculating supply adjustment parameters and applying the OSD model, the study evaluates service coverage and spatial accessibility, systematically revealing the supply-demand matching status of public service facilities at the county level. Finally, by integrating the population flow preferences, supply-demand relationships and matching analysis results, the optimal configuration plan for rural public service facilities is scientifically determined, including adjustment suggestions for the number of facilities, spatial layout and service quality.
Fig. 3
Research Methodology Pathway
3.2.1 Demand Analysis
A
Research indicates that residents’ travel behaviors significantly impact the demand for public service facilities and their spatial distribution (Cui et al., 2022). Although residents’ travel frequency cannot be directly equated with the frequency of use of public service facilities, at the county scale, the proportion of facility use frequency to daily travel frequency is relatively stable (Zhang & Qiu, 2020). This characteristic is particularly evident in agricultural counties, where the purpose of non-commuting travel by local residents is relatively single, mainly to obtain public services (Yu et al., 2023). Therefore, the behavior characteristics of residents (including preferences and frequency) identified by cell phone signaling data can effectively reflect their usage preferences and the supply-demand relationship for public service facilities. For specific methods of extracting residents’ travel behavior, please refer to our previous research (Zhou & Hou, 2021). To identify villagers’ usage preferences for public service facilities from travel behavior, this study introduces multiple linkage analysis in the field of transportation engineering to extract significant population flow patterns from the population flow network. First, the outflow volume of each settlement node is sorted in descending order and noted as X1 to Xk. The expected flow set for a node is defined as {}, where
, and
represents the set of all settlement nodes in the network. The calculation formula is shown in Eq. (1).
Step 1:
=
,
=
=…=
=0Step 2:
=
=
,
=
=…=
=0Step i:
=
=…
=
,
=
=…=
=0Step k:
=
=…
=
(1)
Secondly, the goodness of fit between expected values and actual observed values is evaluated by the determination coefficient (
), and the specific calculation formula is shown in formula (2). In the step-by-step calculation process, when the
value for step i reaches its maximum, it can be determined that the top i flows of that node belong to significant flow patterns.
=1
(2)
Since all public service facilities are located within administrative units, this study regards villages, county towns and townships as network nodes. By analyzing the topological characteristics of each node in the significant flow network, residents’ usage preferences for public service facilities can be effectively identified. Specifically, two indicators are used for evaluation. Node degree Eki represents the number of edges directly connected to node ki in the network. The calculation method is provided in Eq. (3). The higher the node degree value, the stronger the attraction of the public service facilities at the node, reflecting the more concentrated usage preferences of surrounding residents. Such nodes should be equipped with higher service capacity and better service quality. Node strength Esi is defined as the total number of connections between the edge of all incoming nodes, and its calculation method is shown in Eq. (4).
=
(3)
=
(4)
Where Eki is the incoming degree of node i; eij is the edge connecting node i to node j;
is the total number of connection between the edge.
This study further adjusts the supply of conventional public service facilities based on population usage preferences by establishing a supply adjustment parameter
for each administrative village, on the basis of existing facilities. First, the natural breakpoint method is used to divide administrative villages into five types according to the in-degree value: high-inflow type, inflow type, balanced type, outflow type, and high-outflow type. Initial supply adjustment parameters are then determined based on the ratio of preference-based population to planned service population (Su et al., 2022). Among them, the preference-based population refers to the demand population from source villages of node degrees. For villages with multiple usage preferences, the ratio of linkage strength for each preference to total linkage strength is calculated, allowing for separate calculations of demand populations for different preferences. Next, 20% of the villages in each category are selected for field surveys, and the actual usage preferences and demand are verified through questionnaires, which help refine the initial parameters. Finally, the adjusted service supply quantity
for each facility type is calculated based on the verified parameters.
5
Where N is the facility supply quantity calculated according to standard per thousand people metrics. The set Lj ={High-inflow type (LA), Inflow type (LB), Balanced type (LC), Outflow type (LD), High-outflow type (LE)}.
Finally, the public service facilities such as education, medical care, and elderly care are vectorized in the GIS platform. The specific method is to use the geometric centroid of the map where each facility is located as the supply point, and assign the adjusted service supply quantity parameters in the attribute table. At the same time, the geometric centroid of each administrative village acts as the demand point, and its attributes record the corresponding service population data. This process establishes a complete point feature data foundation for subsequent spatial analysis.
3.2.2 Accessibility Measurement
This study measures the accessibility of county public service facilities based on the Optimal Supply-demand Accessibility Method (OSD). Within a given geographical area, the demand point set V={1,2,…,m} represents m rural settlement points, where each administrative village or unit forms a settlement point with an attribute di indicating the population size of settlement point i. The supply point set U={U1,U2,U3}, where U1 − 3={1,2,…,n} represents three types of public service facilities: educational, medical, and elderly care facilities. Each facility point j has an attribute qj representing its service supply quantity, that is, the maximum service capacity of the facility. The variable cij indicates the spatial distance between settlement point ii and facility point j. Based on field survey data, the total service capacity of local public service facilities meets the total demand, ensuring the model has a feasible solution. By introducing decision variables xij to represent the amount of service allocated from facility j to settlement point i, an optimal supply-demand configuration linear programming model is constructed (Zhai et al., 2022). The calculation formulas are shown in Equations (6–9).
(6) | |
|---|---|
(7) | |
(8) | |
(9) |
Formula (6) is used as the objective function to minimize the overall transportation cost. Eq. (7) ensures that the total amount of service received by each demand point equals its total demand. Eq. (8) limits the service configuration for each facility point to not exceed its maximum supply capacity.
This model framework provides a quantitative analysis basis for the spatial optimization of public service facilities. The implementation steps based on the OSD model to evaluate the spatial accessibility of county public service facilities are as follows: ① a basic database of Pucheng County is established in the ArcGIS platform, including spatial location data and service capacity parameters for educational, medical, and elderly care facilities, as well as spatial positions and population size data for each demand point (administrative village). ② The OD time cost matrix is constructed by calculating the Manhattan distance between the facility point and the demand point. ③ The optimal supply-demand configuration model is adopted, and the open-source CBC linear programming solver is used to solve the model (Zhai et al., 2022). ④ Finally, various accessibility evaluation indicators are calculated, including the service acquisition time cost for each demand point and the service coverage rate within different service radii (≤ 15 minutes, 15–30 minutes, > 30 minutes), and the accessibility results are analyzed in depth. In order to enhance the operational feasibility, Zhai (2022) developed an OSD Facility Accessibility Calculation Tool in the Python 3.8 environment. This tool can directly read preprocessed GIS data, automatically build case models, and complete calculations by invoking the open-source CBC mixed integer programming solver (https://github.com/coin-or/cbc). The related tools and experimental data have been openly shared on the GitHub platform (https://github.com/trirumisu/OSD).
3.2.3 Allocation Optimization
This study systematically proposes an optimized configuration plan for county public service facilities. The specific implementation path includes the following key links. First, by comprehensively analyzing the characteristics of county population flow, the supply-demand matching status of public services, and facility coverage and accessibility indicators. This assessment aims to scientifically evaluate the rationality of existing facility layouts, the adequacy of regional service capacity, and the extent to which villager usage needs are met. Secondly, a differentiated configuration strategy is implemented: focus on facility supplementation, service capacity improvement, and resource sharing for the high-inflow type and inflow type villages; focus on the withdrawal and integration of redundant facilities for the high-outflow type and outflow type villages, so as to comprehensively improve the balance and spatial accessibility of the county public service system. Thirdly, it should rigorously follow national standard guidelines such as Guidelines for Compulsory Education Quality Evaluation (2021), Village Health Clinic Service Capacity Standards (2022), and Classification and Evaluation of Elderly Care Institutions (2023). These standards guide the determination of the quantity, spatial layout, and service quality standards for educational, medical, and elderly care facilities. Finally, according to the population flow characteristics and development trends of the five types of villages, targeted optimization strategies for county public service facility configuration are formulate.
4. Research Results
4.1 Demand Simulation Results
As illustrated in Fig. 4, the study identifies 11 high-inflow type villages (nodes 8, 2, 3, 13, 7, etc.), which are mainly located in the core and surrounding areas of the county seat, as well as along the transport corridors of Provincial Highways S201 and S106 that extend outward from the county seat. These areas exhibit the following characteristics. First, the transportation infrastructure is complete, and some areas have shown a trend of urbanization. Second, public service facilities (education, medical care, and elderly care) are mostly coordinated with new industrial parks, high-speed rail hubs, and town administrative centers, and receive sufficient financial investment, with high service quality. Third, as a regional population agglomeration center, it also serves as an employment center and high-quality residential function. Additionally, the study identifies 38 inflow type villages (nodes 20, 130, 18, 75, 278, etc.), which are mainly distributed in regional centers such as Sunzhen, Xingzhen, Chenzhuang Town, and Chunlin Town, as well as along major transportation routes like Provincial Highway S106, County Road X214, and X225. These villages maintain close connections with the county seat through the transportation network, making them regional centers for accessing education and healthcare services. However, the quality of their public service facilities is at a medium level. In addition, the study area contains 49 balanced type villages (nodes 62, 127, 1, 16, 24, etc.), concentrated along the main “two horizontal and two vertical” roads in the central part of the county. These villages retain typical rural characteristics with relatively population density, while their public service facilities are generally aging. In contrast, outflow-type (128) and high-outflow type (129) villages are mainly distributed on the edge of the county and in non-main road areas, facing problems such as backward transportation conditions, dilapidated buildings, and lagging development. Their public service facilities are not only insufficiently allocated, but also have low usage rates. Based on quantitative analysis, the study determines the supply adjustment coefficients for the five types of administrative villages as follows: high-inflow type 1.58, inflow type 1.32, balanced type 1.03, outflow type 0.78, and high-outflow type 0.54. This coefficient system provides a quantitative basis for the differentiated configuration of county public service facilities.
Fig. 4
Rural Classification Results Based on Population Flow Preferences
4.2 Accessibility Measurement Results
The OSD model was further constructed based on the service supply of education, medical and elderly care facilities, and the demand point m was redistributed. The accessibility measurement results are shown in Fig. 5.
4.2.1 Coverage Rate of County Public Service Facilities
Considering the standard service radius for county public service facilities and the travel range across various age groups, the study uses a 30-minute walking time as the evaluation threshold for public service facility coverage (Neutens et al., 2012). As shown in Table 1, the current coverage rates for educational, medical, and elderly care facilities within the county are 71.44%, 54.75%, and 35.17%, respectively, serving populations of 324,821, 248,937, and 159,917 people. Notably, the coverage levels for elderly care and medical facilities are significantly inadequate.
As illustrated in Fig. 5, educational facilities are mainly concentrated in the county and surrounding areas. These facilities offer high-quality services, so that residents in surrounding villages can also enjoy convenient and high-quality educational resources. Meanwhile, in the southwestern part of the county, including Jingyao Town, Xingzhen, and Sufang Town, due to the rich agricultural resources, large settlements and dense flow of people, educational facilities are more sufficient than other marginal areas, resulting in relatively higher coverage rates. However, in the eastern and northwestern parts of the county, such as Gaoyang Town and Hanjing Town, the prevalence of mountainous terrain, smaller and more dispersed settlement sizes, and lagging development result in generally limited and average-quality educational facilities with lower coverage rates. The distribution of medical facilities across the county is extremely uneven. High-quality medical resources are primarily concentrated in the county seat and its surroundings, and there is even an oversupply. Conversely, there is a severe shortage of medical facilities in the southern parts of the county, and the scale of other township medical points is insufficient, causing a significant imbalance between supply and demand and resulting in longer healthcare access times for residents. The number and capacity of elderly care facilities are seriously insufficient throughout the county. Only a few villages near the county seat can access basic services. The eastern part of the county is entirely devoid of elderly care facility coverage.
Fig. 5
Accessibility Measurement Results of Public Service Facilities Based on Improved OSD
4.2.2 Accessibility of County Public Service Facilities
Based on the concept of “15-minute rural community life circle”, this study employs 15 and 30 minutes as evaluation standards for the walking accessibility of public service facilities in Pucheng County (Su, 2021) in combination with the maximum tolerable walking time. As shown in Fig. 5 and Table 2, the 15-minute walking accessibility rates for educational, medical, and elderly care facilities are 52.55%, 41.97%, and 10.81%, respectively. There is a widespread imbalance between supply and demand for public service facilities across the county, among which the accessibility of elderly care facilities is particularly insufficient.
In terms of spatial distribution, areas with high accessibility of public service facilities are concentrated in the central part of the county and around the two provincial roads extending outward from the county seat. These regions not only have high-grade facilities and strong supply capabilities, but also benefit from excellent transportation conditions. Additionally, favorable local employment conditions make them preferred destinations for population flow within the county, resulting in multiple high-inflow villages. As the supply of facilities matches the demand, these areas enjoy relatively higher levels of accessibility.
In contrast, the accessibility of facilities in other areas of the county is generally low. Inflow and balanced villages are often located around the township centers, mainly along major transportation routes. Although the supply of facilities in these areas meets the regulatory requirements and the services are relatively convenient, residents still tend to use the facilities in county towns or new areas, resulting in a large number of cross-regional commuting phenomena, which reduces the actual accessibility. The lowest accessibility levels are found in outflow villages. These villages are typically situated on the periphery of the county, where public service facility numbers are limited and their quality is poor. This is exacerbated by complex terrain, especially in the northern and eastern parts, increasing commuting difficulties. Additionally, the remaining villagers primarily engage in agricultural production with limited daily activity ranges that inhibit effective utilization of public service facilities. Meanwhile, those working outside the village demand higher quality facilities, further widening the gap between public service supply and actual resident needs.
4.3 Layout Optimization Results
Through a comprehensive analysis of population flow characteristics, public service facility coverage and accessibility, it is found that due to the insufficient supply of public service facilities in rural areas and the significant difference in urban and rural service quality, the overall coverage rate of county public service facilities has not reached the national standard. To this end, it is necessary to supplement public service facilities in key areas and promote the sharing of high-quality service resources between urban and rural areas. In order to optimize the layout of public service facilities, this study proposes a public service facility configuration model of “precise gap-filling + medical-care integration + mobile services”. First, a high-quality primary school is newly built in each of the six villages with high population inflow, including nodes 125, 130, 201, and 82. Specifically, nodes 125 and 130 require a fully functional six-year primary school with a capacity of 300 students to precisely address educational facility gaps. At the same time, the primary schools with poor school quality and low utilization rate in 10 villages with high population outflow, such as 53, 185, 108, and 40, will be merged, and school bus transfer points are added in 9 village nodes, such as 125, 75, 19, and 135, to improve the convenience of schooling. Secondly, new village-level clinics are built in 6 village nodes, including nodes 82, 284, 20, and 135, and town-level health centers with 80 beds are configured in nodes 125, 201, and 60. At the same time, all clinics are equipped with a mobile resident physician and basic medical equipment like blood cell analyzers and ECG machines to comprehensively improve healthcare services. Thirdly, 30-bed elderly service stations are built in nodes 34, 60, 10, and 75 to address the shortage of elderly care services especially in the eastern part of the county. Medical and nursing facilities are expanded and renovated on the basis of village clinics in nodes 176, 316, 88, and 35, and new comprehensive complexes are built at nodes 240, 223, 25, and 130 for efficient spatial and resource utilization. Finally, mobile health stations are configured across the 14 towns for full administrative village coverage, and neighborhood support points are established based on village committees to promote local elderly care. The optimized arrangement of public service facilities, with improved numbers, locations, and quality, is illustrated in Fig. 6, and the overall configuration level has been significantly improved.
Fig. 6
Optimized Accessibility Results of Public Service Facilities Based on Improved OSD
The optimized accessibility and coverage rates of public service facilities in Pucheng County are detailed in Table 1 and Table 2. The results reveal that the coverage rates for educational, medical, and elderly care facilities have improved by 10.96%, 29.81%, and 43.96%, respectively, reaching 82.40%, 84.56% and 79.13% after optimization, which basically meet the relevant standards. Within the facility coverage area, the 15-minute walking accessibility has significantly improved: educational facilities by 7.76%, medical facilities by 15.44%, and elderly care facilities with the largest increase of 35.55%. Through the innovative model of facility sharing and mobile services, the convenience of villagers in using public services has been significantly improved. It is worth noting that although the 15-minute walking accessibility rate of elderly care facilities is still less than 50%, the newly added village-level neighborhood assistance points and mobile services such as home-based elderly care have effectively reduced the travel needs of the elderly and made up for the lack of spatial accessibility.
Statistical object | Facility point | Original facility coverage rate | Optimized facility coverage rate | ||
Proportion | Population | Proportion | Population | ||
Population high-inflow type villages | Educational | 98.97% | 141087 | 100.00% | 142557 |
Medical | 95.96% | 136793 | 100.00% | 142557 | |
Elderly | 51.54% | 73470 | 90.97% | 129678 | |
Population inflow type villages | Educational | 69.32% | 32256 | 78.48% | 36519 |
Medical | 35.51% | 16523 | 95.21% | 44302 | |
Elderly | 28.25% | 13144 | 91.23% | 42448 | |
Demographically balanced villages | Educational | 66.18% | 66115 | 86.69% | 86596 |
Medical | 55.38% | 55324 | 85.58% | 85492 | |
Elderly | 39.43% | 39390 | 85.24% | 85154 | |
Population outflow type villages | Educational | 55.99% | 60498 | 71.38% | 77137 |
Medical | 27.36% | 29560 | 66.98% | 72372 | |
Elderly | 21.43% | 23159 | 61.99% | 66984 | |
Population high-outflow type villages | Educational | 43.14% | 24865 | 55.27% | 31857 |
Medical | 16.36% | 9427 | 68.96% | 39744 | |
Elderly | 18.66% | 10754 | 61.64% | 35527 | |
Pucheng county | Educational | 71.44% | 324821 | 82.40% | 374666 |
Medical | 54.75% | 248937 | 84.56% | 384467 | |
Elderly | 35.17% | 159917 | 79.13% | 359791 | |
Statistical object | Facility point | <15min | 15 ~ 30min | ||
Original | Optimized | Original | Optimized | ||
Population high-inflow type villages | Educational | 96.56% | 97.59% | 2.41% | 2.41% |
Medical | 89.70% | 93.74% | 6.26% | 6.26% | |
Elderly | 19.50% | 90.43% | 32.23% | 0.54% | |
Population inflow type villages | Educational | 48.33% | 51.92% | 20.99% | 26.57% |
Medical | 16.32% | 67.82% | 19.19% | 27.40% | |
Elderly | 3.55% | 38.16% | 24.70% | 55.29% | |
Demographically balanced villages | Educational | 37.02% | 60.93% | 29.16% | 24.49% |
Medical | 38.75% | 57.93% | 17.94% | 27.65% | |
Elderly | 2.26% | 32.17% | 37.17% | 53.08% | |
Population outflow type villages | Educational | 29.30% | 35.85% | 26.69% | 35.54% |
Medical | 14.15% | 24.64% | 13.21% | 42.33% | |
Elderly | 12.96% | 20.13% | 8.47% | 41.86% | |
Population high-outflow type villages | Educational | 17.59% | 19.63% | 25.55% | 35.64% |
Medical | 2.38% | 19.71% | 13.97% | 49.25% | |
Elderly | 5.92% | 17.75% | 12.74% | 43.89% | |
Pucheng county | Educational | 52.55% | 60.31% | 18.89% | 22.10% |
Medical | 41.97% | 57.41% | 12.78% | 27.14% | |
Elderly | 10.81% | 46.36% | 37.68% | 32.77% | |
5 Discussion and conclusion
This chapter will systematically explore the core contributions of this study, including analyzing the unique advantages of the proposed method in the layout of county public service facilities, providing specific recommendations for the configuration of public service facilities tailored to the characteristics of central and western agricultural counties, and discussing the innovative contributions of the improved OSD model to urban and rural planning theory. Based on these analyses, the chapter will present the final research conclusions.
5.1 Discussion
5.1.1 Advantages of This Study in the Layout of County Public Service Facilities
This study is based on the detailed planning work of the Luyanghu area in Pucheng County carried out by this team in 2023. The planning aims to solve the prominent issues such as uneven distribution and lack of accessibility of urban and rural public service facilities. The research findings have been directly applied to planning practices, which not only optimizes the layout of regional public service facilities, but also coordinates the spatial relationship of core functional land such as industry, residence and park in combination with the travel characteristics of residents. Through this systematic planning method, it can effectively promote the priority completion of urbanization in the planning area, accelerate the transformation of villagers into citizens, and gradually build a smaller-scale urban living circle. Compared with the traditional public service facility configuration research (Sun et al., 2024), the innovation of this study is mainly reflected in three aspects. By viewing urban-rural spaces, facility layouts, and surrounding land uses as an integrated whole, the study achieves a more systematic analysis. By analyzing significant paths in population flow networks, the study more accurately identifies the actual needs and usage preferences of rural residents, thereby enhancing the scientific basis for public service facility configuration. In view of the implementation problems caused by the current public service facility layout only dividing the short-term and long-term stages (such as staged redundancy or insufficiency of facilities), this study proposes a differentiated configuration strategy for key facilities and mobile facilities, and arranges facilities with strong functional correlation such as medical care and elderly care nearby or in an integrated manner. This method can maximize social and economic benefits under limited investment conditions and provide more precise guidance for project implementation.
5.1.2 Recommendations for the Layout of Public Service Facilities in Central and Western Agricultural Counties
In economically developed plain areas, under the influence of urban radiation effects, the quality and demand of rural public service facilities usually decrease as the distance from the urban center increases (Chen et al., 2023; Fang et al., 2023; Liu, Chang, et al., 2024). However, in agricultural counties, the “siphon effect” results in a severe imbalance in the configuration of urban and rural public services, forming a “unipolar concentration” pattern centered around the county town (An, 2023). This pattern is mainly due to the policy orientation of the county government - attracting villagers to buy houses in the city through public services such as high-quality education and medical care, increasing government revenue through land finance and accelerating the urbanization process. However, this practice often leads to insufficient investment in rural basic services by the state, further exacerbating the urban-rural gap and social injustice, especially in the allocation of education and medical resources. This not only increases the living burden of villagers, but also weakens their “social trust” in rural public services (Huhe et al., 2015). From the perspective of county development, moderate urban-rural differences are not entirely negative; the key is whether they align with the overall interests of the county rather than just the county town's interests (Tang et al., 2012). Therefore, public service facility configuration in central and western agricultural counties should focus on the following aspects. ① The survey indicates that 42% of county villagers prioritize urban public service facilities, with a particular emphasis on educational quality. It is recommended to appropriately reduce the number of rural facilities while improving quality and optimizing service radii: recommended service radii for high-quality education, medical, and elderly care facilities are 1000m, 900m, and 700m, respectively. ② Over the next decade, the rural elderly population is expected to grow by 21% with the initial completion of urbanization and the advent of “ageing society”. Thus, elderly care facilities should be prioritized in villages with population inflow, and gradually achieve full coverage of the county. It is suggested to add “endowment service stations” to existing clinics to save construction costs. ③ For areas where new facility construction is challenging, service quality can be enhanced by increasing personnel and raising supporting standards, along with providing transport points and mobile facilities to improve accessibility. ④ Urban and rural facility investments should be coordinated according to villagers’ usage preferences, ensuring efficiency while also considering fairness. Calculations indicate that rural public service investment in central and western agricultural counties should account for 34% of total county investment, ultimately forming a configuration model of “precise gap-filling + medical-care integration + mobile services” ( Fig. 7).
Fig. 7
Layout Model of “Precise Gap-filling + Medical-care Integration + Mobile Services”
5.1.3 Contributions of the Improved OSD to Urban and Rural Planning
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Firstly, existing research on public service facility configuration often uses time and distance as accessibility metrics, but fails to effectively reflect facility service capacity and residents’ usage preferences. This mismatch results in a gap between public service supply and demand post-planning. To this end, this study calculates the supply adjustment parameters based on the population flow relationship before the accessibility measurement, and solves the above problems by predicting the population flow preference and real supply-demand relationships. Secondly, the method proposed in this study is applicable not only to selecting sites for educational facilities, arranging medical facilities, optimizing transportation stations, and planning park layouts within urban and rural planning but also in evaluating the rationality and convenience of facility use after implementing related spatial plans. Furthermore, compared with the OSD model proposed by Zhai, this study uses dynamic cell phone signaling data to significantly enhance the accuracy of population identification. On the one hand, the difference between the regional resident population data identified by the model and the household survey results during the epidemic is less than 5%. On the other hand, this method can accurately judge the real usage preferences of residents at the demand point, effectively making up for the shortcomings of Zhai’s study (Zhai et al., 2022). In addition, compared with similar studies, the method proposed in this paper is simpler and does not require complex mathematical calculations. By using the CBC solver, the model calculation time only takes about 2.3 seconds, which is convenient for promotion and application. Finally, by comparing the OSD calculation results before and after the improvement, the public service facility configuration method of this study is more economical and efficient. As shown in Fig. 8, first, under the conditions of the same number of facilities and spatial layout, the improved OSD model increases the service coverage of county education, medical and elderly care facilities by 10.44%, 14.84% and 17.11% respectively compared with the traditional OSD model. Second, while ensuring that service coverage meets standards, the total supply of facilities optimized by the improved OSD is reduced by 8%-10% compared to the traditional model, significantly saving service costs. Third, the improved OSD more precisely identifies villagers’ usage preferences for public service facilities, enabling smart urban-rural facility layouts. For example, field surveys show that residents of villages 303 and 304 prefer to use the primary school in village 323, while the traditional OSD model incorrectly allocates their needs to villages 8 and 280. The improved OSD accurately predicted the real demand, achieving a higher supply-demand matching degree than results calculated by the traditional model.
5.2 Conclusion
This study improves the optimal supply and demand allocation method (OSD) to form a comprehensive research framework of “network analysis + OSD”, and applies it to the configuration of public service facilities in agricultural counties in the central and western regions, which effectively addresses the research gap of reflecting both residents’ actual needs and usage preferences in facility configuration. Taking Pucheng County as an example, the study first identifies residents’ usage preferences based on significant population flow characteristics within the county. By introducing supply adjustment parameters, it reveals the true supply-demand relationships for public service facilities. This method expands the research dimensions of public service facility configuration by fully considering the impact of usage preferences on configuration outcomes. Secondly, the coverage and accessibility of education, medical care, and elderly care facilities in agricultural counties are calculated based on the OSD model, which expands the application scenario of the OSD model. Finally, this study first proposed the unipolar concentration characteristics of public service facilities in the central and western counties, systematically revealed the internal mechanism that leads to unfair rights and interests of villagers, and offered optimization strategies accordingly. The study results indicate that based on population flow characteristics, the 355 administrative villages in Pucheng County can be divided into high-inflow type (11), inflow type (38), balanced type (49), outflow type (128) and high-outflow type (129), and the corresponding supply adjustment coefficients are 1.58, 1.32, 1.03, 0.78 and 0.54 respectively. With the 30-minute walking service radius as the standard, the coverage rates of educational facilities, medical facilities, and elderly care facilities in the optimized counties increased from 71.44%, 54.75%, and 35.17–82.40%, 84.56%, and 79.13%, respectively. On this basis, the study innovatively proposes a “precise gap-filling + medical-care integration + mobile services” model for public service facility configuration. This study not only offers scientific guidance for the configuration of public service facilities (including quantity, location, and operational strategy) in agricultural counties of the central and western regions, but also provides decision support for facility configuration in other areas with frequent population flows and underdeveloped public services. The prominent advantages of this study lie in two aspects. On the one hand, the supply adjustment parameters are calculated based on the real needs and usage preferences of county residents, making the configuration of public service facilities more scientific. On the other hand, the use of more precise cell phone signaling data significantly improves the accuracy of identifying supply-demand relationships. It should be pointed out that although cell phone signaling data effectively presents population flow patterns, further integrating the comprehensive impacts of public service facilities, workplace, and commuting relationships will allow for a more accurate prediction of supply-demand relationships and resource allocation optimization. Based on this, future research will focus on analyzing residents’ travel behavior characteristics, systematically examine the spatial relationships between facilities, residences, and workplaces, and promote the in-depth development of county public service configuration research.
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Author Contribution
J.Z. and W.W. wrote the main manuscript text. W.W. also prepared the visualizations. M.C. and W.W. developed the software and conducted data curation and formal analysis. J.Z. and Y.Z. conducted the investigation and managed project administration. Y.L. contributed to investigation and manuscript review and editing. S.L. and J.Z. were responsible for conceptualization and funding acquisition. Q.H. supervised the project and served as the corresponding author. All authors reviewed the manuscript.
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1.Ethical statements
Ethical approval
This article does not contain any studies with human participants performed by any of the authors.
Informed consent
This article does not contain any studies with human participants performed by any of the authors.
2.Data availability
The datasets generated during and the current study are available from the corresponding author on reasonable request.
3.Competing interests
The authors declare no competing interests.
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4. Funding
Declaration
This research was funded by the National Natural Science Foundation of China under Grant 52408051, Natural Science Basic Research Program of Shaanxi under Grant 2024JC-YBQN-0541, Fundamental Research Funds for the Central Universities under Grant 300102414603, Technology Innovation Center for Land Engineering and Human Settlements, Shaanxi Land Engineering Construction Group Co.,Ltd and Xi'an Jiaotong University under Grant 2024WHZ0237. These funds belong to Jizhe Zhou.
