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Article

Exploring the Equality and Determinants of Basic Educational Public Services from a Spatial Variation Perspective Using POI Data

by
Hejie Wei
1,
Wenfeng Ji
1,
Ling Li
1,
Yi Yang
2,* and
Mengxue Liu
3
1
College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou 450046, China
2
College of Forestry, Henan Agricultural University, Zhengzhou 450046, China
3
Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2025, 14(2), 66; https://doi.org/10.3390/ijgi14020066
Submission received: 23 December 2024 / Revised: 26 January 2025 / Accepted: 6 February 2025 / Published: 7 February 2025
(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)
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Abstract

:
The equitable distribution of basic educational services is crucial for attaining educational fairness and promoting balanced demographic and economic growth. This research leverages point-of-interest (POI) data to analyze the spatial arrangement of basic educational service facilities in the Yellow River Basin of China. Employing kernel density analysis and spatial autocorrelation with a geographic information system tool, this study examines the spatial distribution of these facilities. It also applies geographically weighted regression to identify the primary factors influencing their spatial layout. This study reveals a pronounced disparity between the four downstream and five upstream provinces of the Yellow River Basin in terms of basic educational facility availability. In the downstream provinces, facilities constitute 82.45% of the total, markedly surpassing the level of 17.55% in the upstream provinces. The kernel density analysis shows that areas with a high concentration of educational facilities often align with provincial capitals, including Taiyuan in Shanxi Province, Xi’an in Shaanxi Province, Zhengzhou in Henan Province, and regions around Shandong Province. Significant regional differences exist within the Yellow River Basin. Preprimary, primary, and secondary education facilities exhibit strong spatial clustering, with Moran’s I indices of 0.26, 0.19, and 0.09, respectively. High–high clusters of preprimary education are predominantly found in the western region of the basin, whereas low–low clusters appear in some eastern and northern areas. Primary and secondary educational facilities show high–high clustering in the north. The spatial distribution of these educational facilities is chiefly influenced by the permanent population and the proportion of the tertiary industry. Per capita gross domestic product (GDP) and educational fiscal expenditure play secondary roles in influencing the spatial layout. The results have important practical significance for promoting the equalization of basic education public services and equal educational opportunities for the school-age population in the Yellow River Basin.

1. Introduction

Basic public services include essential social conditions necessary for sustaining national economic and social stability, safeguarding individuals’ fundamental rights to survival and development, and facilitating comprehensive human development [1]. These services are determined based on specific societal concepts in alignment with the stage and overall level of a nation’s socioeconomic development. Scholars have explored various research directions concerning basic public services [2]. Some studies primarily focus on the spatial distribution of basic public services [3,4] and residents’ satisfaction [5,6]. Others emphasize government functions and supply-side structural reforms [7,8,9]. For example, drawing from the theory of market failure, Samuelson [10] proposed a model wherein public services should be supplied by the government as a “single center”. This model examines the spatial layout and supply efficiency of urban public services and facilities. Other scholars have investigated the equalization of urban public services and facilities [11,12,13]. Lan et al. [14] and Song et al. [15] analyzed the influence factors and effects of spatial differentiation in public services. These scholars’ research, characterized by distinctive perspectives, provides an in-depth analysis of the spatial distribution and efficiency of public services.
Basic educational public services constitute a crucial component of basic public services, and they include preprimary, primary, and secondary education. The high quality of these services is directly linked to the overall construction of a modern socialist nation, serving as the foundation for the nation’s steady progress, a basic guarantee for the people’s right to education, and the cornerstone for building a high-quality education system [16]. Basic education plays a pivotal role in nurturing talents of various levels and types [17], enhancing the overall quality of the nation, and promoting the construction of socialist modernization. However, current disparities in China’s basic educational public services across regions, urban and rural areas, schools, and different educational groups are pronounced, necessitating urgent solutions to achieve regional equalization in basic educational public services [18,19,20]. The spatial distribution of basic educational resources constitutes a critical aspect of the geographical imbalances in public services [21], with scholars focusing on several key areas in their research on basic educational public services.
For quantitative analyses of the level of basic educational public services, researchers have employed a variety of methods and techniques for measurement [22,23]. For instance, Wen et al. [24] utilized principal component analysis, the Gini coefficient, and factor analysis to quantitatively measure the level of basic education in China. Additionally, some studies have adopted geospatial querying and spatial interpolation methods for calculating and modeling spatial indicators [25,26,27]. Michael employed the gravity model to assess the accessibility of secondary school facilities [28]. Concurrently, Malczewski and Jackson [29] applied GIS technology to outline educational resource regions, providing a spatial analysis perspective for the study of the level of basic educational public services. The application of these diverse methods and technologies enhances the range of quantitative research on the level of basic educational public services.
The spatial distribution characteristics of basic educational resources constitute a significant area of focus. For instance, Wang et al. [30] utilized point-of-interest (POI) data of educational facilities to investigate the spatial distribution characteristics of educational resources in the primary urban area of Xi’an. Additionally, Yeates [31] and Maybee [32] analyzed the site selection, scale, and facility layout of high schools and middle schools, delineating reasonable boundaries. Researchers such as Zhao et al. [33] observed a declining trend in basic educational resources in rural China from the northeast to the southwest. Furthermore, some studies have conducted satisfaction measurements concerning the spatial equity of basic educational resources [34,35,36]. These investigations offer valuable insights into comprehending the distribution of basic educational resources.
Exploring the factors influencing the spatial distribution of basic educational resources includes the equalization of basic educational facilities and their determinants [37,38]. Lü and Liu [39] employing fixed-effect models and multiple linear regression methods and scrutinized the factors influencing the equalization of basic educational facilities in China. Zhao et al. [33] identified that the spatial arrangement of basic educational resources in rural China is positively correlated with rural economic development levels and the administrative capacity of local governments. Wang et al. [16] uncovered that regions with lower value in terms of basic educational facilities in China are predominantly situated west of the Hu Huanyong Line, with this disparity being driven by factors such as population size, industrial structure, and urban area size. Xu et al. [40] detected a significant “core–periphery” spatial distribution pattern of basic educational resources concerning residential areas, underscoring the relationship between basic education and population concentration. Cai et al. [41] contended that the “urban–rural dual disparity” constitutes a primary factor affecting the equality of educational opportunities between urban and rural areas, highlighting the issue of urban–rural educational inequality. Zhang et al. [42] explored the relationship between construction land expansion and basic education schools in Shanghai based on POI data. Jiang et al. [43] found that the population distribution dominated the spatial distribution of primary school facilities in Chengdu. Huang et al. [44] suggested that the spatial distribution and accessibility of educational service facilities in Lincang County, Yunnan Province, an underdeveloped region in China, were positively correlated with population density, aging, and income levels. Furthermore, studies have explored the connection between educational policies and the spatial layout of basic educational resources, furnishing crucial insights for a more profound understanding of the distribution and influencing factors of basic educational resources [45,46,47].
Moreover, some research has focused on the spatial allocation and optimization of basic educational resources. These research domains include aspects such as spatial optimization paths [48,49] and policies [50,51,52] for achieving the equalization of basic educational resources. Additionally, the examination of the equalization of basic educational resources with respect to societal development attracts substantial interest among researchers [53,54,55]. Studies have shown that the equalization of basic educational resources contributes to fostering balanced population distribution and development [56] and enhancing the overall human resource pool [57].
Although significant strides have been made in the study of basic educational public services [58,59], most of these inquiries have lacked a geographical spatial perspective. Furthermore, their focus has predominantly revolved around compulsory education, with insufficient attention being given to preprimary education. As society progresses, research primarily centered on primary and secondary education may no longer adequately address evolving societal needs. The integration of big data into research methodologies has become increasingly prevalent, with big POI data offering distinct advantages in the study of spatial patterns of phenomena. POI data, characterized by large volumes, cost-effectiveness, and frequent updates [60], have the potential to enhance the efficiency and precision of research in the domain of basic public services, including basic educational facilities.
The Yellow River Basin stands as a crucial economic region in China, playing a pivotal role in the country’s socioeconomic development, particularly in poverty alleviation (e.g., implementing education subsidies, building rural schools, improving teacher quality, and increasing employment opportunities) endeavors. Originating in the Bayan Har Mountains in Qinghai Province, the Yellow River traverses nine provinces: Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan, and Shandong. Research within the Yellow River Basin has primarily concentrated on topics such as ecological protection and high-quality development [61,62,63,64,65,66,67,68,69,70,71]. Nonetheless, issues regarding the adequacy of the overall supply of educational resources and regional disparities remain pressing concerns within China’s basic educational infrastructure and are focal points of academic research [72,73,74]. Specific research on the spatial pattern of basic education and related aspects in the Yellow River Basin or its sub-regions has been limited [25,30]. Investigating the spatial distribution of basic education bears practical significance. This study, which takes the Yellow River Basin as its subject for researching basic educational services, serves as a representative example.
Based on the literature review presented above, this study addresses several knowledge gaps in the field of public service research related to basic education. These gaps include insufficient attention to preschool education and a lack of watershed and geospatial research perspectives. The study is founded on a big data analysis model concerning the layout of public educational service facilities. It employs kernel density analysis and spatial autocorrelation methods in ArcGIS to research and analyze the spatial pattern of basic educational facilities within the Yellow River Basin. Additionally, by collecting socioeconomic data and utilizing geographically weighted regression (GWR) and other methodologies, this study explores the factors influencing the spatial layout. This quantitative analysis of regional disparities and the equalization of the spatial pattern of basic educational facilities in the Yellow River Basin, combined with statistical data, aims to investigate the factors affecting the spatial pattern. The objective is to provide a theoretical foundation and scientific reference for the equalization of basic educational facilities and the equitable allocation of basic educational resources within the Yellow River Basin.

2. Materials and Methods

2.1. Study Area

The Yellow River Basin includes the geographical and ecological region influenced by the Yellow River, extending from its source to its estuary (Figure 1). The topography of the Yellow River Basin exhibits significant variations, characterized by elevated terrain in the western regions and lower elevations in the east, resulting in substantial geomorphological diversity across different areas. This basin holds significant importance as an energy and heavy industry hub in China, spanning the eastern, central, and western regions of the country. Its origins can be traced back to Qinghai, and it stretches all the way to Shandong, covering nine provinces and regions: Qinghai, Sichuan, Gansu, Ningxia, Shaanxi, Shanxi, Inner Mongolia, Henan, and Shandong. Within this basin, a diverse and complex ecosystem prevails, including forests, grasslands, deserts, and wetlands. These complex natural resource conditions have profoundly shaped the fundamental characteristics of socioeconomic development within the Yellow River Basin. Presently, the socioeconomic development of the Yellow River Basin is complexly linked with the ecological environment, facing a multitude of challenges. These include increasingly stringent resource limitations, overall inefficiencies in economic development, disparities in development between the upper, middle, and lower reaches, and the absence of a fully functional regional collaborative development mechanism. The gross domestic product (GDP) of the nine provinces and regions within the basin constitutes approximately 26% of the national total. The population residing in this basin accounts for approximately 30% of the national total. According to the statistical yearbooks of the provinces in the Yellow River Basin, the permanent population residing in the Yellow River Basin by the end of 2020 totaled 219.7076 million, and the region boasted a total of 65,909 basic educational facilities. Furthermore, the proportion of annual financial expenditure on education relative to the general public budget expenditure has demonstrated a consistent upward trajectory.

2.2. Data Sources

POI data from 2020 related to basic education (preprimary education, primary education, and secondary education) and fundamental map data, including vector maps of cities within the Yellow River Basin and the Yellow River water system, were obtained from the Resource and Environment Science and Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 4 January 2022). The original POI data were cleaned up by removing duplicate records, correcting incorrect locations, and processing missing data. The study used an algorithm based on unique coordinates to detect and delete duplicate records. Incorrect location data were identified and corrected by verifying the matching degree between the geographic coordinates of the POIs and the online map service. A comprehensive collection of socioeconomic data, which were crucial for analyzing the influencing factors, was obtained from the 2021 statistical yearbooks of each province. This compilation includes relevant socioeconomic information pertaining to the cities involved in this study.

2.3. Methods

2.3.1. Kernel Density Analysis Method

The study of the spatial pattern of basic educational facilities in the Yellow River Basin primarily focuses on the distribution of their quantity and the degree of concentration. In order to examine the concentration of basic educational facilities and visually depict their spatial distribution pattern, the kernel density analysis method is employed. The precise calculation formula is as follows [75]:
f n x = 1 n h ∑ i = 1 n k x − X i h
In Formula (1), k ( x − X i h ) represents the kernel function; h > 0 is the bandwidth; n is the number of POIs of basic education in the study area; x − X i represents the distance from the estimation point to the sample point X i .

2.3.2. Spatial Autocorrelation Analysis

The global Moran’s I statistic is employed to assess the spatial association and level of disparity in basic educational facilities among cities within the Yellow River Basin. If Moran’s I > 0, this indicates a positive spatial correlation among all spatial units of basic educational facilities, indicating that a higher I value corresponds to a greater degree of clustering. If Moran’s I = 0, this indicates a random distribution of basic educational facilities, devoid of spatial correlation. If Moran’s I < 0, this suggests a negative spatial correlation in basic educational facilities within the Yellow River Basin, with a smaller I value indicating a higher degree of dispersion [76].
I = n ∑ i = 1 n ∑ j = 1 n W i j ( x i − x ¯ ) ( x j − x ¯ ) / ∑ i = 1 n ∑ j = 1 n W i j ∑ i = 1 n ( x i − x ¯ ) 2
Here, n is the sample size, i.e., the number of spatial locations, x i and x i are the observed values at spatial locations i and j, and W i j represents the spatial relationship between locations i and j. When i and j are adjacent spatial locations, W i j = 1; otherwise, W i j = 0.

2.3.3. Geographically Weighted Regression Analysis

GWR is employed to analyze the influencing factors affecting the spatial pattern distribution of basic educational public facilities within the Yellow River Basin. Ordinary linear regression models seek to minimize the sum of squares of the distances from the parameters to the regression line. However, when dealing with spatial data, particularly data characterized by spatial non-stationarity, ordinary linear regression models prove inadequate for comprehensive modeling and analysis. In contrast, the GWR model incorporates the spatial structure into the linear regression framework, performing local regressions on spatial data. This approach captures the variations in the relationship between independent and dependent variables that occur with geographical spatial changes [77,78]. Consequently, it is well suited for handling data featuring spatial non-stationarity. The calculation formula is as follows.
y i = β 0 ( u i , v i ) + ∑ k β k ( u i , v i ) x j k + ε i
In Formula (3), y i is the dependent variable, β 0 ( u i , v i ) is the constant term, ( u i , v i ) represents the spatial location of the i sampling point; β k ( u i , v i ) is regression parameter k at sample point i; ε i is the random error at sample point i.
The selection of influencing factors of the spatial distribution of basic education service facilities is explained as follows. The development of these facilities is intrinsically linked to economic support. Education, as a people-centered endeavor, relies on educational infrastructure to serve the population. The construction and maintenance of educational infrastructure fall within the domain of tertiary industry. Given the influence of the household registration system, a significant portion of the floating population leaves their children in their original household registration areas for education, thereby exerting a minimal effect on the spatial distribution of basic educational facilities within the region. Consequently, this study conducts a regression analysis by employing the per capita resource quantity of basic educational facilities as the dependent variable and the per capita GDP, permanent population, proportion of tertiary industry, and educational fiscal expenditure as explanatory variables.
Prior to conducting a GWR analysis on the influencing factors, it is essential to first employ the ordinary least squares (OLS) method to assess the presence of multicollinearity among all variables and verify it using the variance inflation factor (VIF). The results reveal that the VIF for each variable is below 5, signifying the absence of multicollinearity among them. Consequently, all four selected factors can be utilized as explanatory variables. The influencing factors of basic educational facilities at the prefectural level of administrative units exhibit spatial instability. In comparison with conventional regression analysis models, GWR can effectively account for this spatial instability. To illustrate the advantages of GWR, both OLS and GWR models were employed to analyze and compare the data, as presented in Table 1. The coefficient of determination (R2) derived from the GWR analysis, amounting to 0.939, surpasses the R2 value of 0.914 obtained from the OLS analysis. Furthermore, the corrected value of the Akaike information criterion for GWR is lower than that of the OLS analysis, indicating that the GWR model is more suitable for examining the influencing factors of the spatial pattern of basic educational facilities compared with OLS.

3. Results

3.1. Distribution Characteristics of Basic Educational Facilities

3.1.1. Spatial Distribution of Absolute Numbers

The spatial distribution of basic educational facilities within the Yellow River Basin was uneven, and the absolute numbers of preprimary, primary, and secondary educational facilities were 33,558, 21,254, and 11,097, respectively (Figure 1). By examining the distribution of basic educational facilities across various cities within the basin, the proportion of basic educational resources in each city was determined. Regarding the number of preprimary basic educational resources, the proportions in Shaanxi Province, Shanxi Province, Henan Province, and Shandong Province were, respectively, 14.8%, 14.5%, 29.7%, and 25.7%. The proportions in Qinghai Province, Sichuan Province, Gansu Province, Inner Mongolia Autonomous Region, and Ningxia Hui Autonomous Region were less than 5.0%. It is evident that the upstream provinces of Qinghai, Sichuan, Inner Mongolia, and Ningxia Hui Autonomous Region face distinct disadvantages in terms of preprimary education resources compared with the downstream provinces. Henan and Shandong provinces, situated downstream, enjoy a clear advantage, collectively accounting for over half of the total basic educational resources, with each province contributing more than 25.0% of the total. While there may be slight variations in the proportions of primary and secondary educational resources among the provinces, the overall trend remains consistent, with Henan and Shandong provinces maintaining their advantageous positions and the five upstream provinces continuing to face disadvantages. In summary, when considering the statistics of basic educational resources, Shandong and Henan provinces exhibit a substantial advantage, followed by Shanxi and Shaanxi provinces, while the other five provinces contend with a disadvantageous position.

3.1.2. Density Distribution Characteristics of Absolute Numbers

Using the POI data for preprimary education, primary education, and secondary education in the Yellow River Basin as the data source, kernel density analysis was employed to analyze the density distribution of basic educational facilities and to perform a visual analysis (Figure 2).
The analysis findings indicate that preprimary education exhibits a higher density of distribution in the eastern region, with some areas in the central region displaying moderate density, while the western region experiences a more dispersed distribution. Notably, the distribution of high-value areas corresponds to the geographical concentration of certain central cities. These central cities, such as the regions centered around Taiyuan in Shanxi Province, Xi’an in Shaanxi Province, Zhengzhou in Henan Province, and the area centered around Shandong Province, constitute concentrated high-value areas. Conversely, most of the upstream regions within the Yellow River Basin exhibit a lower-density distribution. The density of the distribution of primary and secondary education closely mirrors the pattern observed in preprimary education, with variations in the regions where the median values are situated. The visual representation of the comprehensive POI data pertaining to basic education highlights that the high-value areas of basic educational facilities are prominently situated within the high-density zones of the eastern part of the Yellow River Basin. These zones include contiguous high-value areas in the capitals of the eastern provinces. In contrast, the educational resources in the western region exhibit a clear dispersion. This observation underscores the eastern region’s advantageous position concerning basic educational facilities while also highlighting the significant regional disparities in the availability of basic educational infrastructure.

3.2. Distribution Characteristics of Per Capita Basic Educational Facilities

3.2.1. Spatial Distribution Characteristics

The per capita quantity of basic educational facilities at the city level within the Yellow River Basin has been categorized into five distinct groups, as illustrated in Figure 3. High-value cluster areas for per capita preprimary public services are predominantly situated in the eastern region, with some northern regions such as Baotou in the Inner Mongolia Autonomous Region and Liaocheng in Shandong Province also forming part of this high-value cluster. In contrast, low-value cluster areas are primarily found in the western region, including areas such as Yushu Tibetan Autonomous Prefecture and Golog Tibetan Autonomous Prefecture in Qinghai Province. The per capita primary education public services exhibit a substantial high-value cluster area in select southern regions of Qinghai, Gansu, and Sichuan, with additional high-value clusters evident in certain eastern areas, notably Luoyang in Henan Province. Conversely, low-value clusters are primarily concentrated in the northern areas, such as Alxa, Bayannur, and Baotou in the Inner Mongolia Autonomous Region. Regarding per capita secondary education public services, high-value areas are concentrated in a few eastern regions, including Yuncheng in Shanxi Province and Xuchang in Henan Province. Conversely, low-value areas are predominantly located in the northern regions, such as Bayannur and Ordos in the Inner Mongolia Autonomous Region. The pattern of equalization for per capita preprimary public services closely aligns with the pattern observed for the absolute-number equalization, displaying significant disparities between the eastern and western regions. However, the issue of non-equalization in per capita primary and secondary education public services is not as pronounced as that observed in absolute numbers.

3.2.2. Aggregation Characteristics

The global Moran’s I values for preprimary, primary, and secondary education in the Yellow River Basin are 0.26, 0.19, and 0.09, respectively (Table 2). A Moran’s I value greater than 0 indicates a positive spatial correlation among all study units of basic educational facilities, with a higher I value corresponding to a greater degree of spatial clustering. The global Moran’s I index provides insight into the overall spatial aggregation pattern of basic educational facilities. Specifically, the concentration of preprimary educational facilities is relatively high, followed by primary education and secondary education.
To gain a more nuanced understanding of the internal variations and localized spatial clustering of basic educational facilities within the Yellow River Basin, it is essential to assess the distribution pattern and level of the local Moran’s I value (Figure 4). H-H indicates high–high clustering, signifying that the region possesses a high quantity of per capita educational resources, and neighboring areas also exhibit high quantities. H-L indicates high–low clustering, where the region itself has high quantities, but neighboring areas have low quantities. L-H indicates low–high clustering, indicating that the region itself has low quantities, but neighboring areas exhibit high quantities. L-L indicates low–low clustering, where both the region and its neighboring areas have low quantities. The L-L cluster areas of per capita preprimary educational resources are predominantly located in the western regions, including a substantial area. Examples include Haixi Mongol and Tibetan Autonomous Prefecture, Haibei Tibetan Autonomous Prefecture, Hainan Tibetan Autonomous Prefecture in Qinghai Province, and Gannan Tibetan Autonomous Prefecture, Longnan, and Dingxi in Gansu Province. On the other hand, H-H cluster areas are primarily concentrated in the eastern regions, including cities such as Sanmenxia, Luoyang, Zhengzhou, and Kaifeng in Henan Province and Jinan, Binzhou, Zibo, and Liaocheng in Shandong Province. For per capita primary educational resources, the L-L cluster areas are situated in Baotou, Ulanqab, and Hohhot in the Inner Mongolia Autonomous Region, while H-H cluster areas are primarily found in Sanmenxia, Luoyang, Jiaozuo, and Xinxiang in the southern part of the region. In the case of per capita secondary educational resources, the L-L cluster areas are located in Bayannur, Baotou, and Ordos in the Inner Mongolia Autonomous Region, with H-H cluster areas mainly being centered around cities such as Luoyang, Jiaozuo, and Linfen. Compared with primary and secondary education, the distribution of preschool education resources reveals significant disparities between the eastern and western regions, particularly in terms of aggregation characteristics. Unlike primary and secondary education, preschool education is heavily influenced by market dynamics, resulting in the swift concentration of superior resources in the east, which is considered a high-return area. Consequently, addressing the issue of unequal public services in preschool education has become increasingly urgent. Therefore, the government should assume greater responsibility in promoting the equitable distribution of public services in preschool education.

3.3. Influencing Factors of the Spatial Pattern of Basic Educational Facilities

In the GWR model, each research factor corresponds to a specific regression coefficient. The statistical analysis of the model’s regression results is summarized in Table 3. The analysis reveals that the regression coefficients associated with the proportion of the tertiary industry and permanent population exhibit the highest maximum values, with the coefficient for the tertiary industry displaying the most significant fluctuations. This indicates that these two factors exert a considerable influence on the spatial distribution of basic educational facilities. The regression coefficients related to the permanent population consistently demonstrate positive values, implying a positive effect of the permanent population on basic educational facilities. Conversely, the regression coefficients for per capita GDP, proportion of tertiary industry, and educational fiscal expenditure exhibit both positive and negative values. This suggests that these three factors exert both positive and constraining effects on basic educational facilities in various regions.
As illustrated in Figure 5, the regression coefficients associated with the permanent population are consistently positive, with the highest coefficient being observed in Baotou City and Hohhot City at more than 3.25. Coefficients with low values were distributed in the lower reaches of the Yellow River Basin. It is crucial to emphasize that addressing the ratio of the permanent population to basic educational facilities in the downstream region of the basin is pivotal for promoting balanced development in terms of population and education. Regarding the proportion of tertiary industry, the regression coefficients display both positive and negative values, demonstrating an increasing trend from the upstream to the downstream areas of the Yellow River Basin. The low-value areas for the proportion of the tertiary industry are predominantly situated in the upstream region, while downstream areas exhibit a higher proportion. This indicates a pronounced synergistic effect between tertiary industry and education, with an increase in the proportion of tertiary industry exerting a clear positive effect on the quantity of basic educational facility resources in the region.
In contrast, the regression coefficients linked to per capita GDP and education expenditure are comparatively smaller, and they are categorized as secondary influencing factors for basic educational facilities. The coefficients for per capita GDP include both positive and negative values, with the highest coefficient being recorded in Baoji City at 0.000321, while the lowest coefficient is −0.136 in Jining City. The regions characterized by lower per capita GDP values are predominantly situated in the downstream region of the Yellow River Basin. In these areas, the impact of an increase in per capita GDP on the expansion of basic educational facilities is relatively modest and may even manifest a negative effect in certain areas. This phenomenon can be attributed to an upgraded focus on educational quality over quantity in these downstream regions as a result of increased per capita GDP. Consequently, an elevation in per capita GDP might lead to a deviation from an increase in the quantity of basic educational facilities in these areas. Moreover, the regression coefficients associated with educational fiscal expenditure also include both positive and negative values, with a concentration of high-value areas in the central region of the Yellow River Basin. An increase in educational fiscal expenditure and its proportion significantly influences the increase in the quantity of basic educational facilities in the region. In summary, the primary influencing factors shaping the spatial pattern of basic educational facilities in the Yellow River Basin include the permanent population and the proportion of tertiary industry. The secondary factors include the per capita GDP and education expenditure.

4. Discussion

4.1. Influencing Factors of Regional Disparities and Policy Recommendations

The findings highlight the key role of the permanent population and the proportion of tertiary industry in shaping the spatial pattern of basic educational facilities in the Yellow River Basin, with per capita GDP and educational fiscal expenditure playing a secondary role. The density of the population is crucially important for the distribution of basic educational public service facilities [16,40]. High-population-density areas typically necessitate more basic educational resources to meet the growing educational demands. In the downstream regions of the Yellow River Basin, the influence of the permanent population on the demand for basic educational public service facilities is relatively lower. Consequently, it is crucial to fine-tune the ratio of the permanent population to basic educational public service facilities, thereby achieving a harmonious development of both the population and educational resources. The proportion of tertiary industry significantly shapes the distribution of basic educational public service facilities, often mirroring the level of economic development and the extent of the service industry within a region [37,38]. The research findings underscore that the downstream areas of the Yellow River Basin exhibit a relatively higher proportion of tertiary industry, while this is lower in the upstream areas. This suggests that elevating the proportion of tertiary industry can exert a positive effect on the quantity of basic educational public service resources in the region. This phenomenon may be attributed to the fact that the development of tertiary industry drives economic growth and regional prosperity, thereby generating additional resources and funding for basic educational public service facilities, which, in turn, can expand their reach. The high-value areas of basic educational public service facilities in the Yellow River Basin tend to align with provincial capital cities, which serve as regional centers. These cities attract more economic, educational, and resource investments, consequently boasting a richer array of basic educational public service facilities. This disparity is indicative of the unevenness in regional economic development, resource distribution, and investment patterns.
The research further reveals that per capita GDP and educational fiscal expenditure assume a secondary role in influencing basic educational public service facilities. An elevation in per capita GDP, signifying improved economic conditions, may stimulate the distribution and provision of basic educational public service resources [79,80]. The regression coefficients for educational fiscal expenditure exhibit both positive and negative values, overall remaining relatively low. The high-value areas of basic educational public service facilities are primarily concentrated in the middle and lower reaches of the Yellow River Basin. A moderate increase in educational fiscal expenditure contributes to the enhancement of the supply and quality of basic educational public service facilities. Consequently, as secondary influencing factors, per capita GDP and educational fiscal inputs exert a certain degree of influence on the quantity and provision of basic educational public service facilities.
The spatial distribution of fundamental educational public service facilities in diverse regions is influenced by various factors. Consequently, it is crucial to attain rational resource utilization and allocation strategies tailored to local circumstances. The optimization of the comprehensive structure of fundamental educational public service resources and their proportion in the total permanent resident population holds primary significance. Through efficacious resource allocation and judicious planning, educational resources can be equitably disseminated across regions, ensuring an equitable provision of fundamental educational public service facilities. This endeavor will contribute to narrowing the educational resource gap among different regions, thereby enhancing the universality and equity of fundamental educational public service facilities. The enhancement of the tertiary sector’s share and per capita GDP levels is important. The development of the tertiary sector and the increase in per capita GDP will lead to higher financial support, thereby strengthening government investment in the field of education [24,39,50]. This, in turn, will bolster the construction and enhancement of educational infrastructure, elevate teacher compensation and professional development, and foster the more effective provisioning of quality educational resources, thereby further enhancing the quality of fundamental educational public service facilities. Throughout this process, it is crucial to moderately increase the allocation of government public financial budget expenditure on education. Augmenting government financial support for education will bolster the coverage and quality of fundamental educational public service facilities, thereby ameliorating educational facility conditions and fortifying the teaching staff. This collective endeavor is poised to contribute significantly to the ongoing optimization and enhancement of the spatial arrangement of fundamental educational public service facilities.

4.2. Advantages and Limitations

This research harnessed the potential of big POI data, including a myriad of geographical location information, such as schools, hospitals, and communities, among others. These data sources provide a comprehensive and complex repository of geographic information, rendering them invaluable for research purposes [81,82,83]. Moreover, their accessibility and cost-effectiveness make them an attractive choice for data collection. Presented in point form, POI data represent diverse geographical location information within urban areas, enhancing research efficiency and accuracy [84,85]. Furthermore, they facilitate visual assessments of the clustering and dispersion of basic educational public service facilities. In this study, a range of established spatial analysis methods, including kernel density analysis, spatial autocorrelation, and GWR, were judiciously applied [75,76,86]. These methodologies are widely recognized and employed in the field of spatial analysis, ensuring the robustness of the research framework. In particular, the utilization of the GWR model enables an in-depth exploration of the spatial connections between basic educational public service facilities and their influencing factors. By conducting localized regressions on spatial data, this approach reveals the spatial nuances and fluctuations of basic educational public services. Consequently, it furnishes more targeted recommendations for policy formulation and strategic planning.
In this study, the per capita resource quantity of basic educational public service facilities in the Yellow River Basin served as the dependent variable, while the permanent population, per capita GDP, proportion of tertiary industry, and educational fiscal expenditure functioned as explanatory variables. One limitation of this study pertains to the relatively limited number of explanatory variables, and future research should contemplate the inclusion of additional variables, such as transportation networks, school-age children, and population mobility. These factors also exert an influence on the spatial configuration of basic educational public service facilities, and their integration into the analysis can enhance the comprehensiveness and precision of the study, enabling a more profound understanding of the influencing factors of spatial patterns and facilitating more tailored resource allocation. The current analysis predominantly emphasized the relationship between the quantity of basic educational public service facilities and their influencing factors, with relatively less attention being dedicated to the assessment of the quality of basic educational public service resources. Aspects such as school size, infrastructure, software, and teaching facilities constitute vital components of educational quality. Future research endeavors should aim to include more quality-related factors, including school size, teacher strength, and the completeness of teaching facilities, to holistically assess the quality of basic educational public service resources. Such comprehensive research would significantly contribute to a more profound understanding of the actual provision of basic educational public service facilities, offering comprehensive recommendations for enhancing education quality and equity.

5. Conclusions

Based on the dataset of basic education POIs, this study employed kernel density analysis and spatial autocorrelation methods within ArcGIS software 10.8 to scrutinize the density and spatial clustering of the basic educational public service pattern within the Yellow River Basin. Furthermore, GWR was utilized to investigate and elucidate the factors contributing to its spatial configuration. The findings can be summarized as follows:
(1)
Regarding the quantity of basic educational facilities in the Yellow River Basin, it is evident that the four downstream provinces exhibit a substantially higher concentration of resources compared with the five upstream provinces. The downstream provinces account for a substantial 82.45% of the basic educational resources. A kernel density analysis of basic education within the Yellow River Basin revealed that areas with high-density basic educational facilities coincide geographically with certain provincial capitals. Notable examples include regions centered around Taiyuan in Shanxi Province, Xi’an in Shaanxi Province, Zhengzhou in Henan Province, and areas surrounding Shandong Province.
(2)
Concerning the spatial clustering of basic educational facilities in the Yellow River Basin, H-H is observed in the western region for preprimary education, while L-L is prevalent in certain eastern and northern areas. Primary and secondary education exhibit H-H in the north, and all three levels manifest spatial associations.
(3)
The primary determinants influencing the spatial distribution of basic educational facilities in the Yellow River Basin are the proportion of tertiary industry and the permanent population. The secondary influencing factors include educational fiscal expenditure and per capita GDP, exerting a relatively lesser influence.
According to the spatial pattern and determinants of basic educational public services in the Yellow River Basin, a series of policy measures should be implemented to coordinate and optimize the structure of basic education service resources. These measures include increasing the proportion of the tertiary sector, moderately raising government public fiscal budget expenditures on education to optimize the spatial distribution of basic education public service facilities in the Yellow River Basin, and promoting balanced development in basic education.
In this study, due to data limitations, the assessment results and the selected explanatory variables with their relation to regional disparities in basic educational public service facilities were limited, and they cannot fully represent the quality and the influencing factors of basic educational public services. With the improvement in basic educational public service evaluation systems and the utilization of multi-source data, future research could integrate a diverse array of factors to fully comprehend the spatial layout and influencing determinants of basic educational public service facilities, thereby facilitating more detailed and exhaustive investigations.

Author Contributions

Conceptualization, Hejie Wei; funding acquisition, Hejie Wei; methodology, Mengxue Liu; visualization, Wenfeng Ji; writing—original draft, Hejie Wei; writing—review and editing, Ling Li and Yi Yang. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0608) and Henan Agricultural University Teaching Reform Project 2023 (2023XJGLX111).

Data Availability Statement

The data presented in this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The location of the Yellow River Basin (A), the distribution of school sites (B), the provinces through which the Yellow River flows (C), and administrative divisions at the prefecture level in the Yellow River Basin (D).
Figure 1. The location of the Yellow River Basin (A), the distribution of school sites (B), the provinces through which the Yellow River flows (C), and administrative divisions at the prefecture level in the Yellow River Basin (D).
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Figure 2. Kernel density analysis of basic educational facilities in the Yellow River Basin, including preprimary education (A), primary education (B), and secondary education (C).
Figure 2. Kernel density analysis of basic educational facilities in the Yellow River Basin, including preprimary education (A), primary education (B), and secondary education (C).
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Figure 3. A distribution map of per capita basic educational facilities in the Yellow River Basin, including preprimary education (A), primary education (B), and secondary education (C).
Figure 3. A distribution map of per capita basic educational facilities in the Yellow River Basin, including preprimary education (A), primary education (B), and secondary education (C).
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Figure 4. Cluster distribution map of per capita basic educational facilities in the Yellow River Basin, including preprimary education (A), primary education (B), and secondary education (C).
Figure 4. Cluster distribution map of per capita basic educational facilities in the Yellow River Basin, including preprimary education (A), primary education (B), and secondary education (C).
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Figure 5. The distribution of the GWR model’s regression coefficients, including GDP per capita (A), permanent population (B), proportion of tertiary industry (C), and education expenditure (D).
Figure 5. The distribution of the GWR model’s regression coefficients, including GDP per capita (A), permanent population (B), proportion of tertiary industry (C), and education expenditure (D).
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Table 1. Results of comparison between OLS and GWR.
Table 1. Results of comparison between OLS and GWR.
AICc R2Adjusted R2
OLS1006.980.9140.909
GWR998.370.9390.925
Table 2. The global Moran’s I index of per capita basic educational facilities in the Yellow River Basin.
Table 2. The global Moran’s I index of per capita basic educational facilities in the Yellow River Basin.
ValuePreprimary EducationPrimary EducationSecondary Education
Moran’s I0.2580.1900.085
Expected index−0.014−0.014−0.014
Variance0.0020.0060.006
Z score6.3912.7061.334
p value0.0000.0070.182
Table 3. Statistics and VIF of GWR model’s regression coefficients.
Table 3. Statistics and VIF of GWR model’s regression coefficients.
VariablesMinimum ValueMedian ValueMaximum ValueAverage ValueStandard DeviationVIF
Per Capita GDP−0.136−0.06780.000321−0.001820.00033881.005001
Permanent Population2.5273.0383.5492.8880.19981.922066
Proportion of Tertiary Industry−2.6645.953514.5715.5383.88751.327498
Educational Fiscal Expenditure−1.4590.3922.2430.4451.36971.327498
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Wei, H.; Ji, W.; Li, L.; Yang, Y.; Liu, M. Exploring the Equality and Determinants of Basic Educational Public Services from a Spatial Variation Perspective Using POI Data. ISPRS Int. J. Geo-Inf. 2025, 14, 66. https://doi.org/10.3390/ijgi14020066

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Wei H, Ji W, Li L, Yang Y, Liu M. Exploring the Equality and Determinants of Basic Educational Public Services from a Spatial Variation Perspective Using POI Data. ISPRS International Journal of Geo-Information. 2025; 14(2):66. https://doi.org/10.3390/ijgi14020066

Chicago/Turabian Style

Wei, Hejie, Wenfeng Ji, Ling Li, Yi Yang, and Mengxue Liu. 2025. "Exploring the Equality and Determinants of Basic Educational Public Services from a Spatial Variation Perspective Using POI Data" ISPRS International Journal of Geo-Information 14, no. 2: 66. https://doi.org/10.3390/ijgi14020066

APA Style

Wei, H., Ji, W., Li, L., Yang, Y., & Liu, M. (2025). Exploring the Equality and Determinants of Basic Educational Public Services from a Spatial Variation Perspective Using POI Data. ISPRS International Journal of Geo-Information, 14(2), 66. https://doi.org/10.3390/ijgi14020066

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