Supply–Demand Imbalance in School Land: An Eigenvector Spatial Filtering Approach

• Published in: Sustainability Vol. 15; no. 17; p. 12935
• Main Authors: Sun, Wenwen, Murakami, Daisuke, Hu, Xin, Li, Zhuoran, Kidd, Akari Nakai, Liu, Chunlu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Age; Analysis; Eigenvectors; Equipment and supplies; Fixed assets; Geospatial data; Green buildings; Households; Methods; Migrant workers; Migration; Public services; Quality of education; Quality of service; Registration; Rural areas; Schools; Standard of living; Sustainability; Sustainable development; Teaching; China
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su151712935

The spatial flows of school-age children and educational resources have been driven by such factors as regional differences in population migration and the uneven development of the education quality and living standards of residents in urban and rural areas. This phenomenon further leads to a supply–demand imbalance between the area of school land and the number of school-age children in the geographical location of China. The georeferenced data characterizing supply–demand imbalance presents an obvious spatial autocorrelation. Therefore, a spatial data analysis technique named the Eigenvector Spatial Filtering (ESF) approach was employed to identify the driving factors of the supply–demand imbalance of school land. The eigenvectors generated by the geographical coordinates of all primary schools were selected and added into the ESF model to filter the spatial autocorrelation of the datasets to identify the driving factors of the supply–demand imbalance. To verify the performance of the technique, it was applied to a county in the southwest of Shandong Province, China. The results from this study showed that all the georeferenced indicators representing population migration and education quality were statistically significant, but no indicator of the living standards of residents showed statistical significance. The eigenvector spatial filtering approach can effectively filter out the positive spatial autocorrelation of the datasets. The findings of this research suggest that a sustainable school-land-allocation scheme should consider population migration and the possible preference for high-quality education.