2021
DOI: 10.3390/land10101059
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Spatial Distribution Equilibrium and Relationship between Construction Land Expansion and Basic Education Schools in Shanghai Based on POI Data

Abstract: Basic education is about improving the quality of life of a country’s population and promote social cohesions, and it is also an important factor in shaping a country and region’s person-to-person relationship. This study analyzes the spatial morphological patterns, aggregation characteristics, and distribution inequality among kindergarten, elementary, and junior high schools within districts in Shanghai, using point of interest data, kernel density estimation, Ripley’s K-function, location quotient, and grid… Show more

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Cited by 15 publications
(12 citation statements)
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“…Holistically, PSVs in Shanghai spread outwards from the kernel areas and their numbers exhibit a "cliff fall" instead of a steady decrease; the scope of the transitional area is relatively small; and PSVs are also distributed unevenly across different suburban districts, and small kernel areas have formed near the administrative centers of suburban districts; the density of PSVs in the suburban kernel and peripheral areas is significantly larger than remote areas. In their study of spatial distribution of kindergartens, primary and middle schools in Shanghai, Zhenchao et al found that suburban districts exhibit a multi-kernel aggregation and uneven distribution [28], which is consistent with the findings of this research. The mean centers of the spatial distribution of PSVs in Shanghai are located in the overlapping areas of the seven central districts, and PSV distributional densities are extremely high in central districts, forming a large kernel area; comparatively, the distributional densities of PSVs are distinctly smaller.…”
Section: Analysis Of the Spatial Distribution Of Psvssupporting
confidence: 86%
“…Holistically, PSVs in Shanghai spread outwards from the kernel areas and their numbers exhibit a "cliff fall" instead of a steady decrease; the scope of the transitional area is relatively small; and PSVs are also distributed unevenly across different suburban districts, and small kernel areas have formed near the administrative centers of suburban districts; the density of PSVs in the suburban kernel and peripheral areas is significantly larger than remote areas. In their study of spatial distribution of kindergartens, primary and middle schools in Shanghai, Zhenchao et al found that suburban districts exhibit a multi-kernel aggregation and uneven distribution [28], which is consistent with the findings of this research. The mean centers of the spatial distribution of PSVs in Shanghai are located in the overlapping areas of the seven central districts, and PSV distributional densities are extremely high in central districts, forming a large kernel area; comparatively, the distributional densities of PSVs are distinctly smaller.…”
Section: Analysis Of the Spatial Distribution Of Psvssupporting
confidence: 86%
“…Mean nearest neighbor analysis is a method to calculate the average of the nearest distance between point elements. It also measures the degree of agglomeration and dispersion of data at different points 54 . This paper adopts it to detect whether the producer services are A is the city, d i is the average distance between each urban element and its proximity, and n is the sample size.…”
Section: Methodsmentioning
confidence: 99%
“…Lee and Xia demonstrated both a significant positive autocorrelation and local mismatch between spatial density and population density, where the local mismatch is related to the type of land use, urban location, and time period [4,5]. Zhang demonstrated that the agglomeration scale and educational spatial morphology intensity affect population density distribution [48]. Zhao analysed population distribution characteristics in Shenyang and found an unbalanced distribution of population density in cities and further found that it was closely related to the distribution of infrastructure and functional diversity around the neighbourhoods [49].…”
Section: Main Influencing Factors Of Population Density Distribution ...mentioning
confidence: 99%