TY - JOUR
T1 - Research on the Spatial Distribution Characteristics and Influencing Factors of Educational Facilities Based on POI Data
T2 - A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area
AU - Chen, Bowen
AU - Zhang, Hongfeng
AU - Wong, Cora Un In
AU - Chen, Xiaolong
AU - Li, Fanbo
AU - Wei, Xiaoyu
AU - Shen, Junxian
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/7
Y1 - 2024/7
N2 - This study aims to provide a precise assessment of the distribution of educational facilities within the Guangdong–Hong Kong–Macao Greater Bay Area, serving as a crucial foundation for managing educational resource allocation and enhancing the quality of educational services. Utilizing a kernel density analysis, global autocorrelation analysis, and geographic detectors, this research systematically analyzes the spatial distribution characteristics and influencing factors of educational facilities in the area. The findings reveal significant geographical disparities in facility distribution with dense clusters in urban centers such as Guangzhou and Shenzhen, and less dense distributions in peripheral areas like Zhongshan and Macau. These facilities exhibit a multi-center cluster pattern with strong spatial autocorrelation, mainly influenced by the population size and economic and urban development levels. The results provide actionable insights for refining educational planning and resource allocation, contributing to the enhancement of educational quality across diverse urban landscapes.
AB - This study aims to provide a precise assessment of the distribution of educational facilities within the Guangdong–Hong Kong–Macao Greater Bay Area, serving as a crucial foundation for managing educational resource allocation and enhancing the quality of educational services. Utilizing a kernel density analysis, global autocorrelation analysis, and geographic detectors, this research systematically analyzes the spatial distribution characteristics and influencing factors of educational facilities in the area. The findings reveal significant geographical disparities in facility distribution with dense clusters in urban centers such as Guangzhou and Shenzhen, and less dense distributions in peripheral areas like Zhongshan and Macau. These facilities exhibit a multi-center cluster pattern with strong spatial autocorrelation, mainly influenced by the population size and economic and urban development levels. The results provide actionable insights for refining educational planning and resource allocation, contributing to the enhancement of educational quality across diverse urban landscapes.
KW - development disparities
KW - educational facilities
KW - influencing factors
KW - infrastructure management
KW - regional imbalance
KW - spatial analysis
UR - http://www.scopus.com/inward/record.url?scp=85199639228&partnerID=8YFLogxK
U2 - 10.3390/ijgi13070225
DO - 10.3390/ijgi13070225
M3 - Article
AN - SCOPUS:85199639228
SN - 2220-9964
VL - 13
JO - ISPRS International Journal of Geo-Information
JF - ISPRS International Journal of Geo-Information
IS - 7
M1 - 225
ER -