Measuring the Spatial Match between Service Facilities and Population Distribution: Case of Lanzhou

With rapid urbanization and population growth, achieving equitable distribution of urban facilities in the city center has become a critical research focus due to limited land space and high population density. In this study, we propose a technical method to measure the spatial matching between urba...

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Bibliographic Details
Published inLand (Basel) Vol. 12; no. 8; p. 1549
Main Authors Chen, Yanbi, Zhang, Zilong, Lang, Lixia, Long, Zhi, Wang, Ningfei, Chen, Xingpeng, Wang, Bo, Li, Ya
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2023
Subjects
Activity patterns
Analysis
Big Data
City centres
Decision making
Educational services industry
facility distribution
Heterogeneity
Holidays & special occasions
Matching
Measurement
Population density
Population distribution
Population growth
Population studies
Real estate
Regional planning
Research methodology
Scientific visualization
Service facilities
Spatial analysis
Spatial heterogeneity
spatial matching
spatiotemporal distribution
Supply & demand
Urban areas
Urban development
Urban planning
urban service facilities
Urbanization
China
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Summary:With rapid urbanization and population growth, achieving equitable distribution of urban facilities in the city center has become a critical research focus due to limited land space and high population density. In this study, we propose a technical method to measure the spatial matching between urban service facilities and population at the grid resolution scale, using Baidu heat map and POI data. The method includes spatial heterogeneity analysis and spatial matching analysis between population density and service facilities. We apply the method to the main urban area of Lanzhou, a valley-type city in the upper reaches of the Yellow River, and measure the spatial matching between service facilities and population aggregation. Our results reveal the distribution characteristics of various service facilities and population aggregation in different time slots, and demonstrate that transportation facilities have the highest spatial matching with population aggregation, followed by real estate and education services, with rental business services exhibiting the lowest. The proposed method offers a new perspective for urban planners and decision-makers to understand the matching state between residents’ activity patterns and service facilities. Our findings can provide theoretical support for urban planning and optimize the layout of service facilities and regional function allocation.
ISSN:2073-445X
2073-445X
DOI:10.3390/land12081549