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

Yanbi Chen, Zilong Zhang, Lixia Lang, Zhi Long, Ningfei Wang, Xingpeng Chen (), Bo Wang and Ya Li
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Yanbi Chen: College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
Zilong Zhang: College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
Lixia Lang: College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
Zhi Long: College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
Ningfei Wang: College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
Xingpeng Chen: College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
Bo Wang: CAUPD Beijing Planning and Design Consultants Ltd. (Northwest Branch Office), Lanzhou 730000, China
Ya Li: College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China

Land, 2023, vol. 12, issue 8, 1-17

Abstract: 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.

Keywords: spatial matching; urban service facilities; spatiotemporal distribution; facility distribution (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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