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author:

Yang, Xiping (Yang, Xiping.) [1] | Zhao, Zhiyuan (Zhao, Zhiyuan.) [2] | Shi, Chaoyang (Shi, Chaoyang.) [3] | Luo, Lin (Luo, Lin.) [4] | Tu, Wei (Tu, Wei.) [5]

Indexed by:

EI

Abstract:

The interaction between the population and built environment is a constant topic in urban spaces and is the main driving force of urban evolution. Understanding urban population distribution and its relationship with the built environment could provide guidance for urban planning, traffic, and disaster management. Following this line of thought, this study conducted an empirical analysis in Xi’an, a rapidly developing western city in China. Well-permeated mobile phone location data were used to represent the spatiotemporal dynamics of the population, and the built environment was characterized from five perspectives—transportation, location, building, greenery, and land use—using multisource geospatial data. Finally, the dynamic heterogeneous influence of built environment factors on population distribution was examined using multiscale geographically weighted regression (MGWR). Overall, the influencing coefficients exhibited a significant dynamic changing process from a temporal perspective and simultaneously demonstrated spatial nonstationarity. Moreover, the specific findings about the influence of each built environment factor facilitate a deeper insight into dynamic population distribution and its determinants. © 2023 by the authors.

Keyword:

Disaster prevention Disasters Land use Population distribution Population dynamics Population statistics Urban planning

Community:

  • [ 1 ] [Yang, Xiping]Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen; 518000, China
  • [ 2 ] [Yang, Xiping]School of Geography and Tourism, Shaanxi Normal University, Xi’an; 710119, China
  • [ 3 ] [Zhao, Zhiyuan]Academy of Digital China (Fujian), Fuzhou University, Fuzhou; 350003, China
  • [ 4 ] [Shi, Chaoyang]School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan; 430074, China
  • [ 5 ] [Luo, Lin]School of Geography and Tourism, Shaanxi Normal University, Xi’an; 710119, China
  • [ 6 ] [Tu, Wei]Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, Research Institute of Smart Cities, Shenzhen University, Shenzhen; 518060, China
  • [ 7 ] [Tu, Wei]Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University, Shenzhen; 518060, China

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Source :

Remote Sensing

Year: 2023

Issue: 9

Volume: 15

4 . 2

JCR@2023

4 . 2 0 0

JCR@2023

ESI HC Threshold:26

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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