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author:

Li, Mingxiao (Li, Mingxiao.) [1] | Tu, Wei (Tu, Wei.) [2] | Lu, Feng (Lu, Feng.) [3]

Indexed by:

EI

Abstract:

Sensing the nighttime economy–housing imbalance is of great importance for urban planning and commerce. As an efficient tool of social sensing and human observation, mobile phone data provides an effective way to address this issue. In this paper, an indicator, mobile phone data-based nighttime economy–housing imbalance intensity, is proposed to measure the degree of the nighttime economy–housing imbalance. This indicator can distinguish vitality variations between sleep periods and nighttime activity periods, which are highly related to the nighttime economy–housing imbalance. The spatial pattern of the nighttime economy–housing imbalance was explored, and its association with the built environment was investigated through city-scale geographical regression analysis in Shanghai, China. The results showed that the sub-districts of Shanghai with high-positive-imbalance intensities displayed structures with superimposed rings and striped shapes, and the sub-districts with negative imbalance intensities were distributed around high positive-intensity areas. There were significant linear correlations between imbalance intensity and the built environment. The multiple influences of built environment factors and related mechanisms were explored from a geographical perspective. Our study utilized the social sensing data to provide a more comprehensive understanding of the nighttime economy–housing imbalance. These findings will be useful for fostering the nighttime economy and supporting urban renewal. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Keyword:

Cellular telephones Housing Regression analysis Urban planning

Community:

  • [ 1 ] [Li, Mingxiao]Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, Research Institute of Smart Cities, School of Architecture & Urban Planning, Shenzhen University, Shenzhen; 518060, China
  • [ 2 ] [Li, Mingxiao]State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing; 100101, China
  • [ 3 ] [Tu, Wei]Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, Research Institute of Smart Cities, School of Architecture & Urban Planning, Shenzhen University, Shenzhen; 518060, China
  • [ 4 ] [Lu, Feng]State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing; 100101, China
  • [ 5 ] [Lu, Feng]University of Chinese Academy of Sciences, Beijing; 100049, China
  • [ 6 ] [Lu, Feng]The Academy of Digital China, Fuzhou University, Fuzhou; 350002, China

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Source :

Remote Sensing

Year: 2022

Issue: 12

Volume: 14

5 . 0

JCR@2022

4 . 2 0 0

JCR@2023

ESI HC Threshold:51

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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