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作者:

Wang, Jing (Wang, Jing.) | Zhou, Chenjing (Zhou, Chenjing.) | Rong, Jian (Rong, Jian.) | Liu, Siyang (Liu, Siyang.) | Wang, Yi (Wang, Yi.)

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摘要:

As a key path to sustainable urban development, the jobs-housing balance is crucial in addressing the issues of large commuting distances, congestion, and environmental pollution. Numerous studies have assessed jobshousing relationships for different geographical units, leading to controversial findings that confuse planners and policymakers. The aim of this study is to develop a mobility-based framework to determine geographical units for assessing bilateral jobs-housing balance, i.e., work-based self-containment (WSC) and home-based selfcontainment (HSC). The commuting connection intensity index is proposed based on the Gravity model to reflect the actual commuting relationship among regions using the cell phone signaling data of China Unicom in September 2021. The community detection algorithm is then applied to determine geographic units for the assessment. Evidence from Beijing shows an inconsistency between WSC and HSC in the 39 identified geographic units that are not subjected to administrative boundaries. In addition, lower WSC and HSC values in Beijing indicate a mismatch between jobs and housing throughout the city, particularly in the urban center. The findings provide a geographical recognition method for assessing the jobs-housing balance and assist in understanding bilateral jobs-housing relationships from the perspectives of jobs and housing.

关键词:

Jobs-housing balance Community detection Cell phone signaling data Gravity model Self -containment

作者机构:

  • [ 1 ] [Wang, Jing]Beijing Univ Technol, Beijing Engn Res Ctr, Urban Transport Operat Guarantee,100 Pingleyuan, Beijing 100124, Peoples R China
  • [ 2 ] [Zhou, Chenjing]Guangzhou Univ, 230 West Waihuan Rd, Guangzhou 510006, Peoples R China
  • [ 3 ] [Rong, Jian]Guangzhou Univ, 230 West Waihuan Rd, Guangzhou 510006, Peoples R China
  • [ 4 ] [Wang, Yi]Guangzhou Univ, 230 West Waihuan Rd, Guangzhou 510006, Peoples R China
  • [ 5 ] [Liu, Siyang]Beijing Univ Technol, Beijing Key Lab Traff Engn, 100 Pingleyuan, Beijing 100124, Peoples R China

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来源 :

SUSTAINABLE CITIES AND SOCIETY

ISSN: 2210-6707

年份: 2022

卷: 87

1 1 . 7

JCR@2022

1 1 . 7 0 0

JCR@2022

JCR分区:1

中科院分区:1

被引次数:

WoS核心集被引频次: 12

SCOPUS被引频次: 16

ESI高被引论文在榜: 0 展开所有

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