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Socio-Spatial Variations in Commuting Patterns in Suburban Beijing

Autor(en):


Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Built Environment, , n. 4, v. 45
Seite(n): 523-543
DOI: 10.2148/benv.45.4.523
Abstrakt:

With rapid urbanization and suburbanization in China, there is clear evidence of the decoupling of home-work locations in cities which is in contrast to the socialist danwei system where workers were housed in workplace compounds. This paper examines the diverse commuting patterns of suburban neighbourhoods in the Beijing metropolitan region. The research first examines the relationship between the characteristics of commutes in terms of time, distance, and mode, and the socio-economic attributes of residents. The analysis allows us to examine how different socio-economic groups, via latent class analysis, are often spatially concentrated in marginalized neighbourhoods, and further disadvantaged in their commuting experience. The socio-spatial variations in commuting patterns are analysed via GIS mapping analysis, statistical testing, and multiple regression analysis. Major variations were found in the commuting patterns in terms of time, distance and mode across different socioeconomic groups and across various suburban neighbourhood types. The results from regression models further suggest that personal resources have the strongest influence on commuting time but less so on distance, regardless of the type of neighbourhoods in which they live. The findings call for integrative planning and major transport measures, at different spatial scales, to shape commuting behaviour. Despite the unique institutional and cultural context of China, the lessons learnt from the need to have integrative strategic planning are relevant to other cities, and especially those in the developing world which are undergoing rapid urbanization.

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.2148/benv.45.4.523.
  • Über diese
    Datenseite
  • Reference-ID
    10396143
  • Veröffentlicht am:
    05.12.2019
  • Geändert am:
    05.12.2019
 
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