Measuring segregation between rural migrants and local residents in urban China: an integrated spatio-social network analysis of Kecun in Guangzhou

Miaoxi Zhao, Yiming Wang*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

21 Citations (Scopus)
626 Downloads (Pure)

Abstract

In this paper, we demonstrate an integrated spatio-social network analysis to measure the degree of segregation within a Chinese urban neighborhood in terms of the everyday activities by rural migrants and local urban residents at such routine venues as restaurants, grocery stores, barber shops, etc. Our data were collected in May 2014, through an integrated geographical and social survey conducted within an inner-city neighborhood around Kecun in south China’s Guangzhou Municipality. Although Kecun features a highly condensed and mixed dwelling pattern between the rural migrants and indigenous urban population, we find that within our sample of 110 local and 132 migrant residents, the former tend to socialize more inwardly with their peer locals and visit neighborhood amenities more often such as pubs, stadia, and public kindergartens. In contrast, the migrants tend to attend local roadside food stalls, outdoor recreation facilities, small clinic shops, grocery malls, and private (minban) kindergartens more often. Overall, only a modest degree of social interaction between the locals and migrants appears to exist in Kecun. On top of the methodological implications of our study, we argue that urban segregation in China is both socially and spatially different from its Anglo-American counterpart. More empirical research is needed to understand and assess social segregation underlying the everyday urban life in Chinese cities.

Original languageEnglish
Pages (from-to)417-433
Number of pages17
JournalEnvironment and Planning B: Urban Analytics and City Science
Volume45
Issue number3
Early online date1 Jun 2017
DOIs
Publication statusPublished - May 2018

Keywords

  • Segregation
  • Spatio-social Network Analysis
  • Migrants
  • China

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