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Poverty concentration in an affluent city: Geographic variation and correlates of neighborhood poverty rates in Hong Kong

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  • Yingqi Guo
  • Shu-Sen Chang
  • Feng Sha
  • Paul S F Yip

Abstract

Previous investigations of geographic concentration of urban poverty indicate the contribution of a variety of factors, such as economic restructuring and class-based segregation, racial segregation, demographic structure, and public policy. However, the models used by most past research do not consider the possibility that poverty concentration may take different forms in different locations across a city, and most studies have been conducted in Western settings. We investigated the spatial patterning of neighborhood poverty and its correlates in Hong Kong, which is amongst cities with the highest GDP in the region, using the city-wide ordinary least square (OLS) regression model and the local-specific geographically weighted regression (GWR) model. We found substantial geographic variations in small-area poverty rates and identified several poverty clusters in the territory. Factors found to contribute to urban poverty in Western cities, such as socioeconomic factors, ethnicity, and public housing, were also mostly associated with local poverty rates in Hong Kong. Our results also suggest some heterogeneity in the associations of poverty with specific correlates (e.g. access to hospitals) that would be masked in the city-wide OLS model. Policy aimed to alleviate poverty should consider both city-wide and local-specific factors.

Suggested Citation

  • Yingqi Guo & Shu-Sen Chang & Feng Sha & Paul S F Yip, 2018. "Poverty concentration in an affluent city: Geographic variation and correlates of neighborhood poverty rates in Hong Kong," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-17, February.
    • Handle: RePEc:plo:pone00:0190566
    DOI: 10.1371/journal.pone.0190566
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    Cited by:

    1. Scott William Hegerty, 2023. "Defining ‘metropolitan’ poverty: Isolation gradients in major US urban areas," Urban Studies, Urban Studies Journal Limited, vol. 60(10), pages 1796-1814, August.
    2. Yanyan Chen & Hanqiang Qian & Yang Wang, 2020. "Analysis of Beijing’s Working Population Based on Geographically Weighted Regression Model," Sustainability, MDPI, vol. 12(12), pages 1-16, June.
    3. Xindong He & Xianmin Mai & Guoqiang Shen, 2020. "Poverty and Physical Geographic Factors: An Empirical Analysis of Sichuan Province Using the GWR Model," Sustainability, MDPI, vol. 13(1), pages 1-17, December.

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