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Spatially Analyzing the Inequity of the Hong Kong Urban Heat Island by Socio-Demographic Characteristics

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  • Man Sing Wong

    (Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
    Joint Spatial Information Research Laboratory between The Hong Kong Polytechnic University and Wuhan University, Wuhan 430072, China)

  • Fen Peng

    (Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China)

  • Bin Zou

    (School of Geosciences and Info-Physics, Central South University, Changsha 410083, China)

  • Wen Zhong Shi

    (Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
    Joint Spatial Information Research Laboratory between The Hong Kong Polytechnic University and Wuhan University, Wuhan 430072, China)

  • Gaines J. Wilson

    (Department of Biological Sciences, Huston-Tillotson University, Austin, TX 78702, USA)

Abstract

Recent studies have suggested that some disadvantaged socio-demographic groups face serious environmental-related inequities in Hong Kong due to the rising ambient urban temperatures. Identifying heat-vulnerable groups and locating areas of Surface Urban Heat Island (SUHI) inequities is thus important for prioritizing interventions to mitigate death/illness rates from heat. This study addresses this problem by integrating methods of remote sensing retrieval, logistic regression modelling, and spatial autocorrelation. In this process, the SUHI effect was first estimated from the Land Surface Temperature (LST) derived from a Landsat image. With the scale assimilated to the SUHI and socio-demographic data, a logistic regression model was consequently adopted to ascertain their relationships based on Hong Kong Tertiary Planning Units (TPUs). Lastly, inequity “hotspots” were derived using spatial autocorrelation methods. Results show that disadvantaged socio-demographic groups were significantly more prone to be exposed to an intense SUHI effect: over half of 287 TPUs characterized by age groups of 60+ years, secondary and matriculation education attainment, widowed, divorced and separated, low and middle incomes, and certain occupation groups of workers, have significant Odds Ratios (ORs) larger than 1.2. It can be concluded that a clustering analysis stratified by age, income, educational attainment, marital status, and occupation is an effective way to detect the inequity hotspots of SUHI exposure. Additionally, inequities explored using income, marital status and occupation factors were more significant than the age and educational attainment in these areas. The derived maps and model can be further analyzed in urban/city planning, in order to mitigate the physical and social causes of the SUHI effect.

Suggested Citation

  • Man Sing Wong & Fen Peng & Bin Zou & Wen Zhong Shi & Gaines J. Wilson, 2016. "Spatially Analyzing the Inequity of the Hong Kong Urban Heat Island by Socio-Demographic Characteristics," IJERPH, MDPI, vol. 13(3), pages 1-17, March.
  • Handle: RePEc:gam:jijerp:v:13:y:2016:i:3:p:317-:d:65666
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    References listed on IDEAS

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    1. Bin Zou & Fen Peng & Neng Wan & Keita Mamady & Gaines J Wilson, 2014. "Spatial Cluster Detection of Air Pollution Exposure Inequities across the United States," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-14, March.
    2. Harlan, Sharon L. & Brazel, Anthony J. & Prashad, Lela & Stefanov, William L. & Larsen, Larissa, 2006. "Neighborhood microclimates and vulnerability to heat stress," Social Science & Medicine, Elsevier, vol. 63(11), pages 2847-2863, December.
    3. Helen Berry & Kathryn Bowen & Tord Kjellstrom, 2010. "Climate change and mental health: a causal pathways framework," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 55(2), pages 123-132, April.
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    2. Justin D Stewart & Peleg Kremer, 2022. "Temporal change in relationships between urban structure and surface temperature," Environment and Planning B, , vol. 49(9), pages 2297-2311, November.
    3. John Dialesandro & Noli Brazil & Stephen Wheeler & Yaser Abunnasr, 2021. "Dimensions of Thermal Inequity: Neighborhood Social Demographics and Urban Heat in the Southwestern U.S," IJERPH, MDPI, vol. 18(3), pages 1-15, January.
    4. Xinping Zhang & Dexiang Wang & Hongke Hao & Fangfang Zhang & Youning Hu, 2017. "Effects of Land Use/Cover Changes and Urban Forest Configuration on Urban Heat Islands in a Loess Hilly Region: Case Study Based on Yan’an City, China," IJERPH, MDPI, vol. 14(8), pages 1-25, July.

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