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Int. J. Environ. Res. Public Health 2016, 13(3), 317; doi:10.3390/ijerph13030317

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

1
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
2
Joint Spatial Information Research Laboratory between The Hong Kong Polytechnic University and Wuhan University, Wuhan 430072, China
3
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
4
Department of Biological Sciences, Huston-Tillotson University, Austin, TX 78702, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Yu-Pin Lin
Received: 2 February 2016 / Revised: 7 March 2016 / Accepted: 8 March 2016 / Published: 12 March 2016
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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. View Full-Text
Keywords: environmental inequity; land surface temperature; socio-demographic characteristic; spatial autocorrelation; urban heat island environmental inequity; land surface temperature; socio-demographic characteristic; spatial autocorrelation; urban heat island
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Wong, M.S.; Peng, F.; Zou, B.; Shi, W.Z.; Wilson, G.J. Spatially Analyzing the Inequity of the Hong Kong Urban Heat Island by Socio-Demographic Characteristics. Int. J. Environ. Res. Public Health 2016, 13, 317.

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