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

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 importa...

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Autores principales: Wong, Man Sing, Peng, Fen, Zou, Bin, Shi, Wen Zhong, Wilson, Gaines J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4808980/
https://www.ncbi.nlm.nih.gov/pubmed/26985899
http://dx.doi.org/10.3390/ijerph13030317
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author Wong, Man Sing
Peng, Fen
Zou, Bin
Shi, Wen Zhong
Wilson, Gaines J.
author_facet Wong, Man Sing
Peng, Fen
Zou, Bin
Shi, Wen Zhong
Wilson, Gaines J.
author_sort Wong, Man Sing
collection PubMed
description 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.
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spelling pubmed-48089802016-04-04 Spatially Analyzing the Inequity of the Hong Kong Urban Heat Island by Socio-Demographic Characteristics Wong, Man Sing Peng, Fen Zou, Bin Shi, Wen Zhong Wilson, Gaines J. Int J Environ Res Public Health Article 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. MDPI 2016-03-12 2016-03 /pmc/articles/PMC4808980/ /pubmed/26985899 http://dx.doi.org/10.3390/ijerph13030317 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wong, Man Sing
Peng, Fen
Zou, Bin
Shi, Wen Zhong
Wilson, Gaines J.
Spatially Analyzing the Inequity of the Hong Kong Urban Heat Island by Socio-Demographic Characteristics
title Spatially Analyzing the Inequity of the Hong Kong Urban Heat Island by Socio-Demographic Characteristics
title_full Spatially Analyzing the Inequity of the Hong Kong Urban Heat Island by Socio-Demographic Characteristics
title_fullStr Spatially Analyzing the Inequity of the Hong Kong Urban Heat Island by Socio-Demographic Characteristics
title_full_unstemmed Spatially Analyzing the Inequity of the Hong Kong Urban Heat Island by Socio-Demographic Characteristics
title_short Spatially Analyzing the Inequity of the Hong Kong Urban Heat Island by Socio-Demographic Characteristics
title_sort spatially analyzing the inequity of the hong kong urban heat island by socio-demographic characteristics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4808980/
https://www.ncbi.nlm.nih.gov/pubmed/26985899
http://dx.doi.org/10.3390/ijerph13030317
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