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Clustering of Socioeconomic Data in Hong Kong for Planning Better Community Health Protection

The concept of socioeconomic vulnerability has made a substantial contribution to the understanding and conceptualization of health risk. To assess the spatial distribution of multi-dimensional socioeconomic vulnerability in an urban context, a vulnerability assessment scheme was proposed to guide d...

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Autores principales: Huang, Zhe, Chan, Emily Ying Yang, Wong, Chi Shing, Zee, Benny Chung Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8656856/
https://www.ncbi.nlm.nih.gov/pubmed/34886341
http://dx.doi.org/10.3390/ijerph182312617
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author Huang, Zhe
Chan, Emily Ying Yang
Wong, Chi Shing
Zee, Benny Chung Ying
author_facet Huang, Zhe
Chan, Emily Ying Yang
Wong, Chi Shing
Zee, Benny Chung Ying
author_sort Huang, Zhe
collection PubMed
description The concept of socioeconomic vulnerability has made a substantial contribution to the understanding and conceptualization of health risk. To assess the spatial distribution of multi-dimensional socioeconomic vulnerability in an urban context, a vulnerability assessment scheme was proposed to guide decision-making in disaster resilience and sustainable urban development to reduce health risk. A two-stage approach was applied in Hong Kong to identify subgroups among Tertiary Planning Units (TPU) (i.e., the local geographic areas) with similar characteristics. In stage 1, principal components analysis was used for dimension reduction and to de-noise the socioeconomic data for each TPU based on the variables selected, while in stage 2, Gaussian mixture modeling was used to partition all the TPUs into different subgroups based on the results of stage 1. This study summarized socioeconomic-vulnerability-related data into five principal components, including indigenous degree, family resilience, individual productivity, populous grassroots, and young-age. According to these five principal components, all TPUs were clustered into five subgroups/clusters. Socioeconomic vulnerability is a concept that could be used to help identify areas susceptible to health risk, and even identify susceptible groups in affluent areas. More attention should be paid to areas with high populous grassroots scores and low young-age score since they were associated with a higher mortality rate.
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spelling pubmed-86568562021-12-10 Clustering of Socioeconomic Data in Hong Kong for Planning Better Community Health Protection Huang, Zhe Chan, Emily Ying Yang Wong, Chi Shing Zee, Benny Chung Ying Int J Environ Res Public Health Article The concept of socioeconomic vulnerability has made a substantial contribution to the understanding and conceptualization of health risk. To assess the spatial distribution of multi-dimensional socioeconomic vulnerability in an urban context, a vulnerability assessment scheme was proposed to guide decision-making in disaster resilience and sustainable urban development to reduce health risk. A two-stage approach was applied in Hong Kong to identify subgroups among Tertiary Planning Units (TPU) (i.e., the local geographic areas) with similar characteristics. In stage 1, principal components analysis was used for dimension reduction and to de-noise the socioeconomic data for each TPU based on the variables selected, while in stage 2, Gaussian mixture modeling was used to partition all the TPUs into different subgroups based on the results of stage 1. This study summarized socioeconomic-vulnerability-related data into five principal components, including indigenous degree, family resilience, individual productivity, populous grassroots, and young-age. According to these five principal components, all TPUs were clustered into five subgroups/clusters. Socioeconomic vulnerability is a concept that could be used to help identify areas susceptible to health risk, and even identify susceptible groups in affluent areas. More attention should be paid to areas with high populous grassroots scores and low young-age score since they were associated with a higher mortality rate. MDPI 2021-11-30 /pmc/articles/PMC8656856/ /pubmed/34886341 http://dx.doi.org/10.3390/ijerph182312617 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Zhe
Chan, Emily Ying Yang
Wong, Chi Shing
Zee, Benny Chung Ying
Clustering of Socioeconomic Data in Hong Kong for Planning Better Community Health Protection
title Clustering of Socioeconomic Data in Hong Kong for Planning Better Community Health Protection
title_full Clustering of Socioeconomic Data in Hong Kong for Planning Better Community Health Protection
title_fullStr Clustering of Socioeconomic Data in Hong Kong for Planning Better Community Health Protection
title_full_unstemmed Clustering of Socioeconomic Data in Hong Kong for Planning Better Community Health Protection
title_short Clustering of Socioeconomic Data in Hong Kong for Planning Better Community Health Protection
title_sort clustering of socioeconomic data in hong kong for planning better community health protection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8656856/
https://www.ncbi.nlm.nih.gov/pubmed/34886341
http://dx.doi.org/10.3390/ijerph182312617
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