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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8656856 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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