Cargando…
Spatial pattern of the population casualty rate caused by super typhoon Lekima and quantification of the interactive effects of potential impact factors
BACKGROUND: Typhoons greatly threaten human life and property, especially in China. Therefore, it is important to make effective policy decisions to minimize losses associated with typhoons. METHODS: In this study, the GeoDetector method was used to quantify the determinant powers of natural and soc...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8244144/ https://www.ncbi.nlm.nih.gov/pubmed/34187432 http://dx.doi.org/10.1186/s12889-021-11281-y |
_version_ | 1783715874457255936 |
---|---|
author | Zhang, Xiangxue Nie, Juan Cheng, Changxiu Xu, Chengdong Xu, Xiaojun Yan, Bin |
author_facet | Zhang, Xiangxue Nie, Juan Cheng, Changxiu Xu, Chengdong Xu, Xiaojun Yan, Bin |
author_sort | Zhang, Xiangxue |
collection | PubMed |
description | BACKGROUND: Typhoons greatly threaten human life and property, especially in China. Therefore, it is important to make effective policy decisions to minimize losses associated with typhoons. METHODS: In this study, the GeoDetector method was used to quantify the determinant powers of natural and socioeconomic factors, and their interactions, on the population casualty rate of super typhoon Lekima. The local indicator of spatial association (LISA) method was followed to explore the spatial pattern of the population casualty rate under the influence of the identified dominant factors. RESULTS: Both natural and socioeconomic factors were found to have significantly impacted the population casualty rate due to super typhoon Lekima. Among the selected factors, maximum precipitation was dominant factor (q = 0.56), followed by maximum wind speed (q = 0.45). In addition, number of health technicians (q = 0.35) and number of health beds (q = 0.27) have a strong influence on the population casualty rate. Among the interactive effects of 12 influencing factors, the combined effects of maximum precipitation and ratio of brick-wood houses, the maximum precipitation and ratio of steel-concrete houses, maximum precipitation and number of health technicians were highest (q = 0.72). Furthermore, high-risk areas with very high casualty rates were concentrated in the southeastern part of Zhejiang and northern Shandong Provinces, while lower-risk areas were mainly distributed in northern Liaoning and eastern Jiangsu provinces. CONCLUSIONS: These results contribute to the development of more specific policies aimed at safety and successful property protection according to the regional differences during typhoons. |
format | Online Article Text |
id | pubmed-8244144 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82441442021-06-30 Spatial pattern of the population casualty rate caused by super typhoon Lekima and quantification of the interactive effects of potential impact factors Zhang, Xiangxue Nie, Juan Cheng, Changxiu Xu, Chengdong Xu, Xiaojun Yan, Bin BMC Public Health Research Article BACKGROUND: Typhoons greatly threaten human life and property, especially in China. Therefore, it is important to make effective policy decisions to minimize losses associated with typhoons. METHODS: In this study, the GeoDetector method was used to quantify the determinant powers of natural and socioeconomic factors, and their interactions, on the population casualty rate of super typhoon Lekima. The local indicator of spatial association (LISA) method was followed to explore the spatial pattern of the population casualty rate under the influence of the identified dominant factors. RESULTS: Both natural and socioeconomic factors were found to have significantly impacted the population casualty rate due to super typhoon Lekima. Among the selected factors, maximum precipitation was dominant factor (q = 0.56), followed by maximum wind speed (q = 0.45). In addition, number of health technicians (q = 0.35) and number of health beds (q = 0.27) have a strong influence on the population casualty rate. Among the interactive effects of 12 influencing factors, the combined effects of maximum precipitation and ratio of brick-wood houses, the maximum precipitation and ratio of steel-concrete houses, maximum precipitation and number of health technicians were highest (q = 0.72). Furthermore, high-risk areas with very high casualty rates were concentrated in the southeastern part of Zhejiang and northern Shandong Provinces, while lower-risk areas were mainly distributed in northern Liaoning and eastern Jiangsu provinces. CONCLUSIONS: These results contribute to the development of more specific policies aimed at safety and successful property protection according to the regional differences during typhoons. BioMed Central 2021-06-29 /pmc/articles/PMC8244144/ /pubmed/34187432 http://dx.doi.org/10.1186/s12889-021-11281-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Zhang, Xiangxue Nie, Juan Cheng, Changxiu Xu, Chengdong Xu, Xiaojun Yan, Bin Spatial pattern of the population casualty rate caused by super typhoon Lekima and quantification of the interactive effects of potential impact factors |
title | Spatial pattern of the population casualty rate caused by super typhoon Lekima and quantification of the interactive effects of potential impact factors |
title_full | Spatial pattern of the population casualty rate caused by super typhoon Lekima and quantification of the interactive effects of potential impact factors |
title_fullStr | Spatial pattern of the population casualty rate caused by super typhoon Lekima and quantification of the interactive effects of potential impact factors |
title_full_unstemmed | Spatial pattern of the population casualty rate caused by super typhoon Lekima and quantification of the interactive effects of potential impact factors |
title_short | Spatial pattern of the population casualty rate caused by super typhoon Lekima and quantification of the interactive effects of potential impact factors |
title_sort | spatial pattern of the population casualty rate caused by super typhoon lekima and quantification of the interactive effects of potential impact factors |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8244144/ https://www.ncbi.nlm.nih.gov/pubmed/34187432 http://dx.doi.org/10.1186/s12889-021-11281-y |
work_keys_str_mv | AT zhangxiangxue spatialpatternofthepopulationcasualtyratecausedbysupertyphoonlekimaandquantificationoftheinteractiveeffectsofpotentialimpactfactors AT niejuan spatialpatternofthepopulationcasualtyratecausedbysupertyphoonlekimaandquantificationoftheinteractiveeffectsofpotentialimpactfactors AT chengchangxiu spatialpatternofthepopulationcasualtyratecausedbysupertyphoonlekimaandquantificationoftheinteractiveeffectsofpotentialimpactfactors AT xuchengdong spatialpatternofthepopulationcasualtyratecausedbysupertyphoonlekimaandquantificationoftheinteractiveeffectsofpotentialimpactfactors AT xuxiaojun spatialpatternofthepopulationcasualtyratecausedbysupertyphoonlekimaandquantificationoftheinteractiveeffectsofpotentialimpactfactors AT yanbin spatialpatternofthepopulationcasualtyratecausedbysupertyphoonlekimaandquantificationoftheinteractiveeffectsofpotentialimpactfactors |