Exploring temporal varying demographic and economic disparities in COVID-19 infections in four U.S. areas: based on OLS, GWR, and random forest models

Abstract Although studies have previously investigated the spatial factors of COVID-19, most of them were conducted at a low resolution and chose to limit their study areas to high-density urbanized regions. Hence, this study aims to investigate the economic-demographic disparities in COVID-19 infec...

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Autores principales:	Junfeng Jiao, Yefu Chen, Amin Azimian
Formato:	article
Lenguaje:	EN
Publicado:	Springer 2021
Materias:	COVID-19 Geographically weighted regression Demographic and economic disparities Neighborhood GIS Random forest Cities. Urban geography GF125
Acceso en línea:	https://doaj.org/article/4fd9d93144984b9a8dd61d1441835f7c
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spelling	oai:doaj.org-article:4fd9d93144984b9a8dd61d1441835f7c2021-12-05T12:10:36ZExploring temporal varying demographic and economic disparities in COVID-19 infections in four U.S. areas: based on OLS, GWR, and random forest models10.1007/s43762-021-00028-52730-6852https://doaj.org/article/4fd9d93144984b9a8dd61d1441835f7c2021-12-01T00:00:00Zhttps://doi.org/10.1007/s43762-021-00028-5https://doaj.org/toc/2730-6852Abstract Although studies have previously investigated the spatial factors of COVID-19, most of them were conducted at a low resolution and chose to limit their study areas to high-density urbanized regions. Hence, this study aims to investigate the economic-demographic disparities in COVID-19 infections and their spatial-temporal patterns in areas with different population densities in the United States. In particular, we examined the relationships between demographic and economic factors and COVID-19 density using ordinary least squares, geographically weighted regression analyses, and random forest based on zip code-level data of four regions in the United States. Our results indicated that the demographic and economic disparities are significant. Moreover, several areas with disadvantaged groups were found to be at high risk of COVID19 infection, and their infection risk changed at different pandemic periods. The findings of this study can contribute to the planning of public health services, such as the adoption of smarter and comprehensive policies for allocating economic recovery resources and vaccines during a public health crisis.Junfeng JiaoYefu ChenAmin AzimianSpringerarticleCOVID-19Geographically weighted regressionDemographic and economic disparitiesNeighborhoodGISRandom forestCities. Urban geographyGF125ENComputational Urban Science, Vol 1, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 Geographically weighted regression Demographic and economic disparities Neighborhood GIS Random forest Cities. Urban geography GF125
spellingShingle	COVID-19 Geographically weighted regression Demographic and economic disparities Neighborhood GIS Random forest Cities. Urban geography GF125 Junfeng Jiao Yefu Chen Amin Azimian Exploring temporal varying demographic and economic disparities in COVID-19 infections in four U.S. areas: based on OLS, GWR, and random forest models
description	Abstract Although studies have previously investigated the spatial factors of COVID-19, most of them were conducted at a low resolution and chose to limit their study areas to high-density urbanized regions. Hence, this study aims to investigate the economic-demographic disparities in COVID-19 infections and their spatial-temporal patterns in areas with different population densities in the United States. In particular, we examined the relationships between demographic and economic factors and COVID-19 density using ordinary least squares, geographically weighted regression analyses, and random forest based on zip code-level data of four regions in the United States. Our results indicated that the demographic and economic disparities are significant. Moreover, several areas with disadvantaged groups were found to be at high risk of COVID19 infection, and their infection risk changed at different pandemic periods. The findings of this study can contribute to the planning of public health services, such as the adoption of smarter and comprehensive policies for allocating economic recovery resources and vaccines during a public health crisis.
format	article
author	Junfeng Jiao Yefu Chen Amin Azimian
author_facet	Junfeng Jiao Yefu Chen Amin Azimian
author_sort	Junfeng Jiao
title	Exploring temporal varying demographic and economic disparities in COVID-19 infections in four U.S. areas: based on OLS, GWR, and random forest models
title_short	Exploring temporal varying demographic and economic disparities in COVID-19 infections in four U.S. areas: based on OLS, GWR, and random forest models
title_full	Exploring temporal varying demographic and economic disparities in COVID-19 infections in four U.S. areas: based on OLS, GWR, and random forest models
title_fullStr	Exploring temporal varying demographic and economic disparities in COVID-19 infections in four U.S. areas: based on OLS, GWR, and random forest models
title_full_unstemmed	Exploring temporal varying demographic and economic disparities in COVID-19 infections in four U.S. areas: based on OLS, GWR, and random forest models
title_sort	exploring temporal varying demographic and economic disparities in covid-19 infections in four u.s. areas: based on ols, gwr, and random forest models
publisher	Springer
publishDate	2021
url	https://doaj.org/article/4fd9d93144984b9a8dd61d1441835f7c
work_keys_str_mv	AT junfengjiao exploringtemporalvaryingdemographicandeconomicdisparitiesincovid19infectionsinfourusareasbasedonolsgwrandrandomforestmodels AT yefuchen exploringtemporalvaryingdemographicandeconomicdisparitiesincovid19infectionsinfourusareasbasedonolsgwrandrandomforestmodels AT aminazimian exploringtemporalvaryingdemographicandeconomicdisparitiesincovid19infectionsinfourusareasbasedonolsgwrandrandomforestmodels
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Exploring temporal varying demographic and economic disparities in COVID-19 infections in four U.S. areas: based on OLS, GWR, and random forest models

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