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dc.contributor.authorZhang, Jinting ( Orcid Icon 0000-0003-3037-7645 )
dc.contributor.authorWu, Xiu ( Orcid Icon 0000-0001-8032-3317 )
dc.contributor.authorChow, T. Edwin ( Orcid Icon 0000-0002-0386-5902 )
dc.date.accessioned2021-07-29T16:44:14Z
dc.date.available2021-07-29T16:44:14Z
dc.date.issued2021-05-22
dc.identifier.citationZhang, J., Wu, X., & Chow, T. E. (2021). Space-time cluster’s detection and geographical weighted regression analysis of COVID-19 mortality on Texas counties. International Journal of Environmental Research and Public Health, 18(11), 5541.en_US
dc.identifier.issn1660-4601
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/14124
dc.description.abstractAs COVID-19 run rampant in high-density housing sites, it is important to use real-time data in tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research appropriately analyzed the disparities between spatial-temporal clusters, expectation maximization clustering (EM), and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to Southeast Texas analysis in Geographically Weighted Regression (GWR) modeling. The sensitive period took place in the last two quarters in 2020 and the first quarter in 2021. We explored PostSQL application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental impact’s indices to perform principal component analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, adult population, natural supply, economic condition, air quality or medical care. We established the GWR model to seek the sensitive factors. The result shows that adult population, economic condition, air quality, and medical care are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility, and satisfy for the need of emergency operations plan (EOP).en_US
dc.formatText
dc.format.extent21 pages
dc.format.medium1 file (.pdf)
dc.language.isoenen_US
dc.publisherMultidisciplinary Digital Publishing Instituteen_US
dc.sourceInternational Journal of Environmental Research and Public Health, 2021, Vol. 18, No. 11, Article 5541.
dc.subjectGeographical weighted regressionen_US
dc.subjectSpace-time cluster's detectionen_US
dc.subjectCOVID-19en_US
dc.subjectMortalityen_US
dc.titleSpace-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Countiesen_US
dc.typepublishedVersion
txstate.documenttypeArticle
dc.rights.holder© 2021 The Authors.
dc.identifier.doihttps://doi.org/10.3390/ijerph18115541
dc.rights.licenseCreative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.description.departmentGeography


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