Geographical and temporal weighted regression: examining spatial variations of COVID-19 mortality pattern using mobility and multi-source data
Abstract The COVID-19 pandemic has had profound adverse effects on public health and society, with increased mobility contributing to the spread of the virus and vulnerable populations, such as those with pre-existing health conditions, at a higher risk of COVID-19 mortality. However, the specific s...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-03-01
|
| Series: | Computational Urban Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s43762-024-00117-1 |
| _version_ | 1827328253837180928 |
|---|---|
| author | Nanzhou Hu Ziyi Zhang Nicholas Duffield Xiao Li Bahar Dadashova Dayong Wu Siyu Yu Xinyue Ye Daikwon Han Zhe Zhang |
| author_facet | Nanzhou Hu Ziyi Zhang Nicholas Duffield Xiao Li Bahar Dadashova Dayong Wu Siyu Yu Xinyue Ye Daikwon Han Zhe Zhang |
| author_sort | Nanzhou Hu |
| collection | DOAJ |
| description | Abstract The COVID-19 pandemic has had profound adverse effects on public health and society, with increased mobility contributing to the spread of the virus and vulnerable populations, such as those with pre-existing health conditions, at a higher risk of COVID-19 mortality. However, the specific spatial and temporal impacts of health conditions and mobility on COVID-19 mortality have yet to be fully understood. In this study, we utilized the Geographical and Temporal Weighted Regression (GTWR) model to assess the influence of mobility and health-related factors on COVID-19 mortality in the United States. The model examined several significant factors, including demographic and health-related factors, and was compared with the Multiscale Geographically Weighted Regression (MGWR) model to evaluate its performance. Our findings from the GTWR model reveal that human mobility and health conditions have a significant spatial impact on COVID-19 mortality. Additionally, our study identified different patterns in the association between COVID-19 and the explanatory variables, providing insights to policymakers for effective decision-making. |
| first_indexed | 2024-03-07T15:15:00Z |
| format | Article |
| id | doaj.art-62ef193b34cf4c1e8f6fd1a01395a37f |
| institution | Directory Open Access Journal |
| issn | 2730-6852 |
| language | English |
| last_indexed | 2024-03-07T15:15:00Z |
| publishDate | 2024-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Computational Urban Science |
| spelling | doaj.art-62ef193b34cf4c1e8f6fd1a01395a37f2024-03-05T17:59:01ZengSpringerComputational Urban Science2730-68522024-03-014111310.1007/s43762-024-00117-1Geographical and temporal weighted regression: examining spatial variations of COVID-19 mortality pattern using mobility and multi-source dataNanzhou Hu0Ziyi Zhang1Nicholas Duffield2Xiao Li3Bahar Dadashova4Dayong Wu5Siyu Yu6Xinyue Ye7Daikwon Han8Zhe Zhang9Department of Geography, Texas A&M UniversityDepartment of Electrical and Computer Engineering, Texas A&M UniversityDepartment of Electrical and Computer Engineering, Texas A&M UniversityTransport Studies Unit, Oxford UniversityTraffic Operations and Roadway Safety Division, Texas A&M Transportation InstituteResearch & Implementation, Texas A&M Transportation InstituteDepartment of Landscape Architecture & Urban Planning, Texas A&M UniversityDepartment of Landscape Architecture & Urban Planning, Texas A&M UniversitySchool of Public Health, Texas A&M UniversityDepartment of Geography, Texas A&M UniversityAbstract The COVID-19 pandemic has had profound adverse effects on public health and society, with increased mobility contributing to the spread of the virus and vulnerable populations, such as those with pre-existing health conditions, at a higher risk of COVID-19 mortality. However, the specific spatial and temporal impacts of health conditions and mobility on COVID-19 mortality have yet to be fully understood. In this study, we utilized the Geographical and Temporal Weighted Regression (GTWR) model to assess the influence of mobility and health-related factors on COVID-19 mortality in the United States. The model examined several significant factors, including demographic and health-related factors, and was compared with the Multiscale Geographically Weighted Regression (MGWR) model to evaluate its performance. Our findings from the GTWR model reveal that human mobility and health conditions have a significant spatial impact on COVID-19 mortality. Additionally, our study identified different patterns in the association between COVID-19 and the explanatory variables, providing insights to policymakers for effective decision-making.https://doi.org/10.1007/s43762-024-00117-1COVID-19MobilityMulti-scale geographically weighted regressionGeographical and temporal weighted regression |
| spellingShingle | Nanzhou Hu Ziyi Zhang Nicholas Duffield Xiao Li Bahar Dadashova Dayong Wu Siyu Yu Xinyue Ye Daikwon Han Zhe Zhang Geographical and temporal weighted regression: examining spatial variations of COVID-19 mortality pattern using mobility and multi-source data Computational Urban Science COVID-19 Mobility Multi-scale geographically weighted regression Geographical and temporal weighted regression |
| title | Geographical and temporal weighted regression: examining spatial variations of COVID-19 mortality pattern using mobility and multi-source data |
| title_full | Geographical and temporal weighted regression: examining spatial variations of COVID-19 mortality pattern using mobility and multi-source data |
| title_fullStr | Geographical and temporal weighted regression: examining spatial variations of COVID-19 mortality pattern using mobility and multi-source data |
| title_full_unstemmed | Geographical and temporal weighted regression: examining spatial variations of COVID-19 mortality pattern using mobility and multi-source data |
| title_short | Geographical and temporal weighted regression: examining spatial variations of COVID-19 mortality pattern using mobility and multi-source data |
| title_sort | geographical and temporal weighted regression examining spatial variations of covid 19 mortality pattern using mobility and multi source data |
| topic | COVID-19 Mobility Multi-scale geographically weighted regression Geographical and temporal weighted regression |
| url | https://doi.org/10.1007/s43762-024-00117-1 |
| work_keys_str_mv | AT nanzhouhu geographicalandtemporalweightedregressionexaminingspatialvariationsofcovid19mortalitypatternusingmobilityandmultisourcedata AT ziyizhang geographicalandtemporalweightedregressionexaminingspatialvariationsofcovid19mortalitypatternusingmobilityandmultisourcedata AT nicholasduffield geographicalandtemporalweightedregressionexaminingspatialvariationsofcovid19mortalitypatternusingmobilityandmultisourcedata AT xiaoli geographicalandtemporalweightedregressionexaminingspatialvariationsofcovid19mortalitypatternusingmobilityandmultisourcedata AT bahardadashova geographicalandtemporalweightedregressionexaminingspatialvariationsofcovid19mortalitypatternusingmobilityandmultisourcedata AT dayongwu geographicalandtemporalweightedregressionexaminingspatialvariationsofcovid19mortalitypatternusingmobilityandmultisourcedata AT siyuyu geographicalandtemporalweightedregressionexaminingspatialvariationsofcovid19mortalitypatternusingmobilityandmultisourcedata AT xinyueye geographicalandtemporalweightedregressionexaminingspatialvariationsofcovid19mortalitypatternusingmobilityandmultisourcedata AT daikwonhan geographicalandtemporalweightedregressionexaminingspatialvariationsofcovid19mortalitypatternusingmobilityandmultisourcedata AT zhezhang geographicalandtemporalweightedregressionexaminingspatialvariationsofcovid19mortalitypatternusingmobilityandmultisourcedata |