Geographical and Temporal Weighted Regression: Examining Spatial Variations of COVID-19 Mortality Pattern using Mobility and Multi-Source Data
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Keywords: COVID-19, GTWR, spatial-temporal
Abstract Type: Paper Abstract
Authors:
Nanzhou Hu, Texas A&M University
Zhe Zhang, Texas A&M University
Nick Duffield, Texas A&M University
Xiao Li, University of Oxford
Bahar Dadashova, Texas A&M transportation institute
Jason Wu, Texas A&M transportation institute
Siyu Yu, Texas A&M University
Ziyi Zhang, Texas A&M University
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Abstract
The COVID-19 pandemic has caused huge negative impacts on public health and society. Reviews of the literature indicated the increase in mobility would cause the spread of COVID-19, and populations with concerning health conditions such as diabetes and heart disease are more vulnerable to COVID-19 related mortality. However, how health conditions and mobility factors affect COVID-19 mortality at the spatiotemporal scale has not been fully understood. This study aimed to quantify the impact of human mobility and health conditions on COVID-19 mortality in the United States using the Geographical and Temporal Weighted Regression (GTWR) model. Several significant factors are examined in this model, including mobility, demographic, and health-related factors.
Moreover, we compared the GTWR model results with the Multi-scale Geographically Weighted Regression (MGWR) model to examine the performance of the GTWR model. Results from the GTWR model indicate that human mobility and health conditions pose a significant impact on COVID-19 mortality spatially. More importantly, the association between COVID-19 and the explanatory variables shows different patterns, providing support insights for policymakers.
Geographical and Temporal Weighted Regression: Examining Spatial Variations of COVID-19 Mortality Pattern using Mobility and Multi-Source Data
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Paper Abstract
