TY - JOUR
T1 - Spatiotemporal Modeling of the Association between Neighborhood Factors and COVID-19 Incidence Rates in Scotland
AU - Wang, Ruoyu
AU - Clemens, Tom
AU - Douglas, Margaret
AU - Keller, Markéta
AU - van der Horst, Dan
N1 - Funding Information:
We gratefully acknowledge support from the Scottish Funding Council and the DDI Data Platforms Innovation Programme
Publisher Copyright:
© 2023 by American Association of Geographers.
PY - 2023/5/30
Y1 - 2023/5/30
N2 - This study aims to investigate the association between neighborhood-level factors and COVID-19 incidence in Scotland from a spatiotemporal perspective. The outcome variable is the COVID-19 incidence in Scotland. Based on the identification of the wave peaks for COVID-19 cases between 2020 and 2021, confirmed COVID-19 cases in Scotland can be divided into four phases. To model the COVID-19 incidence, sixteen neighborhood factors are chosen as the predictors. Geographical random forest models are used to examine spatiotemporal variation in major determinants of COVID-19 incidence. The spatial analysis indicates that proportion of religious people is the most strongly associated with COVID-19 incidence in southern Scotland, whereas particulate matter is the most strongly associated with COVID-19 incidence in northern Scotland. Also, crowded households, prepandemic emergency admission rates, and health and social workers are the most strongly associated with COVID-19 incidence in eastern and central Scotland, respectively. A possible explanation is that the association between predictors and COVID-19 incidence might be influenced by local context (e.g., people’s lifestyles), which is spatially variant across Scotland. The temporal analysis indicates that dominant factors associated with COVID-19 incidence also vary across different phases, suggesting that pandemic-related policy should take spatiotemporal variations into account.
AB - This study aims to investigate the association between neighborhood-level factors and COVID-19 incidence in Scotland from a spatiotemporal perspective. The outcome variable is the COVID-19 incidence in Scotland. Based on the identification of the wave peaks for COVID-19 cases between 2020 and 2021, confirmed COVID-19 cases in Scotland can be divided into four phases. To model the COVID-19 incidence, sixteen neighborhood factors are chosen as the predictors. Geographical random forest models are used to examine spatiotemporal variation in major determinants of COVID-19 incidence. The spatial analysis indicates that proportion of religious people is the most strongly associated with COVID-19 incidence in southern Scotland, whereas particulate matter is the most strongly associated with COVID-19 incidence in northern Scotland. Also, crowded households, prepandemic emergency admission rates, and health and social workers are the most strongly associated with COVID-19 incidence in eastern and central Scotland, respectively. A possible explanation is that the association between predictors and COVID-19 incidence might be influenced by local context (e.g., people’s lifestyles), which is spatially variant across Scotland. The temporal analysis indicates that dominant factors associated with COVID-19 incidence also vary across different phases, suggesting that pandemic-related policy should take spatiotemporal variations into account.
KW - COVID-19
KW - geographical random forest model
KW - neighborhood factors
KW - Scotland
KW - spatial-temporal pattern
U2 - 10.1080/00330124.2023.2194363
DO - 10.1080/00330124.2023.2194363
M3 - Article
AN - SCOPUS:85160740876
SN - 0033-0124
VL - 75
SP - 803
EP - 815
JO - Professional Geographer
JF - Professional Geographer
IS - 5
ER -