4.7 Article

Exploring spatial heterogeneity and environmental injustices in exposure to flood hazards using geographically weighted regression

期刊

ENVIRONMENTAL RESEARCH
卷 210, 期 -, 页码 -

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.envres.2022.112982

关键词

Environmental justice; Flood risk; Geographically weighted regression; Socioeconomic inequality; Spatial heterogeneity

资金

  1. Social Sciences and Humanities Research Council (SSHRC), Canada
  2. Canadian Institutes of Health Research (CIHR)
  3. Canadian Foundation for Innovation (CFI)
  4. Statistics Canada
  5. University of Waterloo, Canada

向作者/读者索取更多资源

This study explores the impact of racial, ethnic, and socio-demographic disparities and spatial heterogeneity on flood-related environmental injustices in Canada. By integrating flood maps and socioeconomic data, the study investigates whether vulnerable groups are disproportionately exposed to different types of flooding. The findings reveal that certain vulnerable groups, such as Indigenous peoples and economically insecure residents, are at a higher risk of flooding. These spatial and social disparities have important policy implications for emergency management and disaster risk reduction.
This study explores flood-related environmental injustices by deconstructing racial, ethnic, and socio-demographic disparities and spatial heterogeneity in the areal extent of fluvial, pluvial, and coastal flooding across Canada. The study integrates JBA Risk Management's 100-year Canada Flood Map with the 2016 national census-based socioeconomic data to investigate whether traditionally recognized vulnerable groups and com-munities are exposed inequitably to inland (e.g., fluvial and pluvial) and coastal flood hazards. Social vulnera-bility was represented by neighbourhood-level socioeconomic deprivation, including economic insecurity and instability indices. Statistical analyses include bivariate correlation and a series of non-spatial and spatial regression techniques, including ordinary least squares, binary logistic regression, and simultaneous autore-gressive models. The study emphasizes the quest for the most appropriate methodological framework to analyze flood-related socioeconomic inequities in Canada. Strong evidence of spatial effects has motivated the study to test for the spatial heterogeneity of covariates by employing geographically weighted regression (GWR) on continuous outcome variables (e.g., percent of residential properties in a census tract exposed to flood hazards) and geographically weighted logistic regression on dichotomous outcome variables (e.g., a census tract in or out of flood hazard zone). GWR results show that the direction and statistical significance of relationships between inland flood exposure and all explanatory variables under consideration are spatially non-stationary. We find certain vulnerable groups, such as females, lone-parent households, Indigenous peoples, South Asians, the elderly, other visible minorities, and economically insecure residents, are at a higher risk of flooding in Canadian neighbourhoods. Spatial and social disparities in flood exposure have critical policy implications for effective emergency management and disaster risk reduction. The study findings are a foundation for a more detailed investigation of the disproportionate impacts of flood risk in Canada.

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