Dongmei CHEN, Queen's University, Canada
Philllipe TOUSIGNANT, Queen's University, Canada
Lu WANG, Ryerson University, Canada
The COVID-19 pandemic has caused unmatched global and national economic and social impacts so far. Despite governmental interventions, the impact of the pandemic on the population is not even. Inequality has been already evident in the vulnerable population and neighborhoods during this pandemic. Researchers from different disciplines are interested in factors shaping prevention practices which lead to different COVID-19 exposure risks. This study is intended to examine the socioeconomic factors that contribute to COVID-19 incidence in the city of Toronto. There exists measurable variability in factors such as income, immigration status, and the household size across the study area. Using available data from the city’s open-source web page, the variability in factors influencing the degree to which individuals in each neighbourhood are likely to contract the virus are mapped and visualized. Further analysis is carried out to measure local variability and the presence of outliers over the time scale from January 2020 to December 2021. Major findings include the characterization of peripheral neighborhoods as consecutive hotspots, which concurrently are predominantly inhabited by individuals of lower income and higher count of household members relative to the downtown Toronto area. Another notable finding is the distinct recording of two peaks of COVID-19 that occur in each neighbourhood across the timeline and study region.
Mots clés : COVID-19|health inequality|social and economical factors|GIS|spatial-temporal analysis
A104119DC