New Scatterplots Graph COVID-19 Against Income in Montreal and Other Cities

It has been an interesting few months for the Geo-Social Determinants of Health Research Group. While we have all been working from home, we have managed to stay plenty busy with research!

Along with advancing our work on Canadian active living environments and neighbourhood food environments, we have also applied a Health Geography lens to examine the effects of the COVID-19 pandemic in Montreal.

Our group’s interest in the social determinants of health and spatial variations in health led us to investigate the relationship between rates of COVID-19 cases and socioeconomic status across Montreal neighbourhoods. Lab members Clara Kaufmann and Camille Clement, who are spearheading the research, recently developed scatterplots to visualize the data.

Graph of COVID-19 in Montreal
Data from Ville de Montréal and Santé Montréal (updated May 18, 2020).

Preliminary results show a pattern of higher rates of COVID-19 cases in neighbourhoods with lower median household income.


The same pattern was visible in data from other Canadian and U.S. cities:

Graph of COVID-19 and income in NYC
Data from US Census Bureau and NYC Health (updated May 18, 2020).
Graph of COVID-19 and income in San Francisco
Data from US Census Bureau and DataSF (updated May 14, 2020).

Urban density may play a role in contributing to the impact of COVID-19 at the neighbourhood level. The pattern was also similar at a broader level of geography, looking across boroughs in New York City and municipalities in the Greater Toronto Area.

Graph of COVID-19 and income in NYC
Data from US Census Bureau and NYC Health (Updated May 18, 2020).
Graph of COVID-19 and income in Toronto
Data from Statistics Canada 2016 Census and Public Health Ontario (Updated May 14, 2020).


The neighbourhood trend between low income and higher rates of COVID-19 suggests that the impact of COVID-19 follows the social gradient of health, meaning individuals with lower socioeconomic status are more likely to become infected. Many factors–such as economic insecurity, dependence on commuting via public transit, and poor housing conditions–may determine why low income areas are hit hardest by COVID-19. 

These scatterplots are the first step in an ongoing project that will delve further into the nuances of the social determinants and spatial variation of COVID-19 impact across Montreal and other cities around the globe.