COVID-19: A Neighbourhood View for the City of Toronto
Recent events in the Canadian data world have offered us new information to take a closer look at the COVID-19 pandemic, specifically as it affects Toronto. Some of these events include the City of Toronto releasing daily COVID-19 case data for each of its 140 neighbourhoods and the launch of our newly rebuilt PRIZM segmentation system that provides the most comprehensive and up-to-date portrait of Canadians.
We wondered how combining Toronto’s more detailed case data with EA’s newly released population data could support the public health response to COVID-19. By merging and analyzing these data, we were able to access a richer and more nuanced view of the Toronto population by neighborhood. We believe this type of analysis will only become more valuable as private industry, the public sector and non-profits alike look to understand the social-economic and racial impacts of the virus.
New data for a nuanced view of the COVID-19 pandemic as it affects Toronto
We wanted to understand how COVID-19 affects Toronto by neighbourhood and create a basis for the comparison of different neighbourhoods. To do this, we applied the average Toronto infection rates associated with each PRIZM segment to the corresponding PRIZM population make-up in those areas.
Using geodemographic methods, we were able to look at the differences between actual and expected cases and identify areas with both lower-than-expected cases and higher-than-expected cases. We could then take a closer look at populations at the neighbourhood level and draw insights about how and where to focus prevention efforts.
Through this approach we were able to:
- Highlight areas that are more successful in controlling the spread and those that are less successful (while controlling for socio-demographic characteristics)
- Recognize where resources could be prioritized to prevent outbreaks (i.e. the areas with significantly lower-than-expected cases that could become hotspots)
- Identify which areas to study more closely to understand what is different about those with lower-than-expected case rates
- Look a little more deeply into the areas that have lower-than-expected cases to help identify what these neighbourhoods are doing right and help to further tailor prevention efforts to these populations
Actual vs Expected COVID-19 Cases in TorontoThe first map shows the COVID-19 case count by City of Toronto neighbourhood. For those familiar with the city’s socio-demographic “map”, we see the typical pattern associated with education and affluence evident through the middle of the city.
Difference between actual and "expected" COVID-19 cases in Toronto
The next map puts the neighbourhood case counts into perspective and there appears to be an east-west divide in the city. The dark red neighbourhoods have 50+ cases more than expected, while the dark blue neighbourhoods have at least 50 fewer cases than expected.
This is a sampling of our analysis and only scratches the surface of what is possible. The ability to merge multiple data sources, benchmark and compare results and to understand case rates in context can lead to more appropriate and targeted public health responses and communications.
While we infer the characteristics of patients based on where they live, it is possible to combine patient data (or if outside of healthcare, with customer, donor, prospect and facility location data) to learn about populations of interest at the neighbourhood level. EA produces the kind of privacy-compliant, small-area estimates that have been used for more than 30 years to understand populations of interest in private industry, the public sector and non-profits alike.
By providing access to up-to-date Canadian demographics and applying our expertise in geodemography, we can continue to provide data-driven solutions to organizations facing unprecedented challenges as a result of COVID-19.
Download COVID-19: A Neighbourhood View for the City of Toronto full analysis, here.
As we assess the impact of COVID-19, government, NGOs and businesses alike want to understand who needs support, what kinds of services are required for different populations and the direction business will take as the economy reopens.
Read more about Vulnerability Indices, here.
All maps are generated by Environics Analytics. To learn more about the data used in the creation of this study or to ask a question about your specific situation, get in touch. We’re ready to help.