New COVID-19 data breaks down London's cases by neighbourhood

Data obtained by CBC News reveals for the first time the geographic distribution of COVID-19 cases in London.

The data shows the geographical distribution of COVID-19 cases in London

A map of COVID-19 cases per capita in London, Ont., neighbourhoods. (Created by Travis Dolynny/CBC News)

For the first time, Londoners can get a sense of how their part of the city fared during the first wave of the COVID-19 pandemic. 

Scroll or tap over the postal codes to see case numbers and per-capita rates. (If you can't see the map below, click here)

Data obtained by CBC News shows that the far south of the city where the postal code begins with N6L, east of Wonderland Road and south of Highway 401, had the highest per-capita rate of the coronavirus between Jan. 31 and Sept. 15.

The two other parts of the city with the highest per-capita rates of COVID-19 cases were in the parts of the city with N6A and N6M postal codes. 

The data captures the city's first wave of coronavirus cases, when outbreaks were mostly contained to long-term care facilities. It is limited to the primary residence of those who tested positive for COVID-19, not where the disease was contracted.

How we got the data

  • CBC News asked the Middlesex-London Health Unit (MLHU) for the data, but the request was denied.
  • CBC News filed a freedom of information request for cases broken down by postal code. 
  • MLHU released the information but only using the first three characters of a postal code, called a forward sortation area (FSA) by Canada Post, citing privacy concerns.
  • "The Middlesex-London Health Unit currently has a dashboard that is updated on a daily basis. The dashboard shows cases by age group, municipality, gender, exposure source, reported date and episode date," the health unit's privacy officer wrote to CBC News."Some postal codes have very few households within them; therefore, having access to the postal codes as well as the information on the MLHU dashboard may narrow down the number of cases to a handful of households and people."
  • CBC News used 2016 Census data for each FSA to calculate the per-capita COVID rate. 

In other jurisdictions, such information is publicly available.

In Toronto, for example, city officials released detailed, geographic information about cases in each neighbourhood in May, and has continued to update the data for anyone who wants to use it. 

Dr. Chris Mackie, the region's medical officer of health, said much of the variation on the map is due to the location of long-term care and retirement homes that had outbreaks during the first wave. 

"There is a risk in over-interpreting this data," Mackie said. "We know that COVID-19 can affect anyone in any neighbourhood."

In the case of the N6L area, the large per-capita rate is due to a small population, Mackie said. Only just-over 3,000 people live in that area. 

"In such a relatively small population, something like one or two clusters in families can skew the data," Mackie said. 

"What is helpful is to see how clustering and outbreaks play a big role. This is a disease that explodes under the right conditions: one sick person in close contact with many other people. We absolutely need to avoid large gatherings right now, and never participate in large gatherings where social distancing is not possible."

The more information, the better

The more information residents and public health officials have about who is getting sick and where they are cathing the virus, the better, said Don Kerr, a demographer at King's University College who specializes in the socioeconomic and political consequences of demographic change in Canada.

"It's interesting to look at the data and to see your own neighbourhood. For example, I live near Huron and Highbury, and I'm surprised to see it has a relatively higher rate of infection than other parts of the city," Kerr said.

"Londoners have a right to know this kind of information so they can take proper precautions. We know that there are all kinds of risk factors associated with COVID-19 transmission, things like higher-density housing or lower socio-economic status. 

"It's logical that certain neighbourhoods within London are going to have a higher incidence of COVID."