The Toronto public health unit recently announced that they are going release Covid-19 statistics. Values are reported by age, neighbourhood, outcome, and hospitalization type among other ways. Using this information, some visualizations were created to help try to find some insight in the dataset.
One major limitation of the data is that is updated once a week. From the health unit’s website they have stated: “The data will be completely refreshed and overwritten on a weekly basis. The data are extracted at 3 PM on the Monday of a given week, and posted by Wednesday“.
Data Updating…
Since the data is from an external source, it is going to updated manually to ensure that no new issues have been introduced with it. Note that all of the visualizations were created using the same dataset and therefore have the same time frame.
Map of Covid-19 Cases
The following map was created using D3 + JS to track the number of total, resolved, active, and fatal cases across the city. For me, the big item of interest is which neighbourhoods the number of active (or unresolved) cases is zero.
Click here to view map the map on a separate page (without any wordpress page decorations; for mobile users).
Amounts per Neighbourhood
Next a graph was created with the same data to compare the number of cases per neighbourhood. The main purpose of this graph is to quickly look at the neighbourhoods in each top and bottom case outcome category.
Click here to view the data on a separate page (without any wordpress page decorations; for mobile users).
Hospitalized Cases
With the previous datasets (from the Ontario government or the Dana Lana School of public health), not much has been stated about hospitalized cases. Hence a graph was created to examine the outcome type, gender, and hospitalization type for each age group.
It is interesting that every age group is affected.
Click here to view the data on a separate page (without any wordpress page decorations; for mobile users).
Code for Data Processing and Visualization
Again, all the data is available from the Toronto open data website. To extract some meaning from the data, a set of python scripts were written which generate results into a set of CSV files. Then the javascript code which uses D3.js was written to make the results interactive for the web.
All of the code is available in the following Github repository.
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