The main goal of this exploration is to identify regions that have high occupational risk of being laid off, as well as identify areas that are more resilient. Additionally, an examination on the diversity of occupations in an area deemed “at-risk” to “resilient” was conducted.
First, all occupation data was downloaded from CHASS. All of the occupation data was classified into 4 categories as the following:
| Most at risk | Most likely at risk | Unknown impacts | Resilient |
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Categorization was done based off of Statistics Canada reports on decrease/increase in occupations from February to March, as well as other news reports. If an occupation change in employment was little, we decided to put it under “unknown impacts” as the significance in change wasn’t enough to attribute to COVID-19.
A diversity index was performed on excel using the formula:
Where n represents the amount in each category, and N is the total number employed.
The diversity index was then plotted as a hot spot map to find areas of high and low diversity
Additionally, an LQ analysis was done (similar to the other maps above) for the category “most at risk + most likely at risk” and for “most resilient”. Then, a hotspot map for the 2 categories were done. By using select features, we then filtered to find areas by intersect to find areas in our “most at risk + most likely at risk” hot spot map that had a GI_BIN value > 0 and “most resilient” GI_BIN value < 0 and vice versa. This was done to find any similarities between the two maps.