The Canberra Branch of the Statistical Society meeting on Tuesday 30 August was addressed by Dr Joanna Wang of the University of Technology, Sydney. Over 20 people joined the online seminar.
Joanna began by reviewing the literature, which included studies from the US, Sweden, UK and Canada. Her won work used data from NSW from 2017 – mid-2020, a timespan ensuring that the first COVID lockdown was represented. She focused on a handful of different crime types, noting that there is debate around whether the counts should be aggregated or analysed separately, and whether to expect disaggregated estimates to sum up to aggregated totals.
Joanna used a classic interrupted time series model, though I was curious to learn more about the ARIMA error structure that she was also able to impose. As always, there’s an R package for that (fable in this case) along with the use of AIC for model selection.
The main conclusions were that the change (mostly decline) in crime during the early days of COVID was more about the changing supply of opportunities than the changing supply of perpetrators. Joanna did a great job of presenting the model and results in a clear fashion, along with discussion of what it all meant.
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