Exploring local burglary shifts in Lockdown The lockdown and social distancing measures that were brought in throughout the world to tackle COVID in 2020 have had a significant, widespread effect on crime. In this notebook, I use public London crime data on robbery and burglary to examine where this “COVID crime shift” was strongest, and whether any specific drivers or correlates can be identified.
Our findings suggest that the relative change in burglary and robbery trends during “lockdown” in April and May 2020 was heavily affected by local characteristics: areas with a high residential population saw teh sharpest decreases in burglary (likely due to a reduction in available targets) while the reduction in robberies instead seem to be driven by geographic features and proxy indicators of deprivation (potentially suggesting more available targets for robbery in communities least able to work for from).
The primary purpose of this exercise was to learn R - I’ve previously worked entirely in Python, which is more than sufficient 99% of the time, but has at times proved a blocker when I want to tackle some more experimental geospatial methods or tools geared towards the academic community. With that in mind, this is likely to be a little messy, and I’ll aim to condense my main lessons into a blog post in the future. The models are not heavily tuned (aiming to explore correlates rather than provide accurate predictions) and certain predictors are likely to correlate with each other, and as such likely do not imply direct causations.