Exploring the London COVID Crime Effect in R

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. I use three years of Metropolitan Police Service data from data.police.uk.

The findings suggest that the relative change in burglary and robbery in April and May 2020 was heavily affected by local characteristics: areas with a high residential population saw the 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 indicators of deprivation (potentially suggesting more available targets for robbery in communities least able to work for from home).

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Learning GIS in Python - Robbery and Police Searches in Space

Violence and Police Activity in Space

Amongst stuffing my face with wine and cheese, I've used this Christmas break to learn more about geospatial modelling in Python.

This blog post is largely intended for my reference and to act as a useful example for others...as such, it may be messy! I'll try and tidy it into a Medium post in the coming weeks.

Space is an often disregarded dimension of modelling within policing research. As per Tobler's first law of geography, "everything is related to everything else, but near things are more related than distant things", and this is probably especially true of crime, that tenders to cluster in both time and space...treating your models as not having distinct physical locations that influence how they behave is likely to miss crucial information.

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Predicting Police Call Demand for Fun (and Prophet)

LAPD Call Prediction for Fun (and Prophet)

Time series prediction is more complicated than I originally anticipated when I tackled the subject during my thesis - while you can treat the events independently, like geographic data, everything is related: what happened yesterday will affect what happened today, and a Friday in July is not the same as a Monday in October.

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