A look at the impact of COVID-19 on the proportion of theft-related crimes in Vancouver.
A Vancouver Police Deparment vehicle.
This project leverages statistical inference concepts covered in STAT 201, Statistical Inferece at the University of British Columbia. Data processing and analysis is done using the R programming language.
Here's the full abstract!
Abstract This project looks at the impact of the COVID-19 pandemic on theft-related crime in select Vancouver neighborhoods. Inferential techniques are applied to estimate the difference in the proportion of theft crime relative to all crime between 2020 and the mean proportion of the three previous years, 2017 to 2019; we test to see if there has been a statistically significant change in the proportion of theft related crime across individual Vancouver neighborhoods. We anticipated we would find a significant difference in theft related crime overall due to, presumably, the economic hardship induced by the COVID-19 pandemic in 2020; in the end, we instead found that roughly half of all examined neighborhoods saw no statistically significant change in theft related crime; there were neighborhoods that did see a statistically significant change in the proportion of theft-related crime, but this change did not seem to correlate with each neighborhoods' average income, as anticipated.
This project was authored by Michael DeMarco (@michaelfromyeg), Adam Mitha (@adammitha), Acky Xu (@ackyxu), and Icy Xu (@icyxxl).
To run the source code, install Jupyter, R, and the R kernel for Jupyter. Exact steps will vary based on your system.
Once you have the notebook running (i.e., code/project-final.ipynb is up), make sure to uncomment the first cell and install all needed packages.
This project is largely "as-is," but if you happen to come across any issues or typos, feel free to open an issue.