Team members: Fanny Sigouin, Tania Barrera & Merve Celme
This repo contains our work for the first project of the UofT SCS edX Data Bootcamp.
We are using the Major Crime Indicators dataset published by the Toronto Police Services in the Toronto Open Data website.
Link: https://open.toronto.ca/dataset/major-crime-indicators/
We accessed the data using the API for Toronto Open Data datasets.
The process of getting and cleaning the data is detailed in this Jupyter Notebook: get_and_clean_data.ipynb
Analyze major crime indicators in the city of Toronto, identifying trends by type of offences and by neighbourhoods, as well as comparing years from 2014 to 2022 to identify changing crime trends.
Our target audience is the general public. We seek to inform the people living in the City of Toronto about the crime patterns of their city.
- Year to year, has there been an overall increase or reduction in crime? Has a certain type of offence seen a significant decrease or increase?
- What are the peak times for crime occurences? Does it change according to season? Is there a concentration of crime around a specific period of the year?
- On average, how long after the offence date was it reported (offence date vs reporting date)?
- Can we identify trends or patterns in specific neighbourhoods (e.g. certain neighbourhoods seeing an increase or decrease in crime)? Is there a correlation between the type of offence and the location (e.g. are burglaries more common in residential areas)?
- Is there a correlation between certain types of offences (e.g. do areas with high drug-related crimes also see increased violent crimes)?
You can find the analysis we performed to answer the above five questions in this notebook: analysis.ipynb.
Our findings, including the main visualisations, can be found here: p1g2_write-up.pdf.
And the slides we used to present our project are here: p1g2_slides.pdf.
All visualisations in png format are inside the folder viz-results.