The objective of this project is to leverage Power BI for data cleaning, ensuring consistency by correcting data types and addressing any corrupted entries. The focus is on summarizing and analyzing auto-theft data, uncovering insights into neighborhood clusters, property-type distributions, and theft patterns across various times—such as days of the week and hourly trends. These insights will allow us to identify patterns and better understand the characteristics of high-theft areas. The data was obtained from the Toronto Police Service Public Safety Portal https://data.torontopolice.on.ca/datasets/TorontoPS::auto-theft-open-data/about
Leveraged Power BI query to correct column data types (transform the data) and employed DAX to incorporate national holidays, summarizing auto-theft trends by day of the year. Developed interactive visualizations and ArcGIS maps to present these insights clearly and engagingly.
- West Humber-Clairville reports the highest number of auto-thefts, totaling 2,507 incidents.
- Residential locations account for the majority of thefts, with single-family homes making up 35% of total thefts, while only 7.5% occur at commercial locations, highlighting a significant contrast in targeted areas.
- In West Humber-Clairville, 51.74% of auto-thefts occur in parking lots, underscoring a prominent hotspot within the neighborhood.
- Auto-thefts dropped sharply by 52.5% during COVID-19 (2020) compared to 2023, the year with the highest reported incidents.
- Thanksgiving Weekend saw the highest theft spike, with a 200% increase.
- The peak hour for auto-thefts was around 10 PM, accounting for 9% of total incidents.
- Thursdays experienced the highest frequency of thefts, contributing to 16% of overall cases.
- Major neighborhood clusters around Downsview Airport and Toronto Pearson International Airport were identified, contributing to nearly 10% of all auto-thefts.
- Additional significant clusters include the areas around the Toronto Zoo, Etobicoke City Centre and Wexford-Maryvale Neighborhoods.
