CrimeLens is a real-time AI neighbourhood intelligence platform for Toronto. Real estate professionals use it to give clients objective, data-driven neighbourhood risk assessments.
The problem
Real estate agents had no reliable, real-time source of neighbourhood crime and safety data. The data existed in Toronto’s police open data, but it was raw and needed significant processing to be useful.
What we built
We built a geospatial data ingestion pipeline that pulls Toronto’s public crime data, normalizes it, and feeds it into a PostGIS database. On top of that, we built ML models that score neighbourhood risk across multiple dimensions and trend those scores over time.
The front end is a web application that lets agents and buyers search any Toronto address and see a detailed breakdown of crime trends, risk scores and neighbourhood context.
The hard parts
Open data is published for record-keeping, not for products. Categories, locations and formats need cleaning before they can be compared over time, so we built a normalization layer that outputs a consistent schema the scoring model can rely on.
The other challenge was making the risk scores meaningful rather than just raw counts. A neighbourhood with 100 incidents in a dense downtown core is very different from one with 100 incidents in a quieter residential area. We built population-density weighting into the scoring model to make comparisons meaningful.
The outcome
CrimeLens is live across Toronto, processing real-time data. Real estate professionals use it to give clients objective neighbourhood context.
If you're building a geospatial AI product, book a strategy call. We know this problem well.