Making Toronto's democracy more accessible.
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Updated
Jun 25, 2026 - TypeScript
Making Toronto's democracy more accessible.
Data scraped from various sites for housing data around the greater Toronto area (GTA). Scrapes happen daily and data is in both JSON and CSV formats. Free to use for analysis.
Bike Share Toronto 2021 Data Analysis & Interactive Visualization
A UI for the myttc.ca API, made with tailwind and jquery.
Working repository for the study of the "Motor Vehicle Collisions Involving Killed or Seriously Injured Persons" database from Toronto Open Data.
Project Explores Toronto Neighborhoods and Housing using a variety of data science and machine learning techniques.
Toronto bike share API library
A prepared environment for beginners to start on data science(Python, Jupyter and Pandas), with code retrieving real time Covid-19 case open data, and sample plotting scripts.
An analysis of Toronto Paramedic Services' response times to determine its efficacy as an emergency service.
Production-grade real-time transit analytics platform for Toronto TTC. Ingests GTFS Realtime feeds (vehicle positions & trip updates) every 30s, implements medallion architecture with MinIO data lake and Postgres warehouse. Airflow orchestration, Docker deployment. Built for analyzing delays, vehicle utilization, and service patterns.
🚗 TorontoParking: Revolutionizing 🌆 Toronto's parking game! Tap into open data, find
Statistical and geospatial analysis of Toronto Bike Share data and what it can tell us about the impact of changes to Toronto's bicycle infrastructure
Maze is an Android app (Android Auto compatible) that helps drivers avoid parking violations in real time. Drive with Waze 👻, Park with Maze 🚔
Working repository for CrashPoint ETL, a pipeline for processing and analyzing traffic collisions involving killed or seriously injured (KSI) persons from the City of Toronto
Identified trends in Major Crime Indicators data to recommend crime reduction strategies.
Notify drivers in range of Automated Speed Enforcement cameras in Toronto.
Press a button and be shown a random Toronto street bench. Have a seat.
This is a repository for a research article published in the Journal of Responsible Technology (Elsevier). The article uses Machine Learning and Generative AI to compare how both technologies achieve a particular result in crime suspects exercises.
aws lambda function to get collection schedule for a Toronto address
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