Inspiration
Current navigation tools prioritize speed over safety and fail to provide actionable, context-aware information like lighting, crime prevalence, and nearby safe spaces, forcing people to adjust their lives around unsafe environments instead of navigating them confidently.
What it does
StreetSmart uses a manually created color-coded heatmap built from lighting and crime data to highlight safe and unsafe areas in the city. By analyzing these risk levels, the app generates safer walking routes and helps users avoid poorly lit or high-crime zones.
How we built it
Google Maps API Heat maps - taking data from Toronto Police Service Public Safety Data Portal Toronto Hydro Streetlight Map - We used the Google Maps API as the core of our mapping and routing system, allowing us to dynamically display routes and user locations. To visualize safety across the city, we generated custom heatmaps using crime incident data from the Toronto Police Service Public Safety Data Portal, converting the raw information into GeoJSON layers for accurate rendering. We also incorporated streetlight coverage data from the Toronto Hydro Streetlight Map to provide an additional safety context, layering it atop of our map and being part of the algorithm of safety.
Challenges we ran into
The most difficult challenges we faced was transmitting the data from online resources like Toronto crime reports and maps into usable and visual information. This was solved manually by using a geoJSON layer and creating a heatmap with grids. It allowed us to translate raw incident reports into a structured spatial format, which we then rendered on our map as a color-coded intensity grid. We also ran into several GitHub merge conflicts throughout development. Our team resolved these by carefully reviewing the conflicting sections, understanding the intent behind each change, and manually integrating both sets of code.
Accomplishments that we're proud of:
We are proud of the overall interface, and the effectiveness of our route-tracking algorithm. Starting the project, we didn’t think we would be able to make a program that was as functional and effective as we did. Layering in the heat maps, and integrating it with google maps to find the ‘safest’ route. So, we are ultimately most proud of creating a relatively unique and functional website that uses somewhat complex programming frameworks to solve a genuine need.
What we learned
Though it seemed daunting at the start, we realized that creating an interactive program with features like heat maps and APIs isn’t as intimidating as it may seem. Especially with 4 people, dividing bigger goals into various smaller subtasks makes it seem much more manageable. The start is often the hardest part, but after that progress tends to speed up. This was the first hackathon for 3 of the 4 people in the group, and we definitely came out much more comfortable and confident with programming then we came in.
What's next for Street Smart
Pedestrian safety is a very complex topic, one that can be measured using a variety of methods. We focused on some of the key statistics like general crime indexes and the levels of street lights. However, there are still more factors we can include to improve our algorithm as it comes up with the safest route for pedestrians. This could include, for example, lively areas with high commercial activity tend to be safe. We can also add more heat maps based on other specific variables. Though the core idea of StreetSmart wouldn’t change, these are some of the things that could further improve our project.
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