Inspiration

With outside travel and tourism on the rise in the aftermath of COVID, parking will become a greater challenge; both in finding a spot and avoiding fines. We also saw an opportunity to explore the potential injustices caused to lower-income communities by fixed fines and fees.

What it does

Our web app has multiple functionalities. We used a scrollytelling map to display the injustices caused by parking fines in poorer areas in DC. Users can also enter an address in DC where they would like to park and our app will display parking meters near that location that avoid areas where there have historically been parking fines. This will allow users to pick safer parking spots without worrying about fines.

How we built it

We got the datasets for parking meters, parking violations, and demographics using Open Data DC, which is publicly available data from DC. We then generated GeoJSON files that were used with Mapbox in our React frontend. We also used Chakra UI for the components besides the map on the frontend. In order to get the location given the address, we used Google Maps geocoding which returned us a latitude and longitude to then use in our algorithm.

Challenges we ran into

When making the algorithm for safe parking meters, we initially had trouble making an efficient algorithm. The original algorithm would edit the meter list as we went through it, making the loop almost infinite. Our new algorithm used buffers around each violation, allowing us to filter out more meters that could be in the vicinity of violations, ensuring that whatever meter got picked would be farther from the violations.

Accomplishments that we're proud of

We were able to get scrollytelling working, allowing us to make the data analysis portion of our project more understandable. We were also proud of the fact that we got 3D rendering working in Mapbox.

What we learned

We learned a lot about how to convey data in a visually appealing way and how to explain our findings intuitively. We also learned about the importance of efficiency when dealing with large data.

What's next for Park Alert

In the future, we see Park Alert integrating real-time data on whether parking spaces are occupied or not so users could get a more concentrated number of options. This service could also be expanded to more cities with public data.

Share this project:

Updates