Examining Cash Bail by Zip Code in Philadelphia

Inspiration

This was our first hackathon so our primary goal was to create some sort of finished project and submit it. We thought creating heat maps from the PBF CSVs would be a good place to start, since they could clearly illustrate how cash bail impacts the community around you.

What it does

The python script strips relevant CSV data from the original files and calculates total monetary bails set, percent of bails paid, and percent of bails set as RoR by zipcode. We translate these into maps and put them onto a mock website you might be able to share with your community members

How we built it

I wrote the python scripts and took them into tableau, creating the heat maps. My partner embedded those maps into a website in a way they would be easy to share. We collaborated on the project using GitHub

Challenges we ran into

We wanted to look at bails set by crime but couldn't figure out a good way to parse that data. We also originally wanted to look at the data by amount charged rather than percentages, but thought percentages might lend some better insight

Accomplishments that we're proud of

I think we worked well as a team, we both brought unique skill sets to the table and applied them in a meaningful way. I'm also just happy we could submit something

What we learned

This was our first time collaborating using GitHub which was super helpful and good practice for the future. This was also the most data I've ever crunched at once, working out the kinks in that was definitely a learning experience.

What's next for Examining Cash Bail by Zip Code in Philadelphia

I would like to talk to an actual data scientist and see what conclusions they might be able to draw from this. I think there's some useful insight to be gained from what we did but also feel like we just scratched the surface.

Built With

• css3
• github
• html5
• javascript
• python
• tableau