Pedestrian fatalities are on the rise across the US, yet available data remains difficult to access and understand. I built MOSEY, a Streamlit app that visualizes car crashes with Walk Score to better understand street safety where I live. This talk shows how Python tools can transform civic engagement -- and what I learned from building a personal project to becoming part of collective action.
The motivation of this project came from seeing pedestrian fatalities close to home. As a concerned citizen and as a data scientist, I wanted to better understand pedestrian and cyclist safety in the city north of Boston. The Massachusetts Department of Transportation (MassDOT) Crash Portal provides extensive dashboards and statewide statistics, but it’s not intuitive to use. Concurrently, I thought about how to define walkability. Walk Score is often used, but it is based on if errands can be completed by foot, not safety. I wanted to visualize both together.
Using MassDOT data and the Walk Score API, I built MOSEY (Move On Safely EverYone) with Streamlit, geopandas, and folium. Users enter an address to see nearby car crashes from the last 10 years along with the Walk Score, for a fuller picture of walkability.
I joined local organizations that advocate for pedestrian and cyclist safety and I used my analysis to push for a proposed bike path. We recently conducted walk audits to understand the condition of sidewalks and streets, and we’ll synthesize this data to advance safer streets legislation. What started as a personal project has led me to engage with my community and I hope it inspires others to do the same.