Recap: San Francisco Public Data Hackathon
Intro
Last weekend, Hex and Modelbit hosted a San Francisco public data hackathon at our office downtown. What the heck is a “public data hackathon”? Here’s our magical recipe:
Pull down a few hundred million rows of data from the San Francisco Open Data portal.
Clean (not too much) and load the data into a publicly accessible Snowflake warehouse.
Order a ton of pizza from Pie Punks.
Give free Hex and Modelbit accounts to a hundred curious data people, and set them loose.
We wanted people to discover new insights about the city they live and work in, understand their daily backdrop in a different way, and build data experiences that inspire everyone to make this beautiful city even better (I put an official moratorium on 💩 maps). At the end of the day, twenty teams submitted finished projects that ranged from Vision Pro data apps to careful analysis of sidewalk width variance.
In this blog, I’ll highlight the two winning projects.
Winning project: Road Risk Radar
Anup Mantri, Vinit Agrawal, Lourdes Lopez, Joshua Sorkin, Shayan Guha.
The winning team looked at data on traffic accidents in the city over the last 18 years, and built a model that analyzes a route through the city to provide an aggregated risk level for every intersection that route crosses.
Their project has two components: A data visualization tool and the actual model.
Before diving into model training, the team built a data app in Hex to visualize the traffic injury data. Running directly off the public datasets in Snowflake, it lets you drill in by street and vehicle type, and scrub through time to see specific trends.
After exploring the city and validating some initial assumptions, they built a machine learning model to predict accident risk anywhere in the city. Their model (trained and deployed on Modelbit, using Hex’s Modelbit integration) takes in multiple features: [Day of week, Month, Time, Weather, and lat/lon] and outputs a “risk level”, computed from a predicted incident ratio and overall injury / death forecast. There’s some neat innards to this model, like a clever block-level lat/lon binning to make it more performant, and the inclusion of weather conditions.
Taking it one step further, they deployed this model using Modelbit and built a quick app that lets users make informed decisions about their routes through the city.
Their completed app:
Takes a Start and a Destination location.
Computes a Google Maps route between them.
Returns an overall and intersection-level risk score for the entire route.
This is a really impressive project, completed in just a couple of hours using brand new tools— very impressive and complete work.
Runner-up project: SF Data-Land
Jess Cherny, Mattie Blue
Our runner-up project combined two distinct sources of data: Movie film locations and SF Muni bus routes. They built an app that helps you:
Locate which famous film locations were filmed on your bus route of choice.
Locate all the bus routes that take you to the locations from your movie of choice.
Find transit directions from Hex's office to your SF film location of choice.
My childhood bus route (38 —> 43) is loaded with Hollywood points of interest! But I promise that’s not why this team won the second place prize— behind the scenes of these beautiful maps, there’s actually a lot going on: requests to the Google Maps API, clever joining of data from a few different sources, and some quick geospatial distance calculations that are performant enough for the Hex app to feel smooth and interactive.
This is a great use of Hex, and a nice clean interface to some surprisingly complex data manipulation. And a lovely story to boot.
See you next time?
This is just our first event. If that magical blueprint (remember: millions of rows of public data, unreasonable amounts of pizza, and cool data tools) sounds enticing to you, we’d love to see you next time. Join our slack community and we'll keep you posted on events to come!
And if you want to get a jump on things... you can pop into Hex and explore against our DEMO_DATA.SF_PUBLIC_DATA schema right now. Here’s a project you can fork that shows some recent lobbyist activities:
See you soon 🖖.