Meet Hunter Owens. Hunter is the Senior Data Scientist for the City of Los Angeles where he oversees the entire data team.
How’d you get into data?
It all started with fantasy baseball. I wanted to figure out who the best pitchers were, so I taught myself PHP and wrote regression programs to assess past and project future performance. In college, I had the opportunity to work for the Obama 2012 campaign on their digital analytics team. My experience there taught me how campaigns, and companies, have gotten incredibly smart about using predictive algorithms to drive outcomes. I wanted to help government do the same. I became part of the first cohort of the University of Chicago’s Data Science for Social Good Summer Fellowship. The fellowship pairs three to four students data and policy experience with a PhD. advisor to work with an government or nonprofit organization to produce data solutions. I later worked with the academic center that sponsors the fellowship to generate data on high-school dropout rates.
In my current position with the City, we help turn computer code into government code by, for example, showing city officials how many rent controlled apartments are currently being listed on AirBnB. We also turn government code into computer code through programs like strategically prioritizing which sidewalks to fix first based on factors like the concentration of local businesses. Our role sits at the intersection of machine learning and public policy. The coding side isn’t very complicated, but this work requires making determinations about the “public good.” Much of this determination comes from how elected officials define the public good, but it’s an ongoing conversation that requires public engagement.
What are your go-to analysis and visualization tools?
We’re a Python and R shop. We really prefer open source tools and free libraries like pandas. We post all of our code on GitHub for the public to review, suggest edits, and comment on the ways we decide to use data. We avoid using Excel because its analysis isn’t reproducible, and we want people to be able to replicate our results to hold us accountable.
What issue in Los Angeles do you think has the most potential for a data-driven solution?
There are lots of issues which would benefit from a data-based approach, but transportation is one that I find particularly compelling because it’s a data-rich environment and the political leadership in this arena understands the value of data. Seleta Reynolds, head of LADOT, described data as the “respiration of mobility.” Projects like Vision Zero, Mobility 2035, and the State’s climate goals all require strong data infrastructure. Our office helps to supply part of the technical muscle behind these projects and others.
What’s your favorite “data-story”?
The work we are doing with LADOT. We’ve installed 600 magnetic traffic sensors across different intersections in the City to track vehicular traffic flow to inform transportation policy. Now the goal is to track other kinds of mobility flows, from pedestrians traffic to ride sharing, biking, and alternative forms of transportation like Birds. We are using computer vision algorithms to automate counting processes of non-vehicular traffic. Making informed decisions means looking at total mobility.
Transportation is exciting because it’s an equity story as much as a technology story. What gets measured matters. There are huge impacts on people’s lives if we don’t count pedestrians or cyclists. The goal is to produce data that respects privacy and accurately reflects community mobility.
What advice do you have for someone looking to start using LA Counts datasets to tell their own stories?
It all starts with a bar chart. Poke around, and don’t be afraid to break things because it’s the only way to learn. Nobody’s going to show you exactly how to tell stories about your community. You have to take the initiative to tread new ground for the first time. Remember, don’t let the perfect be the enemy of the good or even the fine.