California Integrated Travel Project’s mission is to make transit across California simpler and more affordable. As part of this, we created an open source data warehouse to allow easy analysis of the travel data people often interact with every day.
In this talk we’ll discuss two big challenges we faced:
Creating tidy representations of daily schedules and payments data across 200 transit agencies. Enabling people with a range of backgrounds (R, SQL, and python) and experience to quickly analyze the data.
Tidy data allowed us to turn equal focus on agencies running a single bus, and those serving entire metropolitan areas.
Hunter Owens does data science in the public interest. Current at Caltrans, he has worked at the City of Los Angeles, the Center for Data Science and Public Policy, and Obama for America. I spend a lot of time thinking about cities, data pipelines and tacos.
