An average driver in Seattle spends 58 hours a year looking for parking. To reduce this burden, city officials must first understand on-street parking occupancy and parking behaviors. The challenge is that pay station transaction data doesn’t reflect actual parking occupancy throughout the year. To reconcile this problem, we leveraged publicly available information assets to build statistical models to predict paid parking occupancy in Belltown North. Our machine learning model, analysis of factors related to occupancy, and documented process is moving the needle toward a citywide system of policies and driver tools that streamline the parking experience.

The City of Seattle Human Services Department (HSD) contracts with community organizations to provide housing & health services to people experiencing homelessness. Previously, HSD staff pieced together program & contract data from many sources. This is a time-consuming and frustrating process, but this information is critical for informing policy and highly visible through media outreach. To meet their needs, we produced a data warehouse and reporting system that captures institutional knowledge, automates data ingest, and makes data analysis more streamlined and trustworthy. We hope to help HSD tell a more complete story about Seattle’s service to its most vulnerable populations.