Project Results: The team performed a feasibility study involving questions of technical accuracy and cost-effectiveness. Working mostly in R, they used a combination of web-scraping for data collection, machine-learning and text mining for data classification, and MTurk for human validation, and were able to construct a viable dataset for North Carolina.
They presented findings at an informal briefing of civic leaders and planning officials.
Partially funded by Counter Tools
Project Lead & Project Manager: Mike Dolan Fliss, Counter Tools
"Coming in, I had little knowledge about what data science research entailed. Participating in Data+ was a great step and helped me better realize my career goals. I learned a host of interdisciplinary skills - ranging from web scraping to survey design – that can definitely be applied to future projects." — Felicia Chen, Computer Science & Public Policy