Utilizing R to analyze a large open dataset of historical public transit ridership data via a modern suite of tools. To improve the affordable housing situation in Charlottesville, new high density urban developments are needed. Where to build these new housing units isn’t always clear, but analyzing public transit data helps inform that decisions by showing how the community gets around. Forecasting future ridership growth by bus-route gives community planners additional data to inform their selection of optimal locations for future developments in the city. By communicating results via mapping visualizations and focusing on relevant community issues, this talk is aimed at anyone interested in urban development in Charlottesville. This talk will show off packages for time-series analysis (fable, tsibble), simple features mapping (sf) and data wrangling (tidyverse), but since the talk is results focused no coding knowledge or experience is required. Technical details of preparing data for time-series analysis and mapping spatial data, will be made available as part of the talk for audience that is more technically inclined and could be expanded on to another more code-centric talk if there is audience interest.
Nathan builds analysis pipelines for drug discovery data. He utilizes Shiny applications and SQL databases to give researchers tools to explore their own experimental results. For fun Nathan practices yoga, plays squash, and helps on civic data projects. He's been living in Belmont... Read More →
