This is an interactive Leaflet map of Healthy Ride access in Pittsburgh. It counts how many Healthy Ride stations are within a 10 minute bike ride of a given location.
The COVID-19 pandemic’s affect on commerce and mobility habits is well documented. For example, Apple publishes Mobility Trends reports about utilization of various transportation modes.
Lawrence Andrews asked me on Twitter if there had been a change in Health Ride usage after COVID-19. Would be interested to see this @healthyridepgh data to compare pre-covid (2019) and during (2020) — Lawrence Andrews (@lawrenceandrews) August 13, 2020 The {tidyverts} universe of packages from Rob Hyndman provides a lot of tools that let you interrogate time series data.
In this post I will use transit line and stop data from the WPRDC to map connections between census tracts. I access the census data via {tidycensus}, which contains information about the commuter connections between census tracts.
This post focuses on how many rivers Pittsburghers cross to get to work. I use the U.S. Census Bureau LEHD Origin-Destination Employment Statistics (LODES) dataset to draw lines between “home” census tracts and “work” census tracts, and then count how many “commuter lines” intersect with the 3 main rivers in Pittsburgh.
Note: high-res images of the main graphs from this post are available here, here, here, and here.
In this post I will use networks plots to analyze patterns of commuters in Allegheny County.
This post is about predicting demand for the Healthy Ride bike system in Pittsburgh. I wanted to try out Facebook’s prophet package and try to do some time series forecasting.
The New York Times recently published an article about zoning in U.S. cities, particularly single-unit detached residential housing. The article did not include Pittsburgh, so I downloaded the zone shapefile from the WPRDC and made my own map.