Intro In this post I will use the Mapbox API to calculate metrics for major commuter routes in Allegheny County. The API will provide the distance and duration of the trip, as well as turn-by-turn directions.
This will be a quick post on cumulative bird observations in Allegheny County. Cumulative graphs show overall trends, seasonality, and quirks in how the data was recorded. They are also fun to turn into animated gifs with gganimate.
As part my work on transit lines in Allegheny County, I am interested in which transit lines are most efficient, in terms of residents and jobs served. This is possible with the Port Authority transit line datasets hosted on the WPRDC and data from the Census.
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.
In this post I will show how to create animated graphs that illustrate the increase in buildings in Allegheny County.
One caveat about the data: it only includes parcels that were sold at some point.
In this post I will be modeling house (land parcel) sales in Pittsburgh. The data is from the WPRDC’s Parcels n’at dashboard.
The goal is to use linear modeling to predict the sale price of a house using features of the house and the property.