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A collection of WPRDC-relevant tools and data analyses.


  • WPRDC Property Dashboard - The Regional Data Center's Property Dashboard integrates data from multiple data sources and provides it all in one place. We're happy to announce that the beta version of the tool is now live, and we built it in-house.
  • WPRDC's Parcels n'at - Parcels n'at allows you to pull a variety of data by neighborhood, municipality, or even for a user-defined area using some of the embedded drawing tools.

Data analyses by dataset

311 Data

Air-Quality Data

Carnegie Library Wi-Fi Use Data

Crash Data

Crime Data

Dog-License Data

  • nrfulton/pittdoggos - Nathan Fulton's Python scripts for analyzing county (but not city) dog-license data and a simple Web page for searching dogs by name.

Fatal Accidental Overdoses Data

Healthy Ride Data

Examples demonstrating different skills


Learning Python/Jupyter notebooks/data analysis

  • WPRDC/urban-informatics-and-visualization - A WPRDC fork of Paul Waddell's course materials for Urban Informatics and Visualization. These Jupyter notebooks cover Python/Jupyter fundamentals, cleaning/manipulating/analyzing/visualizing/mapping data, and using Web APIs for getting and posting data.

Making bar charts

Making SQL queries on WPRDC data


Pandas dataframes

Pulling WPRDC data through the CKAN API

Transforming from a long-format data table to a wide-format data table


Learning R

  • Tutorial on using R to analyze pothole data - Material from a workshop run by Conor Tompkins, using Pittsburgh's 311 data on potholes to teach the basics of using R (including manipulating data and making charts and maps).

Principal component analysis in R

Time-series forecasting in R

  • Forecasting Healthy Ride ridership - A blog post describing the use of the prophet package to extract seasonality features and predict the variation in Healthy Ride bike-ride counts.

CKAN API usage under R + debugging a broken SQL query

Handling Census data + network analysis in R

  • Analyzing commuter patterns in Allegheny County - Conor Tompkins describes how to use R to manipulate Census data about commuting to study and map the most common starting and ending points for travelling between work and home, revealing that a huge number of people commute to downtown Pittsburgh with sizable numbers travelling to work in Oakland, Findlay Township, Moon Township, and Robinson Township.

Useful tools and code libraries

General tools for working with data

  • saulpw/visidata - A terminal spreadsheet multitool for discovering and arranging data. (It's basically a Swiss Army chainsaw for manipulating tabular data.)

WPRDC-specific tools

  • Downstream - An online tool for downloading any tabular data on the WPRDC data portal (even very large tables) as a CSV or TSV file. Small- to medium-sized tables may also be downloaded as Excel files.

Code for dealing with Census data

  • ljwolf/cenpy - A Python library for exploring and querying the US Census API and returning Pandas DataFrames.
  • datamade/census - A Python wrapper for the U.S. Census API.
  • datadesk/census-error-analyzer - Given two Census values and the corresponding margins of error, this Python library can do an analysis to determine whether there is a statistically significant difference between them.

Code for geospatial manipulation

  • mggg/maup - "The geospatial toolkit for redistricting data", a Python package designed to facilitate conversion between spatial regions used for elections (e.g., precincts) and spatial regions used by the Census to collect demographic information (e.g., blocks).

Code/service for calculating routes between locations


Please contribute things that could be useful to others using WPRDC data, including scripts for data cleaning or analyzing data, Jupyter notebooks for particular datasets, and tools for manipulating and visualizing the data.


List of WPRDC-relevant tools and data analyses



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