We’ve seen a trend in requests from potential clients. Organizations are bringing us complex, built-in-house Excel spreadsheets and asking us to convert them into web-based decision-support tools to make their data more useful, user-friendly, and secure.
We learned that OpenStreetMap is a great source of data for tracking SDG indicator 11.7.1. OSM provides data on public open or green spaces in cities that is similar in quality to more “official” sources like municipal Open Data portals, and collecting the data from OSM requires relatively little effort.
The adoption of Philadelphia’s Urban Forest Strategic Plan and Hunting Park’s Forestry Plan presents a unique opportunity to address the need for equitable tree planting and the reduction of heat vulnerability, as these two plans are the city’s first cohesive attempts to prioritize and manage its urban forest.
We calculated which congressional districts have become over or underpopulated since they were last drawn.
CurbLR is a promising new open data specification for curb regulation. We used it to visualize and analyze Philadelphia’s curb management approaches affect on traffic.
We pulled data from disparate hospital data sources to create a comprehensive national dataset of the hospital system for the COVID-19 response, using geocoding, proximity matching, and fuzzy string matching.
All around the country boundaries separate neighborhoods with high and low life expectancy. We used python to find the most extreme examples.
Scoring Philadelphia City Council districts on assets and risks using a weighted spatial analysis model in R and Python.
Learn to create a series of data-driven infographics in the context of a project that evaluates bus performance in each Philadelphia City Council District.