As one of seven pilot programs to address environmental issues in Africa, Azavea trained student workers to label satellite imagery using GroundWork and created a machine learning model to identify tree canopy.
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.
We used three open datasets to calculate the number of people in the world who do not have sufficient access to a road in an effort to move forward the conversation on the Rural Access Indicator (RAI).
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.