Environmental Impact Data Collaborative

For two summers, I worked full-time as a researcher and data scientist at the McCourt School of Public Policy’s Massive Data Institute on the Environmental Impact Data Collaborative (EIDC). The EIDC enables researchers, community groups, and policymakers to analyze and visualize data in ways that help them make environmental policy more effective and just. This platform allows users to discover, access, merge, transform, analyze, visualize, and discuss hundreds of datasets with billions of rows of information. This project, funded in part by Bezos Earth Fund, is a collaboration between the Massive Data Institute at the McCourt School of Public Policy, Howard University, Morgan State University, and other Historically Black Colleges and Universities, and environmental justice think tanks and start-ups. My work primarily focused on analyzing the Biden Adminstration’s Justice40 Initiative, working with the Department of Health and Human Services on new implementation methods for the Low-Income Home Energy Assistance Program (LIHEAP), and with the District of Columbia’s Department of Energy and Environment.

Tutorial: Merge Census Data into Justice 40’s CEJST

Part of my time with the EIDC was devoted to analyzing the Biden Administration’s Justice 40 Initiative, a comprehensive environmental and racial justice policy approach. Many stakeholders were interested in comparing Census estimates for variables of interest to Justice 40’s disadvantaged designation. I set out to build a tool allowing policymakers and stakeholders to create their own analysis pulled data from the Census API, merged with the Justice 40 Climate and Environmental Justice Screening Tool, and displayed basic comparisons, all with essentially no coding required. This video is my tutorial for the tool.

Analysis of Gerrymander in Florida

A project with colleages at the McCourt School of Public Policy looking at how the 2020 redistricting of the state of Florida impacted voting in the state. We wanted to know how the partisan gerrymandering of the state impacted both voting patterns and representation in the following election. Using the Redistricting Data Hub API, we pulled spatial data for each of the congressional districts as well as election results and Census demographics. We then looked to see how many precincts that were redrawn into a new district saw a change in which political party represented them in Congress after the 2020 election. We also used a random forest model to analyze which features of a congressional district best predicted whether they were redrawn.

Fantasy Premier League

While it is not necessarily related to academics, my friends and I take Fantasy Premier League very seriously. As someone who enjoys computational methods, I created a platform for us to track our league with features that the basic FPL website does not track. This dashboard displays our league leaderboard and history with in-depth statistics and data visualizations built with connections to the FPL API.