The U.S. Census Bureau and the Local Employment Dynamics (LED) Partnership in collaboration with the Council for Community and Economic Research (C2ER) and the Labor Market Information (LMI) Institute, welcomes Justin Krohn as he presents, “Decoding Commuting Distance Patterns Using Census Data.” This presentation will provide a brief overview of the Machine Learning model trained to predict the average commuting distance for each census tract in the U.S. Audience will gain insights into the methodology employed for calculating the average commuting distance using Census Longitudinal Employer-Household Dynamics Origin-Destination Statistics (LODES) dataset.

Presenter:
Justin Krohn is a Senior Research Project Analyst for the Center for Applied Research and Engagement Systems, for the University of Missouri-Extension. He works on data processing and visualization for various projects within the center. He also works to automate data flows from beginning to end including data download, processing, and mapping/visualization. He has a Bachelor in Science in Natural Resources from Washington State, a Master in Geography from Western Illinois University, with a Post-Baccalaureate certificate in Community Development and Planning and is currently pursuing his PhD in Geoinformatics.