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Likeness: a toolkit for connecting the social fabric of place to human dynamics

by Joseph V Tuccillo, James D Gaboardi
Publication Type
Conference Paper
Book Title
Proceedings of the 21st Python in Science Conference (SciPy 2022)
Publication Date
Page Numbers
125 to 135
Conference Name
21st annual Scientific Computing with Python Conference (SciPy)
Conference Location
Austin, Texas, United States of America
Conference Sponsor
Conference Date

The ability to produce richly-attributed synthetic populations is key for understanding human dynamics, responding to emergencies, and preparing for future events, all while protecting individual privacy. The Likeness toolkit accomplishes these goals with a suite of Python packages: pymedm/pymedm\_legacy, livelike, and actlike. This production process is initialized in pymedm (or pymedm\_legacy) that utilizes census microdata records as the foundation on which disaggregated spatial allocation matrices are built. The next step, performed by livelike, is the generation of a fully autonomous agent population attributed with hundreds of demographic census variables. The agent population synthesized in livelike is then attributed with residential coordinates in actlike based on block assignment and, finally, allocated to an optimal daytime activity location via the street network. We present a case study in Knox County, Tennessee, synthesizing 30 populations of public K–12 school students \& teachers and allocating them to schools. Validation of our results shows they are highly promising by replicating reported school enrollment and teacher capacity with a high degree of fidelity.