Bridging population, activity spaces, and social interactivity with the Likeness software stack

Bridging population, activity spaces, and social interactivity with the Likeness software stack

Joseph V. Tuccillo[1] and James D. Gaboardi[1]

Human dynamics models, which address how people live, move, and interact, are critical to promote effective and equitable public service delivery. We present Likeness, a new Python software stack for modeling human dynamics built upon American Community Survey (ACS) and deployable for any location in the United States from 2016 onward. Likeness is an extension of Oak Ridge National Laboratory's UrbanPop framework, which uses highly spatially, temporally, and demographically resolved synthetic populations to produce daytime and nighttime population estimates. Likeness's core functionality provides a high-level interface for producing synthetic populations at census block group resolution using the ACS 5-Year Data API, Census Microdata API, and TIGER Web Mapping Service. Linking synthetic populations to open data sources related to the built environment (FEMA USA Structures, OpenStreetMap), Likeness encompasses the three facets of human dynamics: 1) residential location through the `livelike` package, 2) travel about real-world transportation networks through the `movelike` package, and 3) and activity allocation and social contact networks through the `actlike` package. We provide research highlights from various applications of Likeness from 2022 until present, including estimating student and worker commute behavior, understanding healthcare access among underserved populations during the COVID-19 pandemic, and assessing environmental justice issues related to urban extreme heat events. We also discuss the roadmap for continued development of Likeness, including matching synthetic populations to realistic residential locations, incorporating location-specific popularity curves from point of interest (POI) data, and building out scalable representations of multi-modal transportation infrastructure for enhancing knowledge of mobility/accessibility challenges. 

Copyright:  This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy.  The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

[1] Human Geography Group, Geospatial Science and Human Security Division, Oak Ridge National Laboratory