talk on conference website
Promoting community resilience requires population data that captures human dynamics with high spatial, temporal, and demographic fidelity. Likeness is a Python toolkit that supports these aims by creating agents informed by hundreds of individual-level attributes from census microdata and producing realistic simulations of their activity spaces.
Likeness is a Python implementation of the UrbanPop framework developed by Oak Ridge National Laboratory [1] that pairs attribute-rich synthetic populations with realistic simulations of human activity spaces at high spatial and temporal resolutions. A core principle of Likeness is the creation of "vivid" synthetic populations, in which individuals are described by hundreds of census microdata attributes covering demographics, socioeconomic status, housing, and health. Vivid synthetic populations are available for any location in the United States thanks to annual data releases from the American Community Survey (ACS) and its Public Use Microdata Sample (PUMS). Likeness consists of three core packages: `pymedm` (spatial allocation), `livelike` (population synthesis), and `actlike` (activity modeling).
The `pymedm` package is the building block for Likeness, supporting spatial allocation of longform survey responses from the PUMS to granular census geographies (e.g., block groups). `pymedm` is a Python port of Penalized Maximum-Entropy Dasymetric Modeling (P-MEDM), a method designed to accommodate a high volume of individual-level attributes from the PUMS [2, 3].
The `livelike` package is a population synthesizer that integrates `pymedm`/P-MEDM with the Census Microdata API. Interoperability between `livelike` and the Census Microdata API 1) provides a flexible means of generating model constraints for `pymedm`/P-MEDM and 2) supports querying and small-area estimation relative to specific population segments.
The `actlike` package optimally allocates agents from synthetic populations generated by `livelike` to points of interest (e.g., schools) along transportation networks [4, 5] with an integer program that "sends" individual agents from nighttime to daytime locations. This functionality enables researchers to examine human mobility and activity spaces more deeply.
We demonstrate Likeness by modeling human activity spaces in the Knoxville, TN Metropolitan Statistical Area with 2019 ACS 5-Year Estimates. For daytime locations we use K-12 schools with faculty and enrollment sizes obtained from the Homeland Infrastructure Foundation Level Database.
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*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).
Acknowledgement: This material is based upon the work supported by the U.S. Department of Energy under contract no. DE-AC05-00OR22725.*