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This content will become publicly available on December 1, 2025

Title: A Large-Scale Geographically Explicit Synthetic Population with Social Networks for the United States
Within the geo-simulation research domain, micro-simulation and agent-based modeling often require the creation of synthetic populations. Creating such data is a time-consuming task and often lacks social networks, which are crucial for studying human interactions (e.g., disease spread, disaster response) while at the same time impacting decision-making. We address these challenges by introducing a Python based method that uses the open data including that from 2020 U.S. Census data to generate a large-scale realistic geographically explicit synthetic population for America’s 50 states and Washington D.C. along with the stylized social networks (e.g., home, work and schools). The resulting synthetic population can be utilized within various geo-simulation approaches (e.g., agent-based modeling), exploring the emergence of complex phenomena through human interactions and further fostering the study of urban digital twins.  more » « less
Award ID(s):
2200173
PAR ID:
10630446
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Nature
Date Published:
Journal Name:
Scientific Data
Volume:
11
Issue:
1
ISSN:
2052-4463
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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