ArticleScientific data2024
A Large-Scale Geographically Explicit Synthetic Population with Social Networks for the United States.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Gender-responsive infectious disease models: Guidelines and systematic scoping review.Dialogues in health · 2026Review
- Synthetic data Using Population Profiles for cardiOvascular Risk facTors (SUPPORT) in mainland China.Scientific data · 2026Article
- High-resolution datasets of synthetic human contact network in 13 countries for infectious disease transmission.Data in brief · 2026Article
- A Novel Method for Generating Spatially Resolved Synthetic Populations for Health Impact Assessments in Vulnerable Populations.GeoHealth · 2026Article
- A visitor-enriched census in the U.S. cities using large-scale mobile positioning data.Scientific data · 2025Article
- A Large-Scale Geographically Explicit Synthetic Population with Social Networks for the United States.Scientific data · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.