A method to create a synthetic population with social networks for geographically-explicit agent-based models

Abstract

Geographically-explicit simulations have become crucial in understanding cities and are playing an important role in Urban Science. One such approach is that of agent-based modeling which allows us to explore how agents interact with the environment and each other (e.g., social networks), and how through such interactions aggregate patterns emerge (e.g., disease outbreaks, traffic jams). While the use of agent-based modeling has grown, one challenge remains, that of creating realistic, geographically-explicit, synthetic populations which incorporate social networks. To address this challenge, this paper presents a novel method to create a synthetic population which incorporates social networks using the New York Metro Area as a test area. To demonstrate the generalizability of our synthetic population method and data to initialize models, three different types of agent-based models are introduced to explore a variety of urban problems: traffic, disaster response, and the spread of disease. These use cases not only demonstrate how our geographically-explicit synthetic population can be easily utilized for initializing agent populations which can explore a variety of urban problems, but also show how social networks can be integrated into such populations and large-scale simulations.

Genomic Profiling and Spatial SEIR Modeling of COVID-19 Transmission in Western New York

“…While the use of the NYS Thruway data was useful to establish broad regional travel dynamics, we next sought to create a more finely tuned model of population-level movement dynamics within Western New York. To accomplish this task, we built an (7).Then, we constructed social networks (i.e., home, work and education) based on the small-world networks principle (18), where the synthetic individuals are connected based on living in the same household and either working in the same workplace or attending the same daycare/education institute. The rationale for these networks is that an individual might go to work, become exposed to COVID-19, and then go home and infect family members who in turn go to a school and infect students at school, propagating the viral infection through the network.…”

“…To develop a comprehensive understanding of COVID-19 dynamics, we employ a dual approach that combines spatially informed SEIR models with detailed genomic analysis of SARS-CoV-2 lineages (7). SEIR models provide a dynamic framework for simulating disease spread based on population movements and epidemiological parameters.…”

“…The work commute information for adults is consistent with the data from the U.S. Census Bureau's Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES). Full details of the synthetic population generation can be found in previous work by Jiang et, al(7).Then, we constructed social networks (i.e., home, work and education) based on the small-world networks principle(18), where the synthetic individuals are connected based on living in the same household and either working in the same workplace or attending the same daycare/education institute. The rationale for these networks is that an individual might go to work, become exposed to COVID-19, and then go home and infect family members who in turn go to a school and infect students at school, propagating the viral infection through the network.…”

Artificial Populations in Agent-Based Modeling

“…Groups can range from individual families to the entire population. The wide coverage of the size of such populations means there is a growing interest in using such populations in various fields [33,34,35,36,37,38,39,40]. These synthetic populations, often called "digital twins," include agents modeled with different demographic attributes such as age, gender, location and housing, among others.…”

Generating Attribute Similarity Graphs: A User Behavior-Based Approach from Real- Time Microblogging Data on Platform X

“…Another widely adopted approach entails the creation of synthetic social networks if the real-world network is unavailable. Synthetic data modelling involves the generation of synthetic data that replicates the characteristics of real-world data (Agrawal et al, 2024;Faez et al, 2022;Jiang et al, 2022;Nettleton, 2016;O'Neil & Petty, 2019). This allows researchers to examine and assess information without compromising con dentiality or being constrained by the unavailability of data.…”