Integrating social networks into large-scale urban simulations for disaster responses

Jiang, Na; Burger, Annetta; Crooks, Andrew; Kennedy, William G.

doi:10.1145/3423335.3428168

Cited by 3 publications

(1 citation statement)

References 13 publications

(11 reference statements)

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“…As discussed in Section 1, synthetic populations have been utilized by various modeling techniques such as microsimulation and agent-based modeling to explore a wide range of phenomena such as traffic dynamics, disaster response, and disease outbreaks (e.g., Wise et al, 2017; Eubank et al, 2004;Jiang et al, 2020;Xu et al, 2017). Hence, to demonstrate the utility generalizability of our synthetic population, the generated dataset was applied to three different agent-based modeling use cases exploring a variety of urban phenomena.…”

Section: Use Casesmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Use Casesmentioning

confidence: 99%

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

et al. 2022

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Simulating patterns of life: More representative time-activity patterns that account for context

Hubal

2023

Environment International

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The 3rd ACM SIGSPATIAL International Workshop on Geospatial Simulation

et al. 2021

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Space has long been acknowledged by researchers as a fundamental constraint which shapes our world. As technological changes have transformed the very concept of distance, the relative location and connectivity of geospatial phenomena have remained stubbornly significant in how systems function. At the same time, however, technology has advanced the science of geospatial simulation to bear on our understanding of how such systems work. While previous generations of scientists and practitioners were unable to gather spatial data or to incorporate it into models at any meaningful scale, new methodologies and data sources are becoming increasingly available to researchers, developers, users, and practitioners. These developments present new research opportunities for geospatial simulation.

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Integrating social networks into large-scale urban simulations for disaster responses

Cited by 3 publications

References 13 publications

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

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

Simulating patterns of life: More representative time-activity patterns that account for context

The 3rd ACM SIGSPATIAL International Workshop on Geospatial Simulation

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