publications
journal articles
2024
- A Large-Scale Geographically Explicit Synthetic Population with Social Networks for the United StatesNa Jiang, Fuzhen Yin, Boyu Wang, and Andrew CrooksScientific Data, 2024
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.
@article{jiang2024large, title = {A Large-Scale Geographically Explicit Synthetic Population with Social Networks for the United States}, author = {Jiang, Na and Yin, Fuzhen and Wang, Boyu and Crooks, Andrew}, journal = {Scientific Data}, volume = {11}, number = {1}, pages = {1204}, year = {2024}, publisher = {Nature Publishing Group UK London}, doi = {10.1038/s41597-024-03970-1}, } - Community Resilience to Wildfires: A Network Analysis Approach by Utilizing Human Mobility DataQingqing Chen, Boyu Wang, and Andrew CrooksComputers, Environment and Urban Systems, 2024
Disasters have been a long-standing concern to societies at large. With growing attention being paid to resilient communities, such concern has been brought to the forefront of resilience studies. However, there is a wide variety of definitions with respect to resilience, and a precise definition has yet to emerge. Moreover, much work to date has often focused only on the immediate response to an event, thus investigating the resilience of an area over a prolonged period of time has remained largely unexplored. To overcome these issues, we propose a novel framework utilizing network analysis and concepts from disaster science (e.g., the resilience triangle) to quantify the long-term impacts of wildfires. Taking the Mendocino Complex and Camp wildfires - the largest and most deadly wildfires in California to date, respectively - as case studies, we capture the robustness and vulnerability of communities based on human mobility data from 2018 to 2019. The results show that demographic and socioeconomic characteristics alone only partially capture community resilience, however, by leveraging human mobility data and network analysis techniques, we can enhance our understanding of resilience over space and time, providing a new lens to study disasters and their long-term impacts on society.
@article{CHEN2024102110, title = {Community Resilience to Wildfires: A Network Analysis Approach by Utilizing Human Mobility Data}, journal = {Computers, Environment and Urban Systems}, volume = {110}, pages = {102110}, year = {2024}, issn = {0198-9715}, doi = {10.1016/j.compenvurbsys.2024.102110}, url = {https://www.sciencedirect.com/science/article/pii/S0198971524000395}, author = {Chen, Qingqing and Wang, Boyu and Crooks, Andrew}, keywords = {Wildfire, Community resilience, Network analysis, Resilience triangle, Human mobility data}, }
conference & workshop papers
2023
- CSSSA Best Paper AwardGeographically-Explicit Synthetic Populations for Agent-Based Models: A Gallery of ApplicationsNa Jiang, Andrew Crooks, Fuzhen Yin, and Boyu WangIn Conference of the Computational Social Science Society of the Americas, 2023
Best Paper Award
Over the last two decades, there has been a growth in the applications of geographically-explicit agent-based models. One thing such models have in common is the creation of synthetic populations to initialize the artificial worlds in which the agents inhabit. One challenge such models face is that it is often difficult to create reusable geographically-explicit synthetic populations with social networks. In this paper, we introduce a Python based method that generates a reusable geographically-explicit synthetic population dataset along with its social networks. In addition, we present a pipeline for using the population datasets for model initialization. With this pipeline, multiple spatial and temporal scales of geographically-explicit agent-based models are presented focusing on Western New York. Such models not only demonstrate the utility of our synthetic population on commuting patterns but also how social networks can impact the simulation of disease spread and vaccination uptake. By doing so, this pipeline could benefit any modeler wishing to reuse synthetic populations with realistic geographic locations and social networks.
@inproceedings{jiang2023geographically, title = {Geographically-Explicit Synthetic Populations for Agent-Based Models: A Gallery of Applications}, author = {Jiang, Na and Crooks, Andrew and Yin, Fuzhen and Wang, Boyu}, booktitle = {Conference of the Computational Social Science Society of the Americas}, year = {2023}, organization = {Springer}, doi = {10.1007/978-3-031-64193-0_10}, url = {https://doi.org/10.1007/978-3-031-64193-0_10} } - GIScience Short PaperAgent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep LearningBoyu Wang, and Andrew CrooksIn Proceedings of the 12th International Conference on Geographic Information Science (GIScience 2023), 2023
Short Paper
People’s opinions are one of the defining factors that turn spaces into meaningful places. Online platforms such as Yelp allow users to publish their reviews on businesses. To understand reviewers’ opinion formation processes and the emergent patterns of published opinions, we utilize natural language processing (NLP) techniques especially that of aspect-based sentiment analysis methods (a deep learning approach) on a geographically explicit Yelp dataset to extract and categorize reviewers’ opinion aspects on places within urban areas. Such data is then used as a basis to inform an agent-based model, where consumers’ (i.e., agents’) choices are based on their characteristics and preferences. The results show the emergent patterns of reviewers’ opinions and the influence of these opinions on others. As such this work demonstrates how using deep learning techniques on geospatial data can help advance our understanding of place and cities more generally.
@inproceedings{10.4230/LIPIcs.GIScience.2023.81, author = {Wang, Boyu and Crooks, Andrew}, title = {Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning}, booktitle = {Proceedings of the 12th International Conference on Geographic Information Science (GIScience 2023)}, pages = {81:1--81:6}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, isbn = {978-3-95977-288-4}, issn = {1868-8969}, year = {2023}, volume = {277}, editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, url = {https://drops.dagstuhl.de/opus/volltexte/2023/18976}, annote = {Keywords: aspect-category sentiment analysis, consumer choice, agent-based modeling, online restaurant reviews}, doi = {10.4230/LIPIcs.GIScience.2023.81}, }
2022
- Mesa-Geo: A GIS Extension for the Mesa Agent-Based Modeling Framework in PythonBoyu Wang, Vincent Hess, and Andrew CrooksIn Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, Seattle, Washington, 2022
Best Paper Award
Mesa is an open-source agent-based modeling (ABM) framework implemented in the Python programming language, allowing users to build and visualize agent-based models. It has been used in a diverse range of application areas over the years ranging from biology to workforce dynamics. However, there has been no direct support for integrating geographical data from geographical information systems (GIS) into models created with Mesa. Users have had to rely on their own implementations to meet such needs. In this paper we present Mesa-Geo, a GIS extension for Mesa, which allows users to import, manipulate, visualise and export geographical data for ABM. We introduce the main components and functionalities of Mesa-Geo, followed by example applications utilizing geographical data which demonstrates Mesa-Geo’s core functionalities and features common to agent-based models. Finally, we conclude with a discussion and outlook on future directions for Mesa-Geo.
@inproceedings{10.1145/3557989.3566157, author = {Wang, Boyu and Hess, Vincent and Crooks, Andrew}, title = {Mesa-Geo: A GIS Extension for the Mesa Agent-Based Modeling Framework in Python}, year = {2022}, isbn = {9781450395373}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3557989.3566157}, doi = {10.1145/3557989.3566157}, booktitle = {Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation}, pages = {1–10}, numpages = {10}, keywords = {complex systems, agent-based modeling (ABM), geographic information systems (GIS), Python}, location = {Seattle, Washington}, series = {GeoSim '22}, }