publications
journal articles
2024
- CEUSCommunity 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
- GIScienceShort 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
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, 2022
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}, }