Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/70832

Community Dynamics in Smart City Digital Twins: A Computer Vision-based Approach for Monitoring and Forecasting Collective Urban Hazard Exposure

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dc.contributor.author Mavrokapnidis, Dimitri
dc.contributor.author Mohammadi, Neda
dc.contributor.author Taylor, John
dc.date.accessioned 2020-12-24T19:21:21Z
dc.date.available 2020-12-24T19:21:21Z
dc.date.issued 2021-01-05
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/70832
dc.description.abstract Urbanization and the growth of human population are leading to increased complexities in the interactions of citizens with public spaces, creating cities that must be more responsive to community dynamics. Despite the critical need for community-based city management, current decision-making approaches are often uninformed by the collective behavior of communities across time and in different locations. Here, we introduce a computer vision-based monitoring and forecasting approach for Smart City Digital Twins (SCDT) that enables integrating collective behavior into the spatiotemporal assessments of exposure to urban heat stress. Our results from a pilot case study in the city of Columbus, GA, demonstrate the significance of integrating community dynamics into monitoring and forecasting urban hazard exposure. This ongoing study highlights how SCDT can make community dynamics more accessible to and responsive by city managers.
dc.format.extent 9 pages
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Smart Building, Smart Community, and Smart City Digital Twins
dc.subject computer vision
dc.subject digital twins
dc.subject smart cities
dc.subject urban hazard exposure
dc.title Community Dynamics in Smart City Digital Twins: A Computer Vision-based Approach for Monitoring and Forecasting Collective Urban Hazard Exposure
dc.identifier.doi 10.24251/HICSS.2021.220
prism.startingpage 1810
Appears in Collections: Smart Building, Smart Community, and Smart City Digital Twins


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