@inproceedings{239453540f654a2d852d49a956363aea,
title = "Network science-based urban forecast dashboard",
abstract = "The urban environment is a highly dynamic and complex system. Urban dynamics in this complex system is largely reflected by the movement of people to and from Places of Interest (POIs) in the urban area. To better understand and plan for the city's various scenarios, there is a need to forecast urban dynamic conditions in terms of the possible movements of people across POIs. However, such predictions are not easy because an interdependent and living system is hard to forecast. In addition, the commuting and shopping of individuals in urban environments will show distinct patterns at various stages of disasters as compared to normal situations. This paper presents a network science-based urban forecast dashboard, in order to monitor urban events and identify the interdependencies that characterize urban dynamics. Behind the dashboard is a deep learning model that incorporates the network dynamics between POIs. The dashboard powers the prediction of urban dynamics from a network science perspective. This research calls for a unified framework to model the flow and network in the city. The dashboard visualizes how network science and urban science can mutually benefit from each other.",
keywords = "dashboard, events, network science, resilience, urban forecast",
author = "Jiaxin Du and Xinyue Ye and Galen Newman and David Retchless",
note = "Funding Information: This material is based upon work supported by the National Science Foundation under Grant Nos. 2112356 and 2122054 as well as the start-up grant 241117 from Texas A&M University. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation and Texas A&M University. We also acknowledge that safegraph provides the data. Publisher Copyright: {\textcopyright} 2022 ACM.; 5th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2022 ; Conference date: 01-11-2022",
year = "2022",
month = nov,
day = "1",
doi = "10.1145/3557916.3567822",
language = "English (US)",
series = "Proceedings of the 5th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2022",
publisher = "Association for Computing Machinery, Inc",
pages = "7--10",
editor = "Bandana Kar and Shima Mohebbi and Guangtao Fu and Xinyue Ye and Omitaomu, {Olufemi A.}",
booktitle = "Proceedings of the 5th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2022",
}