Data-Driven In-Crisis Community Identification for Disaster Response and Management

Yudong Tao, Renhe Jiang, Erik Coltey, Chuang Yang, Xuan Song, Ryosuke Shibasaki, Mei Ling Shyu, Shu Ching Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Since 2019, the world has been seriously impacted by the global pandemic, COVID-19, with millions of people adversely affected. This is coupled with a trend in which the intensity and frequency of natural disasters such as hurricanes, wildfires, and earthquakes have increased over the past decades. Larger and more diverse communities have been negatively influenced by these disasters and they might encounter crises socially and/or economically, further exacerbated when the natural disasters and pandemics co-occurred. However, conventional disaster response and management rely on human surveys and case studies to identify these in-crisis communities and their problems, which might not be effective and efficient due to the scale of the impacted population. In this paper, we propose to utilize the data-driven techniques and recent advances in artificial intelligence to automate the in-crisis community identification and improve its scalability and efficiency. Thus, immediate assistance to the in-crisis communities can be provided by society and timely disaster response and management can be achieved. A novel framework of the in-crisis community identification has been presented, which can be divided into three subtasks: (1) community detection, (2) in-crisis status detection, and (3) community demand and problem identification. Furthermore, the open issues and challenges toward automated in-crisis community identification are discussed to motivate future research and innovations in the area.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 7th International Conference on Collaboration and Internet Computing, CIC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages96-104
Number of pages9
ISBN (Electronic)9781665416252
DOIs
StatePublished - 2021
Externally publishedYes
Event7th IEEE International Conference on Collaboration and Internet Computing, CIC 2021 - Virtual, Online, United States
Duration: Dec 13 2021Dec 15 2021

Publication series

NameProceedings - 2021 IEEE 7th International Conference on Collaboration and Internet Computing, CIC 2021

Conference

Conference7th IEEE International Conference on Collaboration and Internet Computing, CIC 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/13/2112/15/21

Keywords

  • data analysis
  • disaster management
  • in-crisis community identification
  • multimodal data

ASJC Scopus subject areas

  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

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