Data-Driven In-Crisis Community Identification for Disaster Response and Management
Data-Driven In-Crisis Community Identification for Disaster Response and Management
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DOI：
10.1109/cic52973.2021.00021
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Yudong Tao;Renhe Jiang;Erik Coltey;Chuang Yang;Xuan Song;R. Shibasaki;Mei-Ling Shyu;Shu‐Ching Chen
中科院分区：
文献类型：
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作者：
Yudong Tao;Renhe Jiang;Erik Coltey;Chuang Yang;Xuan Song;R. Shibasaki;Mei-Ling Shyu;Shu‐Ching Chen
自二零一九年以来，全球受到全球大流行COVID-19的严重影响，数百万人受到不利影响。与此同时，飓风、野火和地震等自然灾害的强度和频率在过去几十年中有所增加。更大和更多样化的社区受到这些灾害的负面影响，它们可能会遇到社会和/或经济危机，而当自然灾害和流行病同时发生时，这种危机会进一步加剧。然而，传统的灾害应对和管理依赖于人类调查和个案研究，以确定这些处于危机中的社区及其问题，由于受影响人口的规模，这可能不是有效和高效的。在本文中，我们建议利用数据驱动的技术和人工智能的最新进展，在危机中的社区识别自动化，提高其可扩展性和效率。因此，社会可以向处于危机中的社区提供紧急援助，并可以实现及时的灾害应对和管理。提出了一种新的危机社区识别框架，该框架可以分为三个子任务：（1）社区检测，（2）危机状态检测，（3）社区需求和问题识别。此外，开放的问题和挑战，自动化在危机中的社区识别进行了讨论，以激励未来的研究和创新，在该地区。
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.