J. Basak, Parama Bhaumik, Siuli Roy, S. Bandyopadhyay
{"title":"A Crowdsourcing based Information System Framework for Coordinated Disaster Management and Building Community Resilience","authors":"J. Basak, Parama Bhaumik, Siuli Roy, S. Bandyopadhyay","doi":"10.1145/3369740.3372730","DOIUrl":null,"url":null,"abstract":"Disaster management involves intensive coordination among multiple agencies like police, fire departments, public health, non-govt. agencies, including local volunteers/field workers. Accurate situational information about damage, resource needs, available resources etc., in the affected areas help the disaster management agencies in proper damage and need assessment and prepare suitable resource deployment plan. Crowdsourcing has become a popular approach for information collection where open crowds of people share multimodal situational information (text, images, audio, video etc.) about any event through social media posts. However, the authenticity and reliability of such posts are still debatable. Gathering situational data directly from the affected community (community-sourcing) can supplement social media posts to generate effective insights. In this paper, we attempt to design and develop a multiplatform disaster management information system where both social media-based crowdsourcing and community sourcing techniques are used to accumulate location-specific situational information. Subsequently, a coherent picture of the disaster situation is evolved through the integration of these local snapshots. Here, we explore how community participation, in the context of disaster management, can be enhanced through collaborative knowledge transaction, which eventually will lead towards the development of a resilient community. A field trial of our system is conducted involving a remote village community at Namkhana, West Bengal.","PeriodicalId":240048,"journal":{"name":"Proceedings of the 21st International Conference on Distributed Computing and Networking","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 21st International Conference on Distributed Computing and Networking","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3369740.3372730","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Disaster management involves intensive coordination among multiple agencies like police, fire departments, public health, non-govt. agencies, including local volunteers/field workers. Accurate situational information about damage, resource needs, available resources etc., in the affected areas help the disaster management agencies in proper damage and need assessment and prepare suitable resource deployment plan. Crowdsourcing has become a popular approach for information collection where open crowds of people share multimodal situational information (text, images, audio, video etc.) about any event through social media posts. However, the authenticity and reliability of such posts are still debatable. Gathering situational data directly from the affected community (community-sourcing) can supplement social media posts to generate effective insights. In this paper, we attempt to design and develop a multiplatform disaster management information system where both social media-based crowdsourcing and community sourcing techniques are used to accumulate location-specific situational information. Subsequently, a coherent picture of the disaster situation is evolved through the integration of these local snapshots. Here, we explore how community participation, in the context of disaster management, can be enhanced through collaborative knowledge transaction, which eventually will lead towards the development of a resilient community. A field trial of our system is conducted involving a remote village community at Namkhana, West Bengal.