Providing real-time assistance in disaster relief by leveraging crowdsourcing power

• Published in: Personal and ubiquitous computing Vol. 18; no. 8; pp. 2025 - 2034
• Main Authors: Yang, Dingqi, Zhang, Daqing, Frank, Korbinian, Robertson, Patrick, Jennings, Edel, Roddy, Mark, Lichtenstern, Michael
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2014; Springer; Springer Nature B.V; Springer Verlag
• Subjects: Applied sciences; Computer Science; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Crowdsourcing; Data acquisition; Design engineering; Disaster management; Disaster relief; Exact sciences and technology; Mobile Computing; Networking and Internet Architecture; Original Article; Personal Computing; Platforms; Real time; Software; Tasks; User Interfaces and Human Computer Interaction; Voting; Crowdsourcing; SUS; System Usability Scale; Disaster management platform; Crisis management; Rescue; Data analysis; Information credibility; Crowd; Community participation; Social network; Disaster; Real time; Remote work; Information source; Voting; Efficiency; Team work; Usability; Relief; Data gathering
• ISSN: 1617-4909; 1617-4917
• DOI: 10.1007/s00779-014-0758-3

Crowdsourcing platforms for disaster management have drawn a lot of attention in recent years due to their efficiency in disaster relief tasks, especially for disaster data collection and analysis. Although the on-site rescue staff can largely benefit from these crowdsourcing data, due to the rapidly evolving situation at the disaster site, they usually encounter various difficulties and have requests, which need to be resolved in a short time. In this paper, aiming at efficiently harnessing crowdsourcing power to provide those on-site rescue staff with real-time remote assistance, we design and develop a crowdsourcing disaster support platform by considering three unique features, viz., selecting and notifying relevant off-site users for individual request according to their expertise; providing collaborative working functionalities to off-site users; improving answer credibility via “crowd voting.” To evaluate the platform, we conducted a series of experiments with three-round user trials and also a System Usability Scale survey after each trial. The results show that the platform can effectively support on-site rescue staff by leveraging crowdsourcing power and achieve good usability .