Oleg Aulov, Adam Price, & Milton Halem. (2014). AsonMaps: A platform for aggregation visualization and analysis of disaster related human sensor network observations. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 802–806). University Park, PA: The Pennsylvania State University.
Abstract: In this paper, we describe AsonMaps, a platform for collection, aggregation, visualization and analysis of near real-time, geolocated quantifiable information from a variety of heterogeneous social media outlets in order to provide emergency responders and other coordinating federal agencies not only with the means of listening to the affected population, but also to be able to incorporate this data into geophysical and probabilistic disaster forecast models that guide their response actions. Hurricane Sandy disaster is examined as a use-case scenario discussing the different types of quantifiable information that can be extracted from Instagram and Twitter.
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G.P. Jayasiri, & Raj Prasanna. (2023). Citizen Science for supporting Disaster Management Institutions in Sri Lanka. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 77–88). Palmerston North, New Zealand: Massey Unversity.
Abstract: During 2016, 2017 and 2018, the country witnessed extreme rains which triggered flooding in several urban areas. The number of affected people by the 2018 floods was around 150,000 which shows a significant decrease compared to the events in 2016 and 2017. Several institutions provided their support via funding, relief, and rehabilitation mechanisms during these consecutive disasters. However, there are provisions which can further improve the performance of Disaster Management activities. Given this context, this study is carried out to investigate the application of citizen science concepts in several phases of Disaster Management in Sri Lanka. A scoping review supported by three case studies of floods was considered during the analysis. Limited participation of grass root level communities in decision-making and disaster planning, and issues related to data management are some of the main challenges identified in this study. Participatory mapping, Co-Design Projects, hackathons, and crowdfunding are some of the observed citizen science concepts which can be used to address the challenges and strengthen the Disaster Management activities in Sri Lanka. Further studies including interviews and questionnaire surveys were recommended to justify the findings.
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Nick LaLone, Andrea H. Tapia, Nathan A. Case, Elizabeth MacDonald, Michelle Hall, & Matt Heavner. (2015). HYBRID COMMUNITY PARTICIPATION IN CROWDSOURCED EARLY WARNING SYSTEMS. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: In this paper we present Aurorasaurus: a website, a mobile application, and a citizen science initiative that allows a community of users to report and verify sightings of the Aurora Borealis. Through ad-hoc data indirectly offered through social media, a community of citizen scientists verify sightings of the Aurora Borealis. These verified data are tested against currently existing aurora-forecasting models. The insights these data provide are transformed into map and text-based forms. In addition, notifications are sent to interested participants in a timely manner. This is a design test-bed for an early warning system (EWS) that is capable of detecting and communicating the earliest signs of disaster to community members in near real time. Most importantly, this system incorporates community participation in improving the quality of data mined from Twitter and direct community contributions.
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Nicolas LaLone, & Andrea Tapia. (2016). Three Lessons from Aurorasaurus about Public Facing Information System Design. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: Response-focused information systems have the same data processing needs as citizen science initiatives. We present three lessons learned over a three-year period with a public facing information system devoted to early warning and event detection that will benefit designers of similar systems. First, we urge those creating information systems inside of crisis response to look for proxy events that will serve as an inexpensive means through which to pursue proof-of-concept or to explore pre-existing fully tested products. Second, we urge information system designers to engage the communities and gatekeepers of enthusiast communities surrounding the event that information system is meant to serve. It will not only help development, but also increase the chances of that system?s success. Finally, aiming for self-interest rather than event-interest will allow users to feel involved; ultimately aiding participation and retention.
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Rémy Bossu, Robert Steed, Gilles Mazet-Roux, Caroline Etivant, & Fréderic Roussel. (2015). THE EMSC TOOLS USED TO DETECT AND DIAGNOSE THE IMPACT OF GLOBAL EARTHQUAKES FROM DIRECT AND INDIRECT EYEWITNESSES? CONTRIBUTIONS. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: This paper presents the strategy and operational tools developed and implemented at the Euro-Mediterranean Seismological Centre (EMSC) to detect and diagnose the impact of global earthquakes within minutes by combining « flashsourcing » (real time monitoring of website traffic) with social media monitoring and crowdsourcing.
This approach serves both the seismological community and the public and can contribute to improved earthquake response. It collects seismological observations, improves situation awareness from a few tens of seconds to a couple of hours after earthquake occurrence and is the basis of innovative targeted real time public information services.
We also show that graphical input methods can improve crowdsourcing tools both for the increasing use of mobile devices and to erase language barriers. Finally we show how social network harvesting could provide information on indirect earthquake effects such as triggered landslides and fires, which are difficult to predict and monitor through existing geophysical networks.
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Andrea H. Tapia, Nicolas LaLone, Elizabeth MacDonald, Reid Priedhorsky, & Hall Hall. (2014). Crowdsourcing rare events: Using curiosity to draw participants into science and early warning systems. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 135–144). University Park, PA: The Pennsylvania State University.
Abstract: This research presents a centralized boundary object website and mobile app focused on allowing participants to participate in developing an early warning system through space weather and the beauty of the aurora borealis. Because of the beauty and majesty of auroral activity, people will seek information about when and where these unpredictable events occur. This activity, commonly referred to as nowcasting, can be combined with scientific data collected from observatories and satellites and serve as an early warning system with potentially far greater accuracy and timeliness than the current state of the art. We believe that long-term engagement with a citizen science tool will help bridge the many social worlds surrounding the aurora borealis and lead to the development of an early warning system that may correlate the visibility of the northern lights to violent space weather. We hope this will lead to other real time crowdsourced early warning systems in the future.
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