Viktor Sköld Gustafsson, Tobias Andersson Granberg, Sofie Pilemalm, & Martin Waldemarsson. (2022). Managing Natural Hazards in Sweden – Needs for Improved Information and Decision Support Systems. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 376–384). Tarbes, France.
Abstract: This paper explores opportunities for information systems to support emergency response to multiple natural hazards. Interviews were conducted with 12 representatives from actors of the Swedish emergency response system about response to multiple natural hazards. Challenges and needs connected to five themes influencing the response effort were identified: Cooperation, Resource management, Command and control, Common operational picture, and Risk management. The results illuminate a lack of technology to support decisions and analyses during emergency response to both single and multiple natural hazards. Based on this, the paper suggests and discusses information systems and decision support tools to assist in satisfying the identified needs. The findings can inform policy makers in emergency response of where to concentrate the development of collaborative preparedness and response work, and the scientific community of future research directions.
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Jan Wendland, Christian Ehnis, Rodney J. Clarke, & Deborah Bunker. (2018). Sydney Siege, December 2014: A Visualisation of a Semantic Social Media Sentiment Analysis. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 493–506). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Sentiment Analyses are widely used approaches to understand and identify emotions, feelings, and opinion on social media platforms. Most sentiment analysis systems measure the presumed emotional polarity of texts. While this is sufficient for some applications, these approaches are very limiting when it comes to understanding how social media users actually use language resources to make sense of extreme events. In this paper, a Sentiment Analysis based on the Appraisal System from the theory of communication called Systemic Functional Linguistics is applied to understand the sentiment of event-driven social media communication. A prototype was developed to analyze Twitter data using the Appraisal System. This prototype was applied to tweets collected during and after the Sydney Siege 2014, a hostage situation in a busy café in Sydney. Because the Appraisal System is a theorised functional communication method, the results of this analysis are more nuanced than is possible with traditional polarity based sentiment analysis.
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Kaisa Riikka Ylinen, & Juha Pekka Kilpinen. (2018). Calibrating Ensemble Forecasts to Produce More Reliable Probabilistic Extreme Weather Forecasts. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1089–1097). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Accurate predictions of severe weather events are extremely important for society, economy, and environment. Due to the fact that weather forecasts are inherently uncertain, it is required to give information about forecast uncertainty to all users providing weather forecasts in probabilistic terms utilizing ensemble forecasts. Since ensemble forecasts tend to be under dispersive and biased, they need to be calibrated with statistical methods. This paper presents a method for the calibration of temperature forecasts using Gaussian regression, and the calibration of wind gust forecasts with a box-cox t-distribution method. Statistical calibration was made for the operational European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (ENS) forecasts for lead times from 3 to 360 hours. The verification results showed that calibration improved both temperature and wind gust ensemble forecasts. The probabilistic temperature forecasts were better after calibration over whole lead time scale, but the probabilistic wind gust forecasts up to 240 hours.
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Grégoire Burel, Lara S. G. Piccolo, Kenny Meesters, & Harith Alani. (2017). DoRES -- A Three-tier Ontology for Modelling Crises in the Digital Age. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 834–845). Albi, France: Iscram.
Abstract: During emergency crises it is imperative to collect, organise, analyse and share critical information between individuals and humanitarian organisations. Although dierent models and platforms have been created for helping these particular issues, existing work tend to focus on only one or two of the previous matters. We propose the DoRES ontology for representing information sources, consolidating it into reports and then, representing event situation based on reports. Our approach is guided by the analysis of 1) the structure of a widely used situation awareness platform; 2) stakeholder interviews, and; 3) the structure of existing crisis datasets. Based on this, we extract 102 dierent competency questions that are then used for specifying and implementing the new three-tiers crisis model. We show that the model can successfully be used for mapping the 102 dierent competency questions to the classes, properties and relations of the implemented ontology.
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Rafael de Sousa Ferreira Costa, Tharcisio Cotta Fontainha, Adriana Leiras, Hugo Tsugunobu Yoshida Yoshizaki, Paulo Gonçalves, & Abdon Baptista de Paula Filho. (2017). IT infrastructure at the Rio de Janeiro City Operations Center – the case of 2016 Olympic and Paralympic Games. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 739–751). Albi, France: Iscram.
Abstract: Rio Operations Center (COR) was the agency of Rio de Janeiro Prefecture responsible for monitoring the Rio 2016 Olympic and Paralympic Games operations, due to its role in the integrated management of the city operations. This paper presents a case study considering a brief theoretical reference and data collected through direct observations, interviews, internal documents and access to the systems and software used by COR. The analysis of the COR IT infrastructure and monitoring teams' preparation for the Olympics revealed a successful development of new teams and conflict solving practice. Despite the use of different sources of information and the development of specific systems for the event, the COR preparation faced some restrictions in analytical functions, security and integration among systems. Nevertheless, regionalization for monitoring and inter-agency coordination, cross-agency instant messaging, and a team for active monitoring of social media emerged as new practices, representing opening venues for future research.
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