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Michael Ammann, Tuomas Peltonen, Juhani Lahtinen, Kaj Vesterbacka, Tuula Summanen, Markku Seppänen, et al. (2010). KETALE Web application to improve collaborative emergency management. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: KETALE is a database and web application intended to improve the collaborative decision support of the Finnish Radiation and Nuclear Safety Authority (STUK) and of the Finnish Meteorological Institute (FMI). It integrates distributed modeling (weather forecasts and dispersion predictions by FMI, source term and dose assessments by STUK) and facilitates collaboration and sharing of information. It does so by providing functionalities for data acquisition, data management, data visualization, and data analysis. The report outlines the software development from requirement analysis to system design and implementation. Operational aspects and user experiences are presented in a separate report.
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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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Basanta Chaulagain, Aman Shakya, Bhuwan Bhatt, Dip Kiran Pradhan Newar, Sanjeeb Prasad Panday, & Rom Kant Pandey. (2019). Casualty Information Extraction and Analysis from News. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: During unforeseen situations of crisis such as disasters and accidents we usually have to rely on local news reports for the latest updates on casualties. The information in such feeds is in unstructured text format, however, structured data is required for analysis and visualization. This paper presents a system for automatic extraction and visualization of casualty information from news articles. A prototype online system has been implemented and tested with local news feed of road accidents. The system extracts information regarding number of deaths, injuries, date, location, and vehicles involved using techniques like Named Entity Recognition, Semantic Role Labeling and Regular expressions. The entities were manually annotated and compared with the results obtained from the system. Initial results are promising with good accuracy overall. Moreover, the system maintains an online database of casualties and provides information visualization and filtering interfaces for analysis.
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Benaben, F., Fertier, A., Cerabona, T., Moradkhani, N., Lauras, M., & Montreuil, B. (2023). Decision Support in uncertain contexts: Physics of Decision and Virtual Reality. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 54–66). Omaha, USA: University of Nebraska at Omaha.
Abstract: Virtual Reality (VR) is often used for its ability to mimic reality. However, VR can also be used for its ability to escape reality. In that case, on the one hand VR provides a visualization environment where the user’s senses are still in a familiar context (one can see if something is in front, behind, up, down, far or close), yet on the other hand, VR allows to escape the usual limits of reality by providing a way to turn abstract concepts into concrete and interactive objects. In this paper, the dynamic management of a complex industrial system (a supply chain) is enabled in a VR prototypical environment, through the management of a physical trajectory that can be deflected by the impact of any potentialities such as risks or opportunities, seen as physical objects in the performance space.
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Shubham Gupta, & Craig A. Knoblock. (2010). Building geospatial mashups to visualize information for crisis management. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In time-sensitive environments such as disaster management, decision-making often requires rapidly gathering the information from diverse data sources and then visualizing the collected information to understand it. Thus, it is critical to reduce the overhead in data integration and visualization for efficient decision-making. Geospatial mashups can be an effective solution in such environments by providing an integrated approach to extract, integrate and view diverse information. Currently, mashup building tools exist for creating mashups, but none of them deal with the issue of data visualization. An improper visualization of the data could result in users wasting precious time to understand the data. In this paper, we introduce a programming-by-demonstration approach to data visualization in geospatial mashups that allows the users to customize the data visualization.
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Muhammad Imran, Shady Elbassuoni, Carlos Castillo, Fernando Díaz, & Patrick Meier. (2013). Extracting information nuggets from disaster- Related messages in social media. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 791–801). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Microblogging sites such as Twitter can play a vital role in spreading information during “natural” or man-made disasters. But the volume and velocity of tweets posted during crises today tend to be extremely high, making it hard for disaster-affected communities and professional emergency responders to process the information in a timely manner. Furthermore, posts tend to vary highly in terms of their subjects and usefulness; from messages that are entirely off-topic or personal in nature, to messages containing critical information that augments situational awareness. Finding actionable information can accelerate disaster response and alleviate both property and human losses. In this paper, we describe automatic methods for extracting information from microblog posts. Specifically, we focus on extracting valuable “information nuggets”, brief, self-contained information items relevant to disaster response. Our methods leverage machine learning methods for classifying posts and information extraction. Our results, validated over one large disaster-related dataset, reveal that a careful design can yield an effective system, paving the way for more sophisticated data analysis and visualization systems.
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Vitaveska Lanfranchi, Suvodeep Mazumdar, & Fabio Ciravegna. (2014). Visual design recommendations for situation awareness in social media. 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. 792–801). University Park, PA: The Pennsylvania State University.
Abstract: The use of online Social Media is increasingly popular amongst emergency services to support Situational Awareness (i.e. accurate, complete and real-time information about an event). Whilst many software solutions have been developed to monitor and analyse Social Media, little attention has been paid on how to visually design for Situational Awareness for this large-scale data space. We describe an approach where levels of SA have been matched to corresponding visual design recommendations using participatory design techniques with Emergency Responders in the UK. We conclude by presenting visualisation prototypes developed to satisfy the design recommendations, and how they contribute to Emergency Responders' Situational Awareness in an example scenario. We end by highlighting research issues that emerged during the initial evaluation.
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Christine M. Newlon, Mark Pfaff, Himalaya Patel, Gert-Jan De Vreede, & Karl MacDorman. (2009). Mega-collaboration: The Inspiration and development of an interface for large-scale disaster response. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The need to gather and use decentralized information and resources in responding to disasters demands an integrated interface that can support large-scale collaboration. This paper describes the development of a collaboration tool interface. The tool will surpass existing groupware and social networking applications, providing easy entry, categorization, and visualization of masses of critical data; the ability to form ad-hoc teams with collaboration protocols for negotiated action; and agent-augmented mixed-initiative tracking and coordination of these activities. The paper reports user testing results concerning the data entry interface, emergent leadership, and the directed negotiation process. The paper also discusses planned enhancements, including formalized collaboration engineering and the use of a disaster simulation test bed.
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Ramsey, A., Kale, A., Kassa, Y., Gandhi, R., & Ricks, B. (2023). Toward Interactive Visualizations for Explaining Machine Learning Models. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 837–852). Omaha, USA: University of Nebraska at Omaha.
Abstract: Researchers and end users generally demand more trust and transparency from Machine learning (ML) models due to the complexity of their learned rule spaces. The field of eXplainable Artificial Intelligence (XAI) seeks to rectify this problem by developing methods of explaining ML models and the attributes used in making inferences. In the area of structural health monitoring of bridges, machine learning can offer insight into the relation between a bridge’s conditions and its environment over time. In this paper, we describe three visualization techniques that explain decision tree (DT) ML models that identify which features of a bridge make it more likely to receive repairs. Each of these visualizations enable interpretation, exploration, and clarification of complex DT models. We outline the development of these visualizations, along with their validity by experts in AI and in bridge design and engineering. This work has inherent benefits in the field of XAI as a direction for future research and as a tool for interactive visual explanation of ML models.
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André Sabino, Rui Nóbrega, Armanda Rodrigues, & Nuno Correia. (2008). Life-saver: Flood emergency simulator. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 724–733). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper proposes an agent-based simulation system for Dam Break Emergency Plan validation. The proposed system shows that integrating GIS data with an agent-based approach provides a successful simulation platform for the emergency plan validation process. Possible strategies to emergency plan modeling and representation are discussed, proposing a close relation with the actual workflow followed by the entities responsible for the plan's specification. The simulation model is mainly concerned with the location-based and location-motivated actions of the involved agents, describing the likely effects of a specific emergency situation response. The simulator architecture is further described, based on the correspondence between the representation of the plan, and the simulation model. This includes the involving characteristics of the simulation, the simulation engine, the description of the resulting data (for the later evaluation of the emergency plan) and a visualization and interaction component, enabling the dynamic introduction of changes in the scenario progression.
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Seungwon Yang, Haeyong Chung, Xiao Lin, Sunshin Lee, Liangzhe Chen, Andrew Wood, et al. (2013). PhaseVis1: What, when, where, and who in visualizing the four phases of emergency management through the lens of social media. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 912–917). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The Four Phase Model of Emergency Management has been widely used in developing emergency/disaster response plans. However, the model has received criticism contrasting the clear phase distinctions in the model with the complex and overlapping nature of phases indicated by empirical evidence. To investigate how phases actually occur, we designed PhaseVis based on visualization principles, and applied it to Hurricane Isaac tweet data. We trained three classification algorithms using the four phases as categories. The 10-fold cross-validation showed that Multi-class SVM performed the best in Precision (0.8) and Naïve Bayes Multinomial performed the best in F-1 score (0.782). The tweet volume in each category was visualized as a ThemeRiver[TM], which shows the 'What' aspect. Other aspects – 'When', 'Where', and 'Who' – Are also integrated. The classification evaluation and a sample use case indicate that PhaseVis has potential utility in disasters, aiding those investigating a large disaster tweet dataset.
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