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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Han Che, & Shuming Liu. (2013). Monitoring data identification for a water distribution system based on data self-recognition approach. 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. 166–170). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Detecting the occurrence of hydraulic accidents or contamination events in the shortest time has always been a significant but difficult task. The simple and efficient way is to identify the sudden changes or outliers hidden in the vast amounts of monitoring data produced minute by minute, which is unpractical for human. A new method, which employs a data self-recognition approach to achieve that automatically, has been proposed in this paper. The autoregressive moving average (ARMA) model was employed in this research to construct the self-recognition model. 56 months monitoring data from Changping water distribution network in Beijing, which was firstly cut into different time-slice series, was used to establish the ARMA model. This provided a prediction confidence interval in order to identify the outliers in the test data series. The results showed a good performance in outlier identification and the accuracy ranges from 90% to 95%.Thus, the ARMA model showed great potential in dealing with monitoring data and achieving the expected performance of data self-recognition technology.
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Gonçalo De Jesus, Anabela Oliveira, Maria A. Santos, & João Palha-Fernandes. (2010). Development of a dam-break flood emergency information system. 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: This paper presents a new information system, SAGE-B, structured to support all fundamental data related to dams and the elements associated to an emergency in case of a dam-break flood. Data such as information about the population located in the areas at risk or the vehicles available for rescue that are located in the areas impacted by the predicted flood are always changing. In order to support an effective update of the required information for emergency management, an emergency information system was conceived and proposed. This paper describes the motivation for this research and the basic requirements from an emergency management perspective. The platform has a modular architecture, developed in open and free technologies, which allows a continuous development and improvement. Examples of future developments include a multichannel emergency warning system, flood wave real-time forecast and dam-breaching real-time monitoring models.
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Nicolas Di Tada, & Timothy Large. (2010). Emergency information system. 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: This paper describes an information system designed to be deployed in emergencies caused by sudden onset natural disasters. The aim is to streamline the communication flow and collaboration between media, aid workers and government agencies with the affected population, to help the latter get verified, accurate and actionable information that will enable them to make decisions and recover from the disaster. The Emergency Information Service (EIS) system also provides means for affected population and field workers to channel vital data back up into aid response. This tool is part of a free information service run by Thomson Reuters Foundation to help survivors of natural disasters. It will serve the affected populations, local media and relief responders by providing fast, practical and verified information in local languages through the best means available.
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Sérgio Freire, Daniele Ehrlich, & Stefano Ferri. (2014). Assessing temporal changes in global population exposure and impacts from earthquakes. 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. 324–328). University Park, PA: The Pennsylvania State University.
Abstract: It is frequently conveyed, especially in the media, an idea of “increasing impact of natural hazards” typically attributed to their rising frequency and/or growing vulnerability of populations. However, for certain hazard types, this may be mostly a result of increasing population exposure due to phenomenal global population growth, especially in the most hazardous areas. We investigate temporal changes in potential global population exposure and impacts from earthquakes in the XXth century. Spatial analysis is used to combine historical population distributions with a seismic intensity map. Changes in number of victims were also analyzed, while controlling for the progress in frequency and magnitude of hazard events. There is also a focus on mega-cities and implications of fast urbanization for exposure and risk. Results illustrate the relevance of population growth and exposure for risk assessment and disaster outcome, and underline the need for conducting detailed global mapping of settlements and population distribution.
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Sérgio Freire, & Christoph Aubrecht. (2011). Assessing spatio-temporal population exposure to tsunami hazard in the Lisbon Metropolitan Area. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The coastal region of Lisbon, Portugal, is potentially subject to tsunami hazard. Mapping and assessing tsunami risk requires giving adequate consideration to the population exposure. In the present work we model and map the spatio-temporal distribution of population in the daily cycle and analyze it with a tsunami hazard map to better assess tsunami risk in the Lisbon Metropolitan Area. New high-resolution daytime and nighttime population distribution surfaces are developed using 'intelligent dasymetric mapping' to combine best-available census data and statistics with land use and land cover data. Mobility statistics are considered for mapping daytime distribution. Finally, the population distribution maps are combined with the Tsunami Inundation Susceptibility map to assess potential human exposure to tsunami in daytime and nighttime periods. Results show that a significant amount of population is potentially at risk, and its numbers increase from nighttime to daytime, especially in the zones of high susceptibility.
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María Hernandez, Susana Montero, David Díez, Paloma Díaz, & Ignacio Aedo. (2010). A data transfer protocol for forest fire statistics: Achieving interoperability among independent agencies. 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: The elaboration of statistics after a catastrophic situation allows us not only to analyze the economic, ecological and social impact of the event but also to improve the emergency management process. One compelling example of data collection for statistics is forest fires. The agencies involved in providing data have its own systems to collect data and mechanisms to send them, as well as, its data format for storing. Since such mechanisms are usually proprietary, and in order to normalize the exchange of data with statistics generating systems, a data transfer protocol should be used. In this paper we present a data transfer protocol called Forest Fire Statistics Protocol (FFSP). This protocol aims at transmitting consolidated forest fire data between independent agencies. The data transferred are based on the Forest Fire Report Data Model. Both mechanisms are based on open standards providing both technical interoperability and a solution that might be developed once and fit the needs of all. FFSP has been implemented as a web service over SOAP, SSL/TLS and TCP protocols.
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Johan Jenvald, Michael Morin, Toomas Timpka, & Henrik Eriksson. (2007). Simulation as decision support in pandemic influenza preparedness and response. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 295–304). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Outbreak of a destructive pandemic influenza threatens to disrupt societies worldwide. International agencies and national governments have prepared plans and recommendations, but it is often decision-makers with the local authorities that are responsible for implementing the response. A central issue for these decision makers is what interventions are available and effective for the specific local community. The paper presents a simulator architecture and its relation to a workflow for decision support in influenza preparedness and response. The simulator can simulate pandemic scenarios, using localized community models, in the presence of various interventions to support an evaluation of potential response strategies. The architecture includes a customized modeling tool, separated from the simulation engine, which facilitates swift scenario modification and recalculation. This flexibility is essential both to explore alternative solutions in planning, and to adapt to changing requirements, information, and resources in outbreak response. An example simulation, based on actual population data from a reference city, illustrates the approach.
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Martijn Neef, Kees Van Dongen, & Marijn Rijken. (2013). Community-based comprehensive recovery: Closing collaboration gaps in urban disaster recovery. 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. 546–550). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Disaster recovery in urban environments is a complex process. Because of high population densities and the presence of many societal and infrastructural dependencies, urban areas are prone to severe loss of self-reliance in case of a disaster. Rebuilding such areas to a self-sustaining state is a daunting task, and requires a high degree of community effort and comprehensive knowledge about the affected environment. All too often, these requirements are not properly met, leading to a long recovery trajectory and misalignments between recovery efforts and community needs. We suggest that most issues in disaster recovery stem from 'collaboration gaps': Flawed organisational structures between stakeholder parties that exist between levels of operation and between phases in the recovery process. We introduce two innovation pathways to close these gaps, and present the COBACORE project that will explore these pathways, and create a collaborative platform for effective community-based comprehensive disaster recovery.
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Kamil Panitzek, Immanuel Schweizer, Dirk Bradler, & Max Mühlhäuser. (2011). City mesh – Resilient first responder communication. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Communication between first responders is vital to the success of large scale disaster management. But communication technologies used by first responders today do not scale well due to heterogeneity, point-topoint connections, and centralized communication structures. As the popularity of devices equipped with Wi-Fi grows, the number of access points (APs) in city centers increases as well. This communication infrastructure exists and should be used in city wide disasters as it is readily available in areas with high population density. In this paper, we investigate Wi-Fi access points in 5 major cities deployed in stores, bars, and restaurants. We want to answer the question if these APs can be used as a mesh networking backbone in disaster response. The main contributions of this paper are (i) the surveyed and analyzed public Wi-Fi layout of five major cities and (ii) the connectivity analysis of the city wide network topology.
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St. Denis, L. A., & Hughes, A. L. (2023). Use of Statistics in Disaster by Local Individuals: An Examination of Tweets during COVID-19. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 449–458). Omaha, USA: University of Nebraska at Omaha.
Abstract: We report on how individuals local to the US state of Colorado used statistics in tweets to make sense of the early stages of the COVID-19 pandemic. Tweets provided insight into how people interpreted statistical data, sometimes incorrectly, which has implications for crisis responders tasked with understanding public perceptions and providing accurate information. With widespread concerns about the accuracy and quality of online information, we show how monitoring public reactions to and uses of statistics on social media is important for improving crisis communication. Findings suggest that statistics can be a powerful tool for making sense of a crisis and coping with the stress and uncertainty of a global, rapidly evolving event like the COVID-19 pandemic. We conclude with broader implications for how crisis responders might improve their communications around statistics to the public, and suggestions for how this research might be expanded to look at other types of disasters.
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Yan, S. (2005). Design of enterprise crisis predicting system based on cluster and outlier data mining. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management (pp. 143–145). Brussels: Royal Flemish Academy of Belgium.
Abstract: In order to solve such problems as half-structured and non-structured data analysis in enterprise crisis predicting system, a predicting system based on cluster and outlier data mining is put forward. The system organization, frame construction, function and working principles are illustrated. And the working process is showed by an example of cheat predicting. The experimental results show that this method is efficient and it is a new way to solve such problems.
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Christopher W. Zobel, Stanley E. Griffis, Steven A. Melnyk, & John R. MacDonald. (2012). Characterizing disaster resistance and recoveryusing outlier detection. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Most definitions of disaster resilience incorporate both the capacity to resist the initial impact of a disaster and the ability to recover after it occurs. Being able to characterize and analyze resilient behavior can lead to improved understanding not only of the capabilities of a given system, but also of the effectiveness of different strategies for improving its resiliency. This paper presents an approach for quantifying the transient behavior resulting from a disaster event in a way that allows researchers to not only describe the transient response but also assess the impact of various factors (both main and interaction effects) on this response. This new approach combines simulation modeling, time series analysis, and statistical outlier detection to differentiate between disaster resistance and disaster recovery. Following the introduction of the approach, the paper provides a preliminary look at its relationship to the existing concept of predicted disaster resilience. © 2012 ISCRAM.
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