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Eelco Vriezekolk, Sandro Etalle, & Roel Wieringa. (2015). Validating the Raster Risk Assessment Method in Practice. 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: Telecommunication services are essential to modern information systems, especially so for crisis management. Telecoms systems are complex and difficult to analyse. Current risk assessment methods are either not used because of their complexity, or lack rigorous argumentation to justify their results because they are oversimplified. Our challenge has been to develop a risk assessment method that is both usable in practice and delivers understandable arguments to explain and justify its risk evaluations. After experiments to validate the method in laboratory environments, we now present the first results from successful application with practitioners in a regional crisis organization that provides evidence about the practical usability of the method.
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Therese Friberg, Stephan Prödel, & Rainer Koch. (2011). Information quality criteria and their importance for experts in crisis situations. 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: In the past a lot of researchers have defined criteria to determine information quality. Various criteria and dimensions have been identified and examined in different contexts. But very few of them focused on information quality in the context of complex situations, especially in the domain of crisis management. These complex situations demand for an extensive level of information as a basis to the difficult decisions an officer-in-charge has to make. Therefore, if we want to support the decision-making of the leading officers through an at least semi-automated process, we need first of all to find a set of criteria to assess the information quality considering the special requirements of such complex situations. In this paper we describe our approach of defining a criteria set by identifying the characteristics of complex situations, then we analyze existing models of information quality and map their aggregated criteria to the identified characteristics and finally first results of interviews to evaluate the set through the involvement of domain experts are presented.
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Herrera, L. C., & Gjøsæter, T. (2023). Leveraging Crisis Informatics Experts: A co-creating approach for validation of social media research insights. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 439–448). Omaha, USA: University of Nebraska at Omaha.
Abstract: Validation of findings is a challenge in practice-based research. While analysis is being conducted and findings are being constructed out of data collected in a defined period, practitioners continue with their activities. This issue is exacerbated in the field of crisis management, where high volatility and personnel turnover make the capacity to attend research demands scarce. Therefore, conducting classic member validation is logistically challenging for the researcher. The need for rigor and validity calls for alternative mechanisms to fulfill requirements for academic research. This article presents an approach for validation of results of a qualitative study with public organizations that use social media as a source of information in the context of crisis management. The unavailability of original interview-objects to validate our findings resulted in an alternative validation method that leveraged experts in crisis informatics. By presenting our approach, we contribute to encouraging rigor in qualitative research while maintaining the relationship between practice and academia.
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Johannes Anhorn, Benjamin Herfort, & João Porto de Albuquerque. (2016). Crowdsourced Validation and Updating of Dynamic Features in OpenStreetMap – An analysis of Shelter Mapping after the 2015 Nepal Earthquake. 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: The paper presents results from a validation process of OpenStreetMap (OSM) rapid mapping activities using crowdsourcing technology in the aftermath of the Gorkha earthquake 2015 in Nepal. We present a framework and tool to iteratively validate and update OSM objects. Two main objectives are addressed: first, analyzing the accuracy of the volunteered geographic information (VGI) generated by the OSM community; second, investigating the spatio-temporal dynamics of spontaneous shelter camps in Kathmandu. Results from three independent validation iterations show that only 10 % of the OSM objects are false positives (no shelter camps). Unexpectedly, previous mapping experience only had a minor influence on mapping accuracy. The results further show that it is critical to monitor the temporal dynamics. Out of 4,893 identified shelter camps, 54% were already empty/closed six days after the first mapping. So far, updating geographical features during humanitarian crisis is not properly addressed by the existing crowdsourcing approaches.
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Jonas Rybing, Johan Larsson, Carl-Oscar Jonson, & Erik Prytz. (2016). Preliminary Validation Results of DigEmergo for Surge Capacity Management. 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: This paper presents preliminary analysis from a validation study of a novel emergency medicine command and control training and evaluation simulator: DIGEMERGO®. The simulated emergency scenario was a surge capacity event at a generic emergency department, in which the participants took on a management role as the emergency department?s coordinating head nurse. A between group validation design with medical expert and novice participants was used. Initial analysis examined three triage measures associated with surge capacity management performance: time to triage, amount of patients triaged, and triage accuracy. The results show that experts were significantly more accurate at triaging in-hospital patients, but not incoming trauma patients. No significant differences in time or number of patients triaged was found. These initial results partially indicate simulator validity, but trauma patient triage accuracy suffered from a confounding variable in the triage system used. Analysis of additional measures is undergoing to further investigate validity claims.
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Mirko Zaffaroni, & Claudio Rossi. (2020). Water Segmentation with Deep Learning Models for Flood Detection and Monitoring. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 66–74). Blacksburg, VA (USA): Virginia Tech.
Abstract: Flooding is a natural hazard that causes a lot of deaths every year and the number of flood events is increasing worldwide because of climate change effects. Detecting and monitoring floods is of paramount importance in order to reduce their impacts both in terms of affected people and economic losses. Automated image analysis techniques capable to extract the amount of water from a picture can be used to create novel services aimed to detect floods from fixed surveillance cameras, drones, crowdsourced in-field observations, as well as to extract meaningful data from social media streams. In this work we compare the accuracy and the prediction performances of recent Deep Learning algorithms for the pixel-wise water segmentation task. Moreover, we release a new dataset that enhances well-know benchmark datasets used for multi-class segmentation with specific flood-related images taken from drones, in-field observations and social media.
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Ooms, D. (2023). Civil-Military Interaction: a Case Study to validate a Conceptual Framework. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 501–515). Omaha, USA: University of Nebraska at Omaha.
Abstract: International peace operations in response to complex emergencies require effective interaction between international civil and military participants and local actors. Although these operations frequently occur worldwide, civil-military interaction (CMI) remains problematic. CMI problems are described in the literature at length. However, the knowledge management aspects of these problems have received less attention. The feasibility of technical support solutions for CMI should be investigated using a design science approach. This requires validated models of the structural and behavioral characteristics of the CMI domain. A CMI conceptual framework providing such models has been proposed earlier and should be validated. A case study has been conducted into a Netherlands military CMI organization. This study provides for initial user validation of the models. In follow-on research, the validated conceptual framework is used to structure the investigation of CMI problems, knowledge process deficiencies, and their causal relations. It may subsequently support knowledge engineering-based solution design.
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Per-Anders Oskarsson, Niklas Hallberg, Johan Nordström, Magdalena Granåsen, & Mari Olsén. (2022). Assessment of Collaborative Crisis Management Capability by Generic Questions. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 385–391). Tarbes, France.
Abstract: Societies need the ability to respond to crises such as terrorism, pandemics and natural disasters. Hence, it is essential to ensure that the capability of crisis management is attained, maintained, and developed. Since large crises cannot be handled by single organizations, collaborative crisis management capability is needed. The objective of this work was to provide support by an instrument for assessment of collaborative crisis management capability. The work was iteratively performed in a workgroup. The outcome was two templates with sets of generic questions, one for assessment of the actual capabilities and one for assessment of the preconditions of the capabilities. The templates mainly focus on assessment of collaborative crisis management capability. However, since the questions are generically formulated, they should be usable for assessments of any type of crisis management capability.
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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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Shivam Sharma, & Cody Buntain. (2021). An Evaluation of Twitter Datasets from Non-Pandemic Crises Applied to Regional COVID-19 Contexts. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 808–815). Blacksburg, VA (USA): Virginia Tech.
Abstract: In 2020, we have witnessed an unprecedented crisis event, the COVID-19 pandemic. Various questions arise regarding the nature of this crisis data and the impacts it would have on the existing tools. In this paper, we aim to study whether we can include pandemic-type crisis events with general non-pandemic events and hypothesize that including labeled crisis data from a variety of non-pandemic events will improve classification performance over models trained solely on pandemic events. To test our hypothesis we study the model performance for different models by performing a cross validation test on pandemic only held-out sets for two different types of training sets, one containing only pandemic data and the other a combination of pandemic and non-pandemic crisis data, and comparing the results of the two. Our results approve our hypothesis and give evidence of some crucial information propagation upon inclusion of non-pandemic crisis data to pandemic data.
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