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Anastasia Moumtzidou, Marios Bakratsas, Stelios Andreadis, Anastasios Karakostas, Ilias Gialampoukidis, Stefanos Vrochidis, et al. (2020). Flood detection with Sentinel-2 satellite images in crisis management systems. 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. 1049–1059). Blacksburg, VA (USA): Virginia Tech.
Abstract: The increasing amount of falling rain may cause several problems especially in urban areas, which drainage system can often not handle this large amount in a short time. Confirming a flooded scene in a timely manner can help the authorities to take further actions to counter the crisis event or to get prepared for future relevant incidents. This paper studies the detection of flood events comparing two successive in time Sentinel-2 images, a method that can be extended for detecting floods in a time-series. For the flood detection, fine-tuned pre-trained Deep Convolutional Neural Networks are used, testing as input different sets of three water sensitive satellite bands. The proposed approach is evaluated against different change detection baseline methods, based on remote sensing. Experiments showed that the proposed method with the augmentation technique applied, improved significantly the performance of the neural network, resulting to an F-Score of 62% compared to 22% of the traditional remote sensing techniques. The proposed method supports the crisis management authority to better estimate and evaluate the flood impact.
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Andreas Lotter, Philipp John, Patricia Schütte, Florian Brauner, & Frank Fiedrich. (2020). Field Observation of Parallel Working Coordination Groups and Command and Control Centres to Understand and Improve Inter-organizational Information Management – A Methodical Approach. 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. 303–314). Blacksburg, VA (USA): Virginia Tech.
Abstract: In emergency response, parallel working coordination groups and command and control centres are responsible to deal with complex events. A well-functioning exchange of information between organizations, officials and these coordination groups is the basis for an efficient risk management. This paper describes a methodical ap-proach for field observation to understand and improve the inter-organizational information management be-tween the involved partners. The method was tested within a practical approach and possible occurring problems during the observation were identified and solutions for these problems are provided.
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Andrew Arnette, Christopher W. Zobel, & Duygu Pamukcu. (2020). Post-Impact Analysis of Disaster Relief Resource Pre-Positioning After the 2013 Colorado Floods. 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. 237–243). Blacksburg, VA (USA): Virginia Tech.
Abstract: Pre-positioning of supplies is important to facilitate disaster relief operations, however it is only after a disaster event occurs that the effectiveness of the pre-positioning strategy can be properly assessed. With this in mind, this paper analyzes a risk-based pre-positioning algorithm, developed for the American Red Cross, in the context of its actual performance in the 2013 Colorado Front Range floods. The paper assesses the relative effectiveness of the pre-positioning approach with respect to historical asset placements, and it discusses changes to the model that are necessary to support such comparisons and allow for further model extensions.
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Andrew Marinik, Ludwig Gantner, Scott Fritz, & Sean Smith. (2020). Developing Performance Metrics of an Emergency Notification System. 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. 663–668). Blacksburg, VA (USA): Virginia Tech.
Abstract: The use of emergency notification systems (ENS), or early warning systems, are not only common practice among Institutes of Higher Education (IHEs), but are required by law in the United States. The dramatic increase in use is matched by the increase in community expectation. This community expectation corresponding with societal shifts challenges Public Safety leaders to implement and maintain a broad and highly reliable ENS. Most Public Safety programs lack the internal resources to consistently assess system risk, reliability, and messaging validity of their ENS sufficient to match the required system performance. Virginia Tech Emergency Management is proposing an ENS evaluation system capable of supporting assessment of reliability and risk across the entire system through the lens of Socio-Technical Systems (STS) theory at a practitioner level. By organizing emergency notification/early warning systems through Human Subsystems, Technical Subsystems, and Task Design the practitioner can assess their system by performance and risk.
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Anjum, U., Zadorozhny, V., & Krishnamurthy, P. (2023). Localization of Events Using Neural Networks in Twitter Data. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 909–919). Omaha, USA: University of Nebraska at Omaha.
Abstract: In this paper, we develop a model with neural networks to localize events using microblogging data. Localization is the task of finding the location of an event and can be done by discovering event signatures in microblogging data. We use the deep learning methodology of Bi-directional Long Short-Term Memory (Bi-LSTM) to learn event signatures. We propose a methodology for labeling the Twitter date for use in Bi-LSTM However, there might not be enough data available to train the Bi-LSTM and learn the event signatures. Hence, the data is augmented using generative adversarial networks (GAN). Finally, we combine event signatures at different temporal and spatial granularity to improve the accuracy of event localization. We use microblogging data collected from Twitter to evaluate our model and compare it with other baseline methods.
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Anmol Haque, Duygu Pamukcu, Ruixiang Xie, Mohsen Zaker Esteghamati, Margaret Cowell, & Jennifer L. Irish. (2021). Cascading Effects of Mass Gatherings on COVID-19 Infections from a Multi-hazard Perspective: A Case Study of New York City. 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. 218–227). Blacksburg, VA (USA): Virginia Tech.
Abstract: The devastating economic and societal impacts of COVID-19 can be substantially compounded by other secondary events that increase individuals' exposure through mass gatherings such as protests or sheltering due to a natural disaster. Based on the Crichton's Risk Triangle model, this paper proposes a Markov Chain Monte Carlo (MCMC) simulation framework to estimate the impact of mass gatherings on COVID-19 infections by adjusting levels of exposure and vulnerability. To this end, a case study of New York City is considered, at which the impact of mass gathering at public shelters due to a hypothetical hurricane will be studied. The simulation results will be discussed in the context of determining effective policies for reducing the impact of multi-hazard generalizability of our approach to other secondary events that can cause mass gatherings during a pandemic will also be discussed.
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Anna Kruspe. (2020). Detecting Novelty in Social Media Messages During Emerging Crisis Events. 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. 860–871). Blacksburg, VA (USA): Virginia Tech.
Abstract: Social media can be a highly valuable source of information during disasters. A crisis' development over time is of particular interest here, as social media messages can convey unfolding events in near-real time. Previous approaches for the automatic detection of information in such messages have focused on a static analysis, not taking temporal changes and already-known information into account. In this paper, we present a novel method for detecting new topics in incoming Twitter messages (tweets) conditional upon previously found related tweets. We do this by first extracting latent representations of each tweet using pre-trained sentence embedding models. Then, Infinite Mixture modeling is used to dynamically cluster these embeddings anew with each incoming tweet. Once a cluster reaches a minimum number of members, it is considered to be a new topic. We validate our approach on the TREC Incident Streams 2019A data set.
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Anne-Marie Barthe-Delanoë, & Wenxin Mu. (2020). Towards a Context-Aware Systemic Risk Management Framework for the Crisis Response. 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. 1122–1129). Blacksburg, VA (USA): Virginia Tech.
Abstract: Crisis response is, as any other collaborative networked organization, challenged by changes and vulnerabilities. Moreover, as a complex system with distributed activities and numerous interdependencies, considering the risk of such an organization at a systemic level, including time and space dimensions, is necessary. Systemic risk management is a topic traditionally studied in the finance area. Even if a few researches now focus on the supply chain management area (a more relatable domain regarding crisis response), there is even fewer literature regarding systemic risk management for the crisis response. Thus, this paper proposes first to define systemic risk related to the case of the crisis response. Then, a framework for context-aware systemic risk management is presented, to support the design as well as the follow-up of the crisis response, meeting one of the challenges of the Sendai Framework for Disaster Risk Reduction.
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Anouck Adrot, & Mercedes Aguerre. (2022). Data Ecosystems and Disaster Risk Reduction in Cross-border Regions: Visioning from 2020 Roya Valley Flood Disaster. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 878–886). Tarbes, France.
Abstract: Knowledge on the practical support from data ecosystems to disaster risk reduction remains partial. More specifically, we misunderstand the drivers and challenges inherent to emergency data ecosystems development in cross-border regions. We also miss cases of data ecosystem building in those regions. This research addresses these gaps by abiding by the principles and guidelines of visioning, a prospective and collaborative research design. Based on qualitative interviewing and archive analysis of the case 2020 Roya Valley floods, this work provides a prospect of a segment of data ecosystem that involves an organizational field aiming at Disaster Risk Reduction (DRR) at the French-Italian border. Still in progress, this work provides a comprehensive narration of a fictious data ecosystem. The narration hints at the major benefits and challenges inherent to this potential data ecosystem. This work enriches our understanding of data ecosystems’ features and benefits to cooperation between organizations involved in emergencies at borders (such as governments, civil protection agencies, volunteer-based organizations). In future development it will propose an agenda to support practitioners in the development good practices related to data ecosystems.
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Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.). (2021). 18th ISCRAM Conference Proceedings. Blacksburg, VA (USA): Virginia Tech.
Abstract: The theme of ISCRAM 2021 is ?Embracing the Interdisciplinary Nature of Crisis Management.? These
proceedings highlight the range of interdisciplinary research required to understand the design, behavior,
and performance of crisis and emergency management systems. We are pleased to present the included
papers, which offer excellent contributions on a wide range of topics related to the use of information
systems in crisis response and management.
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Anouck Adrot, Samuel Auclair, Julien Coche, Audrey Fertier, Cécile Gracianne, & Aurélie Montarnal. (2022). Using Social Media Data in Emergency Management: A Proposal for a Socio-technical Framework and a Systematic Literature Review. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 470–479). Tarbes, France.
Abstract: Data represents an essential resource to the management of emergencies: organizations have been growingly investing in technologies and resources to lever data as an asset before, during, and after disasters and emergencies. However, research on data usage in emergency management remains fragmented, preventing practitioners and scholars from approaching data comprehensively. To address this gap, this research in progress consists of a systematic review of the literature in a two-steps approach: we first propose a socio-technical framework and use it in an exploratory mapping of the main topics covered by the literature. Our preliminary findings suggest that research on data usage primarily focuses on technological opportunities and affordances and, hence, lacks practical implementation aspects in organizations. The expected contribution is double. First, we contribute to a more comprehensive understanding of data usage in emergency management. Second, we propose future avenues for research on data and resilience.
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Anton Björnqvist, Marc Friberg, Carl-Oscar Jonson, Jenny Pettersson, & Peter Berggren. (2022). An Analysis of a Swedish Medical Command and Control System’s Situation Reports from the COVID-19 Pandemic. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 334–348). Tarbes, France.
Abstract: This paper presents an analysis of situation reports used and created by a crisis management team within the Swedish healthcare sector during the early phase of the COVID-19 pandemic. The analysis was conducted through a deductive content analysis, where categories were identified based on the concepts of common operational pictures, sensemaking, and situation awareness. In the analysis, support for all identified categories was found. Based on the analysis and the concepts, future recommendations regarding what type of information that ought to be included in situation reports were created. These recommendations include, amongst others, the categories of consequences, how it is perceived by the public, objectives, status and implications of information, future scenarios, actions, resources, and work procedures.
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Antone Evans Jr., Yingyuan Yang, & Sunshin Lee. (2021). Towards Predicting COVID-19 Trends: Feature Engineering on Social Media Responses. 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. 792–807). Blacksburg, VA (USA): Virginia Tech.
Abstract: During the course of this pandemic, the use of social media and virtual networks has been at an all-time high. Individuals have used social media to express their thoughts on matters related to this pandemic. It is difficult to predict current trends based on historic case data because trends are more connected to social activities which can lead to the spread of coronavirus. So, it's important for us to derive meaningful information from social media as it is widely used. Therefore, we grouped tweets by common keywords, found correlations between keywords and daily COVID-19 statistics and built predictive modeling. The features correlation analysis was very effective, so trends were predicted very well. A RMSE score of 0.0425504, MAE of 0.03295105 and RSQ of 0.5237014 in relation to daily deaths. In addition, we found a RMSE score of 0.07346836, MAE of 0.0491152 and RSQ 0.374529 in relation to daily cases.
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Antonio De Nicola, Maria Luisa Villani, Francesco Costantino, Andrea Falegnami, & Riccardo Patriarca. (2021). Knowledge Fusion for Distributed Situational Awareness driven by the WAx Conceptual Framework. 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. 79–85). Blacksburg, VA (USA): Virginia Tech.
Abstract: Large crisis scenarios involve several actors, acting at the blunt-end of the process, such as rescue team directors, and at the sharp-end, such as firefighters. All of them have different perspectives on the crisis situation, which could be either coherent, alternative or complementary. This heterogeneity of perceptions hinders situational awareness, which is defined as the achievement of an overall picture on the above-mentioned crisis situation. We define knowledge fusion as the process of integrating multiple knowledge entities to produce actionable knowledge, which is consistent, accurate, and useful for the purpose of the analysis. Hence, we present a conceptual modelling approach to gather and integrate knowledge related to large crisis scenarios from locally-distributed sources that can make it actionable. The approach builds on the WAx framework for cyber-socio-technical systems and aims at classifying and coping with the different knowledge entities generated by the involved operators. The conceptual outcomes of the approach are then discussed in terms of open research challenges for knowledge fusion in crisis scenarios.
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Anying Chen, Zhongliang Huang, Manchun Liang, & Guofeng Su. (2020). Empirical Study of Individual Evacuation Decision-making in Fire Accidents: Evacuate Intention and Herding Effect. 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. 200–209). Blacksburg, VA (USA): Virginia Tech.
Abstract: People's decision of evacuating or not could greatly influence the final losses in fire accidents. In order to study people's response under emergent occasions, a fire accident evacuation drill experiment was conducted in an office building without advance notice. 113 Participants' response and their decision-making process were collected by questionnaire survey right after the experiment. In this study, we mainly focused on two aspects of people's response, including participants' evacuate intention and their herding tendency during evacuate decision-making. It is found that the classical Expected Utility Theory (EUT) has certain limitation in explaining individual's evacuation intention, but the relationship between the expected utility and the evacuation intention could be represented with a modified model based on EUT. Furthermore, the herding tendency is found to be different for the two groups of people who intend to evacuate and not to evacuate. People who firstly intend not to evacuate are more easily to form herding behavior and change their minds to evacuate. Based on these findings, models of individual evacuation intention and herding tendency for two groups of people are put forward. Simulation is conducted to investigate the effect of these two changes in people's evacuation decision-making process, and results show that they both increase the final evacuation rate, reflecting the majority's risk aversion characteristics.
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Apoorva Chauhan, & Amanda Hughes. (2021). COVID-19 Named Resources on Facebook, Twitter, and Reddit. 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. 679–690). Blacksburg, VA (USA): Virginia Tech.
Abstract: Crisis Named Resources (CNRs) are social media accounts and pages named after a crisis event. They are created soon after an event occurs. CNRs share a lot of information around an event and are followed by many. In this study, we identify CNRs created around COVID-19 on Facebook, Twitter, and Reddit. We analyze when these resources were created, why they were created, how they were received by members of the public, and who created them. We conclude by comparing CNRs created around COVID-19 with past crisis events and discuss how CNR owners attempt to manage content and combat misinformation.
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Arnis Parsovs. (2020). Solving the Estonian ID Card Crisis: the Legal Issues. 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. 459–471). Blacksburg, VA (USA): Virginia Tech.
Abstract: In 2017, Estonia experienced a cyber crisis caused by a vulnerability found in the smart card chips produced by Infineon Technologies AG. Since the affected chip was used in the electronic identity card (ID card) issued by the State to more than half of the Estonian population, the vulnerability posed a risk to the resilience of Estonian e-state and thus quickly escalated into a manageable crisis. This work studies to what extent, in such a national emergency, the involved parties were able to precisely follow the applicable laws and regulations in the field. We enlist the cases where the requirements were not fully followed, either due to the lack of technical preparedness, suboptimal decisions made under heavy time pressure, or the critical nature of the situation.
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Artur Ricardo Bizon, Luciana P. de Araújo Kohler, Adilson Luiz Nicoletti, Fernanda Dal Bosco, Murilo Schramm da Silva, & Thales Bohn Pessatti. (2020). Integration statistical systems for land cover mapping in Southern Brazil. 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. 498–505). Blacksburg, VA (USA): Virginia Tech.
Abstract: The remote sensing is a way to optimize the process of land cover classification allowing that this process will be by high definition images of satellite. For the research it was used the Google Earth Engine with JavaScript programming language to classify the images, identifying the areas with forest or reforest. It was identified that classifiers Random Forest and Logistic Regression have a high performance in classify the images. From them it was developed functions to process automatically of new images with purpose of classify them in relation to land cover.
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Audun Stolpe, & Jo Hannay. (2021). On the Adaptive Delegation and Sequencing of Actions. 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. 28–39). Blacksburg, VA (USA): Virginia Tech.
Abstract: Information systems support to crisis response and management relies crucially on presenting actionable information in a manner that supports cognitive processes, and does not overwhelm them. We outline how AI Planning can be used viably to support the \emph{delegation and sequencing} of tasks. The idea is to use standard operating procedures as initial specifications of plans in terms of actors, actions and delegation rules. When expressed in the AI planning language \textit{Answer set Programming} (ASP), machine reasoning can be used in a \textit{pre-incident review} to display relevant delegation and sequencing inherent in a plan. % together with measures of goal achievement. The purpose of this is to uncover weaknesses in the initial plan and to optimize sequencing and delegation to increase the likelihood of achieving goals. Further, adaptive planning can be supported in \textit{during-incident reviews} by updating the current status, upon which ASP will then compute new alternatives. % and corresponding goal achievement measures. At this point, initial goals may no longer be viable and the explicit suggestion of prior sub-optimal goals now worth pursuing can be a game-changer under stress. The conceptual basis we lay out in terms of delegation and sequencing can be readily extended with further planning factors, such as resource requirements, role transfer and goal achievement.
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Aurélie Congès, Alexis Evain, Olivier Chabiron, Col. Jacob Graham(USMC, R.), & Frédérick Benaben. (2020). Virtual Reality to Train for Crisis Management. 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. 1100–1112). Blacksburg, VA (USA): Virginia Tech.
Abstract: The EGCERSIS project aims at using virtual reality to improve the efficiency of the crisis management preparation phase. The idea is to tackle the drawbacks of regular crisis management exercises thanks to fully configurable scenarios taking place in digital twins of real critical sites. Virtual exercises will improve, among other things, the frequency, efficiency, and modularity of crisis management preparation, while reducing its costs. In this article, we demonstrate the idea of the project through a simple use-case taking place in a metro station and involving three crisis responders. By linking virtual exercises to our crisis management platform, we also want to demonstrate the usefulness of decision-support systems during a crisis.
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Ayda Kianmehr, & Duygu Pamukcu. (2022). Analyzing Citizens’ Needs during an Extreme Heat Event, based on 311 Service Requests: A Case Study of the 2021 Heatwave in Vancouver, British Columbia. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 174–182). Tarbes, France.
Abstract: Heat waves are becoming more common and intense with global climate change, which requires deploying resilience strategies of governments to prepare for long-term trends of higher temperatures and carefully plan emergency responses for such extreme heat events. The British Columbia province of Canada is one of the regions severely affected by extreme climatic events in 2021, which resulted in several deaths and put hundreds of thousands of people scrambling for relief. This study examines the public reactions to one of these extreme climatic events, the 2021 Pacific Northwest heatwave, in a non-emergency service request platform to uncover the types of municipal service needs during severe climatic disasters. City of Vancouver 311 system data is used to identify the impact of the heatwave on the frequency and types of service needs and examine the significance of the relationship between climatic conditions and the non-emergency service volumes.
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Balogh, Z., Gatial, E., Dolatabadi, S. H., Dlugolinský, Štefan, Saltarella, M., Scipioni, M. P., et al. (2023). Communication Protocol for using Nontraditional Information Sources between First Responders and Citizens during Wildfires. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 152–165). Omaha, USA: University of Nebraska at Omaha.
Abstract: One of the biggest challenges faced during the wildfires is communication. A specific case represents the need to establish communication between first responders and the public. This paper presents a proposal for a generic protocol to ensure effective communication between fire fighters and many citizens at the incident site or in the surrounding area using nontraditional information sources such as a dedicated mobile app or social media. Specific challenges, concepts and technologies relevant to such communication are described specifically customized for forest fires and wildfires. The protocol itself is provided by proposing information flows between the involved actors. Moreover, several technologies including a Citizen Engagement Mobile App, an Edge Micro Data Center for forward command centers, a Mesh in the Sky communication infrastructure or a Dashboard integrating and displaying all the data in one place is shortly introduced. The presented paper is a work in progress.
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Bas Lijnse. (2022). Modeling Real World Crisis Management Plans with C2Sketch. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 404–413). Tarbes, France.
Abstract: When crisis management plans are described in natural language documents, they may contain logical inconsistencies or ambiguities that are not immediately apparent. To allow automated early detection of such errors, they need to be described in a well defined formalism. C2Sketch is a tool for modeling command and control systems that provides such a structure for formalizing (crisis management) plans. However, C2Sketch is in active development and to what extent real-world crisis management plans can be expressed in it is unknown. In this exploratory study the unstructured text of a small sample of publicly available regional-level crisis management plans was translated systematically into structured C2Sketch mission-plans to uncover limitations and opportunities for further development of the tool. The plans contained enough information to largely capture the networks of actors and their tasks therein, but did contain enough operational information to develop complete C2Sketch models from.
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Ben Ortiz, Laura Kahn, Marc Bosch, Philip Bogden, Viveca Pavon-Harr, Onur Savas, et al. (2020). Improving Community Resiliency and Emergency Response With Artificial Intelligence. 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. 35–41). Blacksburg, VA (USA): Virginia Tech.
Abstract: New crisis response and management approaches that incorporate the latest information technologies are essential in all phases of emergency preparedness and response, including the planning, response, recovery, and assessment phases. Accurate and timely information is as crucial as is rapid and coherent coordination among the responding organizations. We are working towards a multi-pronged emergency response tool that provide stakeholders timely access to comprehensive, relevant, and reliable information. The faster emergency personnel are able to analyze, disseminate and act on key information, the more effective and timelier their response will be and the greater the benefit to affected populations. Our tool consists of encoding multiple layers of open source geospatial data including flood risk location, road network strength, inundation maps that proxy inland flooding and computer vision semantic segmentation for estimating flooded areas and damaged infrastructure. These data layers are combined and used as input data for machine learning algorithms such as finding the best evacuation routes before, during and after an emergency or providing a list of available lodging for first responders in an impacted area for first. Even though our system could be used in a number of use cases where people are forced from one location to another, we demonstrate the feasibility of our system for the use case of Hurricane Florence in Lumberton, a town of 21,000 inhabitants that is 79 miles northwest of Wilmington, North Carolina.
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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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