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Min Zhu, Ruxue Chen, Tianye Lin, Quanyi Huang, & Guang Tian. (2019). Describing and Forecasting the Medical Resources assignments for International Disaster Medical relief Forces Using an Injury-Driven Ontology Model. 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: Available medical resources are the basis of efficient disaster medical relief. The medical resources assignment for disaster medical relief forces is usually fixed. However, the injury condition distribution changes in different disaster and so does the demand for the medical resources. So the assignment of medical relief forces should be more flexible and based on the injury. We analyzed the component parts and rules of disaster medical relief, defining the related concepts and rules. Then, we constructed the describing rules of injury-treatment-medical-technique-resource-assignment process. Based on these, we established the ontology of disaster medical relief system and the injury-driven medical resources assignment ontology model (MRAOM). We used the model to describe the medical relief situation after earthquake to demonstrate the model could describe complicated situations. We also used the model to describe and forecast the medical resource assignment of treating batch wounded to demonstrate the model's validity.
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Olawunmi George, Rizwana Rizia, MD Fitrat Hossain, Nadiyah Johnson, Carla Echeveste, Jose Lizarraga Mazaba, et al. (2019). Visualizing Early Warning Signs of Behavioral Crisis in Military Veterans: Empowering Peer Decision Support. 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: Several attempts have been made at creating mobile solutions for patients with mental disorders. A preemptive approach would definitely outdo a reactive one. This project seeks to ensure better crisis detection, by assigning patients (veterans) to caregivers (mentors). This is called the mentor-mentee approach. Enhanced with the use of mobile technology, veterans can stay connected in their daily lives to mentors, who have gone through the same traumatic experiences and have overcome them. A mobile application for communication between veterans and their mentors has been developed, which helps mentors get constant feedback from their mentees about their state of well-being. However, being able to make good deductions from the data given as feedback is of great importance. Under-represent ing or over-representing the data could be dangerously misleading. This paper presents the design process in this project and the key things to note when designing a data visualization for
timely crisis detection and decision-making.
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Paige Maas, Shankar Iyer, Andreas Gros, Wonhee Park, Laura McGorman, Chaya Nayak, et al. (2019). Facebook Disaster Maps: Aggregate Insights for Crisis Response & Recovery. 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: After a natural disaster or other crisis, humanitarian organizations need to know where affected people are located
and what resources they need. While this information is difficult to capture quickly through conventional methods,
aggregate usage patterns of social media apps like Facebook can help fill these information gaps.
In this paper, we describe the data and methodology that power Facebook Disaster Maps. These maps utilize
information about Facebook usage in areas impacted by natural hazards, producing aggregate pictures of how the
population is affected by and responding to the hazard. The maps include insights into evacuations, cell network
connectivity, access to electricity, and long-term displacement.
In addition to descriptions and examples of each map type, we describe the source data used to generate the maps,
and efforts taken to ensure the security and privacy of Facebook users. We also describe limitations of the current
methodologies and opportunities for improvement.
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Patricia Quiroz-Palma, Ma Carmen Penadés, & Ana-Gabriela Núñez. (2019). Towards a Capability Model for Emergency Training Improvement. 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: Giving adequate attention to training personnel within an organization to perform an activity of any kind
determines its success or failure. Training in emergency management is a key point and the participants must have
adequate preparation for each activity they carry out. The different activities in each emergency management
phase generate the appropriate training according to the role performed by stakeholders. The training is provided
through techniques and IT support tools that consolidate the knowledge imparted by the trainer. This paper
describes the initial steps in creating a capability model to support the training of stakeholders and ensure the
effectiveness of the response teams, as well as the appropriate actions of workers and citizens in an emergency.
Knowledge is consolidated through training, evaluation and feedback from practice. The proposed model is being
integrated in the QuEP framework to guide organizations in assessing and improving the management of their
emergency plans.
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Patrick Lieser, Julian Zobel, Björn Richerzhagen, & Ralf Steinmetz. (2019). Simulation Platform for Unmanned Aerial Systems in Emergency Ad Hoc Networks. 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.
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Peter Berggren, Molly Lundberg, Joeri van Laere, & Björn J E Johansson. (2019). Community resilience towards disruptions in the payment system. 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: This paper presents a study where nine Swedish citizens were interviewed about their concerns and expectations, from a customer perspective, in relation to a 10 day disruption in the payment system. The purpose of the study was to understand the customer?s perspective in order to provide input to the development of a simulation environment. This simulation environment aims at allowing different stakeholders to experience how a disruption in the payment system affects the local community and thereby create understanding of how resilience is built and affected. The research questions were: What do customers expect to get access to? When? What are customers prepared for? How does this differ among different customer groups? The results indicate some understanding of how such a crisis affects the local community and what the informants expects to happen. The respondents represented a diversity of socio-economic backgrounds from rural and urban parts of the municipality.
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Philipp Hertweck, Tobias Hellmund, Hylke van der Schaaf, Jürgen Moßgraber, & Jan-Wilhelm Blume. (2019). Management of Sensor Data with Open Standards. 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: In an emergency, getting up-to-date information about the current situation is crucial to orchestrate an efficient response. Due to its objectivity, preciseness and comparability, time-series data offer broad possibilities to manage emergency incidents. Since the Internet of Things (IoT) is rapidly growing with an estimated number of 30 billion sensors in 2020, it offers excellent potential to collect time-series data for improving situational awareness. The IoT brings several challenges: caused by a splintered sensor manufacturer landscape, data comes in various structures, incompatible protocols and unclear semantics. To tackle these challenges a well-defined interface, from where uniform data can be queried, is necessary. The Open Geospatial Consortium (OGC) has recognized this demand and developed the SensorThings API standard, an open, unified way to interconnect devices throughout the IoT, which is implemented by the FRaunhofer-Opensource-SensorThings-Server (FROST). This paper presents the standard, its implementation and the application to the domain of crisis management.
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Prithviraj Dasgupta, & Deepak Khazanchi. (2019). A Unified Approach Integrating Human Shared Mental Models with Intelligent Autonomous Team Formation for Crisis Management. 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: Autonomous systems are being exceedingly used to assist humans in various crisis responses scenarios such as earthquakes and nuclear disasters. Because they operate in highly unstructured and uncertain environments, failures are an inherent part of such autonomous systems, and, techniques for making these systems robust to failures arising from computer hardware, software or communication malfunctions are already integrated into their design. However, an important aspect while designing such systems is often times overlooked: how to better coordinate and communicate across distributed, possibly diverse human teams who are working in cooperation with autonomous systems into the design of the autonomous system itself. Unfortunately, this results in limited adoption of autonomous systems in real-life crisis scenarios. In this working paper, we describe ongoing work that attempts to address this deficit by integrating research on shared mental models between humans with techniques for autonomous agent team formation in the context of search and rescue scenarios.
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Pär Hans Tuve Eriksson, & Niklas Hallberg. (2019). Design to Fit – Morphological Analysis as a Tool for Exercise Design. 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: Exercises are considered as vital to develop and sustain crisis management capabilities. An exercise may have a role both as a provider of knowledge about the crisis management system, and its performance, and as a driver of change through training of individuals, groups and organizations. However, the relationship between the design and characteristics of a specific exercise, and the usability of its results in the development of the crisis management system is not well-understood. The objective of this paper is to explore if a morphological field can be used to investigate this relationship. Such a field was designed and evaluated. Even though this field was relatively simple, it was concluded that it provides results that deepen the understanding of how different types of exercises can contribute to the development of the crisis management system.
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Quynh Nhu Nguyen, Antonella Frisiello, & Claudio Rossi. (2019). The Design of a Mobile Application for Crowdsourcing in Disaster Risk Reduction. 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: Disaster Risk Reduction is a complex field in which a huge amount of data is collected and processed every day
in order to plan and run preparedness and response actions, which are required to get ready and to effectively
respond to natural disasters when they strike. This paper, which targets a wide audience, focuses on the design of
a mobile application that aims to integrate the crowdsourcing paradigm in current Disaster Risk Reduction
processes. The design process is integrated in the User Centred Approach, which we apply through a co-design
methodology involving end-users, iterative prototyping and development phases, and five in-field evaluations of
the implemented solution. We describe both the design activities and the results obtained from end-users�
feedbacks focusing on the perspective of first responders.
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Rahul Pandey, Gaurav Bahl, & Hemant Purohit. (2019). EMAssistant: A Learning Analytics System for Social and Web Data Filtering to Assist Trainees and Volunteers of Emergency Services. 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: An increasing number of Machine Learning based systems are being designed to filter and visualize the relevant
information from social media and web streams for disaster management. Given the dynamic disaster events, the
notion of relevant information evolves, and thus, the active learning techniques are often considered to keep
updating the predictive models for the relevant information filtering. However, the active relevant feedback
provided by the human annotators to update the models are not validated. As a result, they can introduce
unconscious biases in the learning process of humans and can result in an inaccurate or inefficient predictive
system. Therefore, this paper describes the design and implementation of an open-source technology-based
learning analytics system ? EMAssistant ? for the emergency volunteers or practitioners – referred as the trainee, to
enhance their experiential learning cycle with the cause-effect reasoning on providing relevant feedback to the
machine learning model. This continuous integration between the cause (providing feedback) and the effect
(observing predictions from the updated model) in a visual form will likely to improve the understanding of the
trainees to provide more accurate feedback. We propose to present the system design as well as provide
hands-on exercises for the conference session.
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Rajius Idzalika, Yulistina Riyadi, Imaduddin Amin, George Hodge, & Jong Gun Lee. (2019). Understanding Aggregate Human Behaviour Changes in Response to a Natural Disaster in Vanuatu via Mobile Network Data Analysis. 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: This paper presents a use case of mobile network data as a new source of insight for humanitarian action. Based on analysis covering the Republic of Vanuatu, we identify aggregate behavioral patterns indicating short term and medium term behavior changes as a result of a tropical cyclone, which could contribute to our understanding of the resilience of communities to natural hazards. We also find interesting behavioral insights on how the human movement network is impacted by a cyclone. Due to the detail and tractability of the data set, insights on preparation, displacement, damage and resilience could enable more agile and adaptive responses by public institutions and other actors to humanitarian emergencies. Considering the array of natural hazards that the South Pacific region faces on an annual basis, this use case contributes more evidence in favor of using anonymized mobile network data to inform humanitarian action.
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Richard McCreadie, Cody Buntain, & Ian Soboroff. (2019). TREC Incident Streams: Finding Actionable Information on Social Media. 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: The Text Retrieval Conference (TREC) Incident Streams track is a new initiative that aims to mature social
media-based emergency response technology. This initiative advances the state of the art in this area through an
evaluation challenge, which attracts researchers and developers from across the globe. The 2018 edition of the track
provides a standardized evaluation methodology, an ontology of emergency-relevant social media information types,
proposes a scale for information criticality, and releases a dataset containing fifteen test events and approximately
20,000 labeled tweets. Analysis of this dataset reveals a significant amount of actionable information on social
media during emergencies (> 10%). While this data is valuable for emergency response efforts, analysis of the
39 state-of-the-art systems demonstrate a performance gap in identifying this data. We therefore find the current
state-of-the-art is insufficient for emergency responders? requirements, particularly for rare actionable information
for which there is little prior training data available.
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Rob Grace, Shane Halse, Jess Kropczynski, Andrea Tapia, & Fred Fonseca. (2019). Integrating Social Media in Emergency Dispatch via Distributed Sensemaking. 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: Emergency dispatchers typically answer 911 calls and relay information to first responders; however, new workflows arise when social media analysts are included in emergency dispatch work. In this study we examine emergency dispatch workflows as distributed sensemaking processes performed among 911 call takers, dispatchers, and social media analysts during simulated emergency dispatch operations. In active shooter and water rescue scenarios, emergency dispatch teams including call takers, dispatchers, and social media analysts make sense of unfolding events by analyzing, aggregating, and synthesizing information provided by 911 callers and social media users during each scenario. Findings from the simulations inform design requirements for social media analysis tools that can help analysts detect, seek, and analyze information posted on social media during a crisis, and protocols for coordinating analysts? sensemaking activities with those of 911 call takers and dispatchers in reconfigured emergency dispatch workflows.
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Robin Batard, Aurélie Montarnal, Frédérick Benaben, Caroline Rizza, Christophe Prieur, & Andrea Tapia. (2019). Integrating citizen initiatives in a technological platform for collaborative crisis management. 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: Although they can make a significant contribution to crisis response and management, citizens tend to be underestimated and under-integrated by official crisis responders. There is a necessity to take citizen contribution into crisis management tools, both for the information they can provide (information-focused volunteers) and the actions they can carry out (action-focused volunteers). Therefore, professionals need to be aware of the diverse ways citizens can help them to manage a critical situation: obviously by improving the situational awareness, but also by spontaneously performing tasks to meet specific needs on the ground.
After presenting the RIO-Suite software, a crisis management tool based on collaboration of stakeholders, this paper suggests ideas about how to make the most of action-focused volunteers to improve the orchestration of the crisis response. Given a volunteer action, four possible decision types are identified: Ignore, Stop, Consider and Support, and their consequences on 1) the crisis response and 2) the collaboration process are presented.
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Robin Batard, Caroline Rizza, Aurélie Montarnal, Frédérick Bénaben, & Christophe Prieur. (2019). Taxonomy of post-impact volunteerism types to improve citizen integration into crisis response. 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: Information and Communication Technologies (ICT), and particularly Social Media, drastically changed communication channels and organization during a crisis response. In this context, new forms of citizen initiatives appear, contributing to situational awareness, providing new profiles of stakeholders and broadening the scope of volunteerism in disaster situations. Thus, given the increasing need to understand and take citizen initiatives into account, this article provides a taxonomy of volunteerism types in crisis contexts, based on a literature review on the subject. Mapped on two main dimensions: the status (who they are) and the focus (what they are doing), multiple types of volunteers are presented on this taxonomy. Then, the article deals with possible use of this taxonomy towards integration of citizen initiatives into the crisis response.
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Samer Cheade, Nada Matta, Jean-Baptiste Pothin, & Remi Cogranne. (2019). Situation Representation and Awareness for Rescue Operations. 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 rescue operations, being aware of the situation is very critical for rescuers and decision-makers to reduce the impacts. This work aims to support situation awareness amongst actors participating in rescue operations by adopting an ontology-based approach. An application ontology is proposed based on existing related ontologies and operational expertise collection. It will help to ensure common situation representation and understanding between different actors. After that, a knowledge-based system will be developed and integrated in actors? environment to support decision-making. Our preliminary results are shown in this paper.
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Sammy Abdelghani Teffali, Nada Mattta, & Eric Chatelet. (2019). Generating Crisis Situation by Using Ontology and Fuzzy Theory. 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: A crisis is a complex situation, difficult to manage by the actors. Some of them are under stress it is difficult to
deal with problems when consequences cannot be predict. The human conditions (concerning familial and life)
and, the influence of the environment related to politics, economic, and media pushe the actors to lose control of
the crisis situation. The question we face in this paper is: ?is it possible to use the fuzzy theory for predicting the
stress impact in crisis?? Our main hypothesis to represent experience feedback in a situation prediction in order
to show negative consequences and correctness actions is taken account. Fuzzy theory concept is used in
prediction in order to generate several situations.
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Sandra König. (2019). Choosing Ways to Increase Resilience in Critical Infrastructures. 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: Increasing resilience is a core interest in critical infrastructure (CI) protection that involves many challenges. It is necessary to agree on a common understanding of resilience and identify potential strategies to improve it.
Once this is done, the question arises how to choose among these strategies. We propose to decide based on a game-theoretic framework that allows identification of optimal actions under various scenarios. This framework considers different threat scenarios as attacks to the CI and the identified strategies to improve resilience as defense strategies for the CI. Since the payoff of the game, namely the resilience of the CI, can hardly be measured with certainty we choose an extension of classical game theory that allows taking uncertainty into account and still finds provably optimal solutions. This approach is especially useful in a situation where we aim to optimize a quantity that is difficult to measure (such as resilience). The result of this analysis is two-fold: it identifies an optimal defense but also provides information about the resilience in the worst case. The approach is illustrated with a small example using a publicly available implementation.
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Sandra König, & Stefan Schauer. (2019). Cascading Threats in Critical Infrastructures with Control Systems. 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: Critical infrastructures (CIs) increase in complexity due to numerous dependencies on other CIs but also due to the ongoing digitalization in the industry sector. This yields an increased risk of failure of a single CI as the overall systems gets very fragile and sensitive to errors Failure of a single component may affect large parts of an infrastructure due to cascading effects. One way to support functionality of a CI is the use of Industrial Control Systems (ICS) that allow monitoring remote sites and controlling processes. However, this is an additional source for threats as recent cyber-attacks have shown. Further, the additional information for such cyber systems is often not efficiently combined with existing information on the physical infrastructure. We here propose a method to combine these two sources of information in order to estimate the impact of a security incident on CIs, taking into account cascading effects of threats. An implementation of the model allows simulation of the dynamics inside a CI and yields a record of the status of each asset of the CI. The way the assets change their states illustrates the consequences of an incident on the entire CI. Visualization of the results provides an overview on the situation of the entire CI at a certain point of time and a sequence of such visualization over an entire period of time illustrates the changes over time. The results from this analysis may be used to support security officers in analyzing the current (hybrid) state of their CI in case of an incident and thus increase the hybrid situational awareness.
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Sara Barozzi, Jose Luis Fernandez Marquez, Amudha Ravi Shankar, & Barbara Pernici. (2019). Filtering images extracted from social media in the response phase of emergency events. 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: The use of social media to support emergency operators in the first hours of the response phases can improve the
quality of the information available and awareness on ongoing emergency events. Social media contain both textual
and visual information, in the form of pictures and videos. The problem related to the use of social media posts
as a source of information during emergencies lies in the difficulty of selecting the relevant information among
a very large amount of irrelevant information. In particular, we focus on the extraction of images relevant to an
event for rapid mapping purpose. In this paper, a set of possible filters is proposed and analyzed with the goal of
selecting useful images from posts and of evaluating how precision and recall are impacted. Filtering techniques,
which include both automated and crowdsourced steps, have the goal of providing better quality posts and easy
manageable data volumes both to emergency responders and rapid mapping operators. The impact of the filters on
precision and recall in extracting relevant images is discussed in the paper in two different case studies.
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Sebastian Lindner, Stefan Sackmann, & Hans Betke. (2019). Simulating Spontaneous Volunteers: A System Entity Structure for Defining Disaster Scenarios. 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: Fast and easy communication, e.g. via Twitter or Facebook, encourages self-coordination between spontaneous
volunteers in disasters. Unfortunately, this is more and more challenging official disaster management. The need
for the directed coordination of spontaneous volunteers triggered researchers to develop effective coordination
approaches. However, evaluating and comparing such approaches as well as their exercising are lacking a
standardized way to describe repeatable disaster scenarios, e.g. for simulations. Therefore, we present a novel
System Entity Structure (SES) for describing disaster scenarios considering the disaster environment,
communication infrastructure, disaster management, and population of spontaneous volunteers. The SES is
discussed as a promising scheme for including spontaneous volunteers in disaster scenarios on a general level. Its
applicability is demonstrated by a Pruned Entity Structure derived from a real disaster scenario. Based on the
results, we give an outlook on our subsequent research, the XML-based Spontaneous Volunteer Coordination
Scenario Definition Language (SVCSDL).
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Sebastian Schmitz, Konrad Barth, Tim Brüstle, Tobias Gleibs, & Ompe Aimé Mudimu. (2019). Testing the implementation of a flying localization system into emergency response using a tabletop exercise. 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: To optimize the search for trapped victims after building collapses, the authors participated in the development ofI a localization system based on an unmanned aerial vehicle. The objective of this study is to evaluate an approach to implement this system into the command and control structures during the emergency response after a building collapse. For this purpose, a tabletop exercise, based on a gas explosion scenario in an apartment building, was carried out with emergency response managers of the fire department and the German federal agency of technical relief. Observers have documented the exercise. Additionally, audio and video recordings were used. Thus, statements could be made about the implementation approach and the tabletop exercise method. Based on the results, the implementation approach can be considered appropriate. In addition, knowledge was gained about the appropriateness of tabletop exercises for the purpose of scientific evaluation.
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Shane Errol Halse, Rob Grace, Jess Kropczynski, & Andrea Tapia. (2019). Simulating real-time Twitter data from historical datasets. 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: In this paper, we will discuss a system design for simulating social media data based on historical datasets. While many datasets containing data collected from social media during crisis have become publicly available, there is a lack of tools or systems can present this data on the same timeline as it was originally posted. Through the design and use of the tool discussed in this paper, we show how historical datasets can be used for algorithm testing, such as those used in machine learning, to improve the quality of the data. In addition, the use of simulated data also has its benefits in training scenarios, which would allow participants to see real, non-fabricated social media messages in the same temporal manner as found on a social media platform. Lastly, we will discuss the positive reception and future improvements suggested by 911 Public Service Answering Point (PSAP) professionals.
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Sooji Han, & Fabio Ciravegna. (2019). Rumour Detection on Social Media for Crisis Management. 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: We address the problem of making sense of rumour evolution during crises and emergencies. We study how
understanding and capturing emerging rumours can benefit decision makers during such event. To this end, we
propose a two-step framework for detecting rumours during crises. In the first step, we introduce an algorithm to
identify noteworthy sub-events in real time. In the second step, we introduce a graph-based text ranking method
for summarising newsworthy sub-events while events unfold. We use temporal and content-based features to
achieve the effective and real-time response and management of crises situations. These features can improve
efficiency in the detection of key rumours in the context of a real-world application. The effectiveness of our
method is evaluated over large-scale Twitter data related to real-world crises. The results show that our framework
can efficiently and effectively capture key rumours circulated during natural and human-made disasters.
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