Disaster Information, Resilience, for Emergency and Crisis Technologies
Permanent URI for this collectionhttps://hdl.handle.net/10125/107467
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Item type: Item , Adopting a Trading Zone Framework to Emergency Management(2024-01-03) Rustenberg, Kjetil; Radianti, Jaziar; Gjøsæter, TerjeThis paper presents a novel framework for identifying and detailing the processes of Information Trading Zones in Emergency Management. Drawing inspiration from various scientific disciplines, we demonstrate the applicability of these insights within Emergency Management in the Information Systems domain through a detailed case study. By leveraging these adapted concepts, our framework enhances understanding of information exchange and collaboration in emergency response efforts. Through this research, we shed light on the relevance and effectiveness of the framework in addressing the unique and dynamic challenges of Information Management in Emergency Management scenarios.Item type: Item , Simulating casualty transportation and allocation policies for mass casualty incident scenarios(2024-01-03) Hager, Florentina; Metz, Franziska B.; Reuter-Oppermann, MelanieDuring mass casualty events, fast medical care must be provided to often many affected individuals. When resources like ambulances are limited, decision makers may need to consider incorporating mass transportation vehicles to assist with transportation. With time being a crucial factor, on-field decisions are typically based on practical and straightforward policies, such as sending casualties to the closest hospital until the capacity limit is reached. However, the integration of mass transportation vehicles necessitates a re-evaluation of these conventional policies. Therefore, this study aims to develop and evaluate various casualty transportation and allocation policies during mass casualty incidents using discrete-event simulation. An important feature of this study is the differentiation between life-threateningly and severely injured casualties. Life-threateningly injured casualties require immediate transportation to a hospital, whereas the latter can be considered stable enough to be transported using alternative modes of transportation, freeing up ambulances and decreasing overall transportation times.Item type: Item , "Listening In": Social Signal Detection for Crisis Prediction(2024-01-03) Janzen, Sabine; Saxena, Prajvi; Baer, Sebastian; Maass, WolfgangCrises send out early warning signals; mostly weak and difficult to detect amidst the noise of everyday life. Signal detection based on social media enables early identification of such signals supporting pro-active organizational responses before a crisis occurs. Nonetheless, social signal detection based on Twitter data is not applied in crisis management in practice as it is challenging due to the high volume of noise. With OSOS, we introduce a method for open-domain social signal detection of crisis-related indicators in tweets. OSOS works with multi-lingual Twitter data and combines multiple state-of-the-art models for data pre-processing (SoMaJo) and data filtration (GPT-3). It excels in crisis domains by leveraging fine-tuned GPT-3\textsuperscript{FT} (Curie) model and achieving benchmark results in the CrisisBench dataset. The method was exemplified within a signaling service for crisis management. We were able to evaluate the proposed approach by means of a data set obtained from Twitter (X) in terms of performance in identifying potential social signals for energy-related crisis events.Item type: Item , Recognizing Similar Crises through the Application of Ontology-based Knowledge Mining(2024-01-03) Lê, Ngoc Luyen; Abel, Marie-Hélène; Negre, ElsaRecognizing and learning from similar crisis situations is crucial for the development of effective response strategies. This study addresses the challenge of identifying similarities within a wide range of crisis-related information. To overcome this challenge, we employed an ontology-based crisis situation knowledge base enriched with crisis-related information. Additionally, we implemented a semantic similarity measure to assess the degree of similarity between crisis situations. Our investigation specifically focuses on recognizing similar crises through the application of ontology-based knowledge mining. Through our experiments, we demonstrate the accuracy and efficiency of our approach to recognizing similar crises. These findings highlight the potential of ontology-based knowledge mining for enhancing crisis recognition processes and improving overall crisis management strategies.Item type: Item , Damaged Building Detection using Multiple Object Tracking and Decision Tree from Aerial Videos for Disaster Response(2024-01-03) Fujita, Shono; Hatayama, MichinoriInformation about damaged buildings is crucial in disaster response owing to the risk they pose, including property damage and loss of human lives. However, it is difficult to capture this information rapidly during disaster. This study developed an automatic model to detect buildings damaged by earthquakes from aerial videos. It is composed of multiple object tracking model of buildings, classification model of damage, and decision tree model to output final estimation by each track. This system considers; (1) detection of damaged and collapsed buildings such as pancake collapse of wooden buildings and significant roof damage, (2) input of time-series information to determine the extent of building damage, (3) less annotation labor to train datasets, and (4) effective usage of decision tree nodes for disaster response. The obtained results indicated that the average recall of three classes was 47.9%, average precision was 48.4%, and average F-measure was 45.7%.Item type: Item , Motivated Bias in Detecting Climate Change Misinformation(2024-01-03) Fong, Mia; John, RichardMisinformation undermines a shared understanding of the complexity of climate change and impedes public support for mitigation policies to build resilience. This paper reports an empirical study (N=398) of U.S. adults’ accuracy and bias in identifying true and false headlines related to climate change. The headlines were evenly balanced such that half were true and half false (fake news); likewise, half emphasized, and half questioned the urgency of climate change. Respondents indicated whether they believed each of the 24 headlines was true or false and provided confidence ratings. Pooled ROC analyses suggested moderate accuracy (AUC = 0.68) and a bias (c=0.09) favoring accuracy for false headlines (specificity=0.67) and attenuating the accuracy rate for true headlines (sensitivity = 0.60). Regression analysis on individual Signal Detection Theory (SDT) performance measures revealed that political liberalism and actively open-minded thinking (AOT) positively predicted accuracy (AUC), while climate change anxiety (CCAS) positively predicted bias.Item type: Item , A Design Theory for Spontaneous Volunteer Coordination Systems in Disaster Response(2024-01-03) Betke, Hans; Sperling, Martina; Schryen, Guido; Sackmann, StefanSpontaneous volunteers have always played an important role in responding to major disasters. Over the last 20 years, social media and mobile devices have both increased their potential as key players in disaster management and changed the way they organize themselves. However, one main challenge, especially for public authorities and security organizations, is how to integrate such spontaneous volunteers into official disaster management activities to realize their potential. Since several IT systems for coordinating spontaneous volunteers have been proposed and first practical experiences as well as evaluations are already at hand, developing a comprehensive design theory for such IT systems is aim of this paper. The design theory presented is based on interviews and focus groups with practitioners and supported by literature. We illustrate the applicability and usefulness of the developed design theory/principles through an instantiation with the spontaneous volunteer coordination system KUBAS and first exercise results.Item type: Item , Digital Resilience in Flux: A Comparative Analysis in Manufacturing Pre- and Post-Crisis(2024-01-03) Stein, Hannah; Janzen, Sabine; Haida, Benedikt; Maass, WolfgangMajor disruptions or crises such as COVID-19 or the war in Ukraine impact various industries such as manufacturing. These crises triggered declines in production due to lockdowns, problems in supply chains, and unavailability of production material. To cope with disruptions and crises, manufacturing companies need to be or become resilient. This work presents how major disruptions impact the digital resilience capabilities of manufacturing companies. By comparing the results of two surveys on digital resilience in manufacturing - one conducted in 2019 (pre-crisis), one replicated and conducted in 2023 (post-crisis) - it was found that major disruptions strengthened the capabilities of absorbing disruptions and adapting to disruptions in manufacturing companies.Item type: Item , Applying Crisis Informatics in Environmental Events in North America - A Systematic Review(2024-01-03) Yakubu, Mustapha; Botsyoe, Lily; Kropczynski, Jess; Johnson, Joe; Pang, Feifei; Gyamfi, Emmanuel KojoEnvironmental disasters caused by human activities are increasing. Despite the availability of data from many agencies, it can be challenging for citizens and local decision-makers to discover and make sense of information about an unfolding environmental disaster in a timely manner. Common operational picture tools (COPs) are standard tools in a crisis informatics toolbox; however, COPs are infrequently the type of technology used to share environmental information. This paper offers a systematic literature review of crisis informatics tools used to support decision-making during environmental events in North America. The results show that much of the literature in this space has focused on social media to support situation awareness, however, data from authoritative sources are not always present in this medium. We discuss these results in light of insights from the crisis informatics field to support situation awareness during a crisis.Item type: Item , Introduction to the Minitrack on Disaster Information, Resilience, for Emergency and Crisis Technologies(2024-01-03) Dugdale, Julie; Sakurai, Mihoko; Negre, Elsa; Benaben, Frederick