|
|
Katja Schulze, Daniel Lorenz, Bettina Wenzel, & Martin Voss. (2015). Disaster Myths and their Relevance for Warning Systems. 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: Warning systems are technically, socially, and organizationally shaped and rest on specific assumptions concerning human behavior during disasters. The common notions about people?s behavior in disaster situations are often not based on empirical data, but rather on so-called ?myths? which overemphasize rare and situation-dependent extreme behaviors such as panic, disaster shock, looting or helplessness. Due to the fact that these expectations are shaped within social environments, different stakeholders such as a heterogeneous population and professionals exhibit different assumptions. These assumptions may not only be misplaced, they additionally interfere with warning systems. The paper compares empirical results of three connected surveys: a comprehensive document analysis on disaster behavior, qualitative interviews with disaster relief workers and a quantitative representative poll. By contrasting the status of research with professional narrations as well as with the people?s expectations, different expectations and their variations are explored.
|
|
|
|
Sigmund Kluckner, Johannes Sautter, Matthias Max, Wolf Engelbach, & Tina Weber. (2012). Impacting factors on human reactionsto alerts. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Crisis response authorities have to deal with the unpredictability of their population's behavior. One of the complex challenges is to understand the people's reaction after an official alert in a crisis situation has been issued. This paper elaborates a knowledge base to describe impacting factors on human reactions in alerting situations. For this purpose, a literature review in the theme of human behavior after warnings was conducted and augmented with information gathered in a series of interviews in German-speaking countries. The outcome is phrased as factors that might impact the human reaction to a warning. This knowledge base shall support crisis management practitioners in the elaboration of alerting strategies as well as allow researchers to systematically structure human behavior aspects for the purpose of modeling and simulating alert effects. © 2012 ISCRAM.
|
|
|
|
Ma Ma, Shengcheng Yuan, H. Zhang, & Yi Liu. (2013). Framework design for operational scenario-based emergency response system. 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. 332–337). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The present paper introduces a scenario-based framework design for connecting emergency response system with human behavior analysis and social information processing, which aims at improving its comprehensive capability in dealing with unexpected situations caused by physical, social and psychological factors during a crisis. The overall framework consists of four function modules: Scenario awareness, scenario analysis, scenario evolvement and scenario response. A detailed function design for each module is presented as well as the related methodologies used for integration of four modules. The contribution of this paper includes two aspects. One is realizing the integration of incident evolution, information-spreading and decision-making by taking account of physical, social and psychological effects during emergency. The other is improving the efficiency of decisionmaking through dynamic optimization process.
|
|
|
|
Rouba Iskandar, Julie Dugdale, Elise Beck, & Cécile Cornou. (2021). PEERS: An integrated agent-based framework for simulating pedestrians' earthquake evacuation. 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. 86–96). Blacksburg, VA (USA): Virginia Tech.
Abstract: Traditional seismic risk assessment approaches focus on assessing the damages to the urban fabric and the resultant socio-economic consequences, without adequately incorporating the social component of risk. However, the human behavior is essential for anticipating the impacts of an earthquake, and should be included in quantitative risk assessment studies. This paper proposes an interdisciplinary agent-based modeling framework for simulating pedestrians' evacuation in an urban environment during and in the immediate aftermath of an earthquake. The model is applied to Beirut, Lebanon and integrates geo-spatial, socio-demographic, and quantitative behavioral data corresponding to the study area. Several scenarios are proposed to be explored using this model in order to identify the influence of relevant model parameters. These experiments could contribute to the development of improved of emergency management plans and prevention strategies.
|
|
|
|
Yudi Chen, Angel Umana, Chaowei Yang, & Wenying Ji. (2021). Condition Sensing for Electricity Infrastructures in Disasters by Mining Public Topics from Social Media. 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. 598–608). Blacksburg, VA (USA): Virginia Tech.
Abstract: Timely and reliable sensing of infrastructure conditions is critical in disaster management for planning effective infrastructure restorations. Social media, a near real-time information source, has been widely used in the disaster domain for building timely, general situational awareness, such as urgent public needs and donations. However, the employment of social media for sensing electricity infrastructure conditions has yet been explored. This study aims to address the research gap to sense electricity infrastructure conditions through mining public topics from social media. To achieve this purpose, we proposed a systematic and customized approach wherein (1) electricity-related social media data is extracted by the classifier developed based on Bidirectional Encoder Representations from Transformers (BERT); and (2) public topics are modeled with unigrams, bigrams, and trigrams to incorporate the formulaic expressions of infrastructure conditions in social media. Electricity infrastructures in Florida impacted by Hurricane Irma are studied for illustration and demonstration. Results show that the proposed approach is capable of sensing the temporal evolutions and geographic differences of electricity infrastructure conditions.
|
|
