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Anja Van Der Hulst, Rudy Boonekamp, & Marc Van Den Homberg. (2014). Field-testing a comprehensive approach simulation model. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 575–584). University Park, PA: The Pennsylvania State University.
Abstract: This paper describes the field tests of a simulation based game aiming at raising awareness and creating a deeper understanding of the dynamics of the comprehensive approach (CA). The setting of this game is that of a failed state where an UN intervention takes place after massive conflict that requires a CA to stabilize the situation. That is, the civil and military actors need to collaborate effectively, taking into account their respective strengths, mandates and roles. Underlying the game is the Go4it CA simulation Model (GCAM2.0). GCAM2.0 was extensively field-tested in eight sessions with about 16 persons each, aiming at assessment of the perceived realism and learning effects. It was found to provide a sufficiently authentic experience to obtain awareness of the CA in novices. With regard to improving the deeper understanding of the dynamics and complexity of the CA, in a cooperation-oriented setting only deeper learning can be reached.
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Marc van den Homberg, Jannis Visser, & Maarten van der Veen. (2017). Unpacking Data Preparedness from a humanitarian prioritization perspective: towards an assessment framework at subnational level (eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes, Ed.). Albi, France: Iscram.
Abstract: All too often the collection as well as analysis of data for humanitarian response only starts once a disaster hits. This paper proposes a framework to assess Data Preparedness on five dimensions: Data Sets, Data Services and Tooling, Data Governance, Data Literacy, and Networked Organizations for Data. We demonstrate for one dimension, i.e. Data Sets, how it can be quantified. First step is to determine which Data Sets must be collected before a disaster strikes so that as many as possible decision-makers' information needs are covered. Subsequently, a Data Sets Preparedness Index can be calculated based on Completeness, Recency and Accuracy & Reliability. We tested the index for Malawi and The Philippines and show how it can be used to direct data collection and determine when data analysis for e.g. predicting severity becomes meaningful. The index can be modified for reporting on global policies such as the Sustainable Development Goals.
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Marc van den Homberg, Lydia Cumiskey, Esther Oprins, Pablo Suarez, & Anja van der Hulst. (2015). Are you Ready! to take early action? Embedding serious gaming into community managed DRR in Bangladesh. 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: This paper applies a Game-based Learning Evaluation Model (GEM) to assess whether the early warning ? early action serious game ?Ready!? is an effective component to add to existing Disaster Risk Reduction (DRR) training curricula, facilitated by NGO staff and applied at the community level. We developed a paper-based survey with 17 five-level Likert items and 15 open questions addressing the different GEM indicators to question 16 NGO staff, and used a simplified set of five questions with emoticons for 58 community people. The results showed that the staff saw great potential in embedding Ready! in DRR processes and that the community highly appreciated the game. The GEM was found to be a useful methodology to evaluate the effectiveness of this serious game. However, in the context of a lower educated and partly illiterate community, the importance of designing an individual, largely visual assessment instrument instead of a paper-based survey was acknowledged.
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Stella van Esch, Marc van den Homberg, & Kees Boersma. (2021). Looking Beyond the Data: an Assessment of the Emerging Data Ecosystem of Nepal's Flood Early Warning Systems. 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. 282–293). Blacksburg, VA (USA): Virginia Tech.
Abstract: Increasingly, data-driven instruments are used in disaster risk reduction to foster more efficient, effective, and evidence-based decision-making. This data revolution brings along opportunities and challenges, which are sometimes related to the data itself, but more often seem related to the environment in which the data is put to use. To provide insight into such an emerging data ecosystem, this paper uses a qualitative case study to assess the use of data in flood early warning systems (EWS) in Nepal. In response to the research question 'How does the data ecosystem impact the opportunities and challenges regarding data use in flood early warning systems in Nepal?', this paper discusses the importance of considering the broader context instead of regarding data as an entity unto itself. It shows how actors, policies and other contextual factors impact the effectiveness of data use by either presenting opportunities, like the establishment of a national disaster data repository, or challenges, like inadequate human resources for working with data.
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