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Benjamin Herfort, João Porto De Albuquerque, Svend-Jonas Schelhorn, & Alexander Zipf. (2014). Does the spatiotemporal distribution of tweets match the spatiotemporal distribution of flood phenomena? A study about the River Elbe Flood in June 2013. 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. 747–751). University Park, PA: The Pennsylvania State University.
Abstract: In this paper we present a new approach to enhance information extraction from social media that relies upon the geographical relations between twitter data and flood phenomena. We use specific geographical features like hydrological data and digital elevation models to analyze the spatiotemporal distribution of georeferenced twitter messages. This approach is applied to examine the River Elbe Flood in Germany in June 2013. Although recent research has shown that social media platforms like Twitter can be complementary information sources for achieving situation awareness, previous work is mostly concentrated on the classification and analysis of tweets without resorting to existing data related to the disaster, e.g. catchment borders or sensor data about river levels. Our results show that our approach based on geographical relations can help to manage the high volume and velocity of social media messages and thus can be valuable for both crisis response and preventive flood monitoring.
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Benjamin Herfort, Melanie Eckle, João Porto de Albuquerque, & Alexander Zipf. (2015). Towards assessing the quality of volunteered geographic information from OpenStreetMap for identifying critical infrastructures. 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: Identifying the assets of a community that are part of its Critical Infrastructure (CI) is a crucial task in emergency planning. However, this task can prove very challenging due to the costs involved in defining the methodology and gathering the necessary data. Volunteered Geographic Information from collaborative maps such as OpenStreetMap (OSM) may be able to make a contribution in this context, since it contains valuable local knowledge. However, research is still due to assess the quality of OSM for the particular purpose of identifying critical assets. To fill this gap, this paper proposes a catalogue of critical asset types, based on the analysis of different reference frameworks. We thus analyze how good the emergent OSM data model is for representing these asset types, by verifying whether they can be mapped to tags used by the OSM community. Results show that critical asset types of all selected sectors and branches are well represented in OSM.
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Lívia Castro Degrossi, João Porto de Albuquerque, Roberto dos Santos Rocha, & Alexander Zipf. (2017). A Framework of Quality Assessment Methods for Crowdsourced Geographic Information: a Systematic Literature Review. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 532–545). Albi, France: Iscram.
Abstract: Crowdsourced Geographic Information (CGI) has emerged as a potential source of geographic information in different application domains. Despite the advantages associated with it, this information lacks quality assurance, since it is provided by different people. Therefore, several authors have started investigating different methods to assess the quality of CGI. Some of the existing methods have been summarized in different classification scheme. However, there is not an overview of the methods employed to assess the quality of CGI in the absence of authoritative data. On the basis of a systematic literature review, we found 13 methods that can be employed to this end.
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Melanie Eckle, Benjamin Herfort, Yingwei Yan, Chiao-Ling Kuo, & Alexander Zipf. (2017). Towards using Volunteered Geographic Information to monitor post-disaster recovery in tourist destinations. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 1008–1019). Albi, France: Iscram.
Abstract: The aftereffects of disaster events are significant in tourist destinations where they do not only lead to destruction and casualties, but also long-lasting economic harms. The public perception causes tourists to refrain from visiting these areas and recovery of the tourist industry, a major economic sector, to become challenging. To improve this situation, current information about the tourist and infrastructure recovery is crucial for a “rebranding”- information that is however time and cost-intensive in acquisition using traditional information sources. An alternative data source that has shown great potential for information gathering in other disaster management phases, which was less considered for disaster recovery purposes, is Volunteered Geographic Information (VGI). Therefore, this paper introduces a VGI-based methodology to address this task. Initial analyses conducted with Flickr data indicate a potential of VGI for recovery monitoring, whereas the analysis of OpenStreetMap data shows, that this form of VGI requires further quality assurance.
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Melanie Eckle, João Porto de Albuquerque, Benjamin Herfort, Alexander Zipf, Richard Leiner, Rüdiger Wolff, et al. (2016). Leveraging OpenStreetMap to Support Flood Risk Management in Municipalities: A Prototype Decision Support System. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: Floods are considered the most common and devastating type of disasters world-wide. Therefore, flood management is a crucial task for municipalities- a task that requires dependable information to evaluate risks and to react accordingly in a disaster scenario. Acquiring and maintaining this information using official data however is not always feasible, especially for smaller municipalities. This issue could be approached by integrating the collaborative maps of OpenStreetMap (OSM). The OSM data is openly accessible, adaptable and continuously updated. Nonetheless, to make use of this data for effective decision support, the OSM data must be first adapted to the needs of decision makers. In the pursuit of this goal, this paper presents the OpenFloodRiskMap (OFRM)- a prototype for a OSM based spatial decision-support system. OFRM builds an intuitive and practical interface upon existing OSM data and services to enable decision makers to utilize the open data for emergency planning and response.
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Michael Auer, Melanie Eckle, Sascha Fendrich, Luisa Griesbaum, Fabian Kowatsch, Sabrina Marx, et al. (2018). Towards Using the Potential of OpenStreetMap History for Disaster Activation Monitoring. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 317–325). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: “Over the last couple of years, the growing OpenStreetMap (OSM) data base repeatedly proved its potential for various use cases, including disaster management. Disaster mapping activations show increasing contributions, but oftentimes raise questions related to the quality of the provided \emph{Volunteered Geographic Information} (VGI). In order to better monitor and understand OSM mapping and data quality, we developed a software platform that applies big data technology to OSM full history data. OSM full history data monitoring allows detailed analyses of the OSM data evolution and the detection of remarkable patterns over time. This paper illustrates the specific potential of the platform for disaster activations by means of two case studies. Initial results demonstrate that our flexible and scalable platform structure enables fast and easy information extraction and supports mapping processes and data quality assurance.”
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Svend-Jonas Schelhorn, Benjamin Herfort, Richard Leiner, Alexander Zipf, & João Porto De Albuquerque. (2014). Identifying elements at risk from OpenStreetMap: The case of flooding. 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. 508–512). University Park, PA: The Pennsylvania State University.
Abstract: The identification of elements at risk is an essential part in hazard risk assessment. Especially for recurring natural hazards like floods, an updated database with information about elements exposed to such hazards is fundamental to support crisis preparedness and response activities. However, acquiring and maintaining an up-to-date database with elements at risk requires both detailed local and hazard-specific knowledge, being often a challenge for local communities and risk management bodies. We present a new approach for leveraging Volunteered Geographic Information to identify elements at risk from the free and open-source mapping project OpenStreetMap. We present initial results from a case study in the city of Cologne, Germany, to validate our approach in the case of flood-hazard. Our results show that the identification of elements at flood risk from OpenStreetMap is a suitable and cost-effective alternative for supporting local governments and communities in risk assessment and emergency planning.
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