Samuel Lee Toepke. (2017). Temporal Sampling Implications for Crowd Sourced Population Estimations from Social Media. 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. 564–571). Albi, France: Iscram.
Abstract: Understanding the movements of a population throughout the 24-hour day is critical when directing disaster response in an urban area. An emergency situation can develop rapidly, and understanding the expected locations of groups of people is required for the success of first responders. Recent advances in modern consumer technologies have facilitated the generation, sharing and mining of an extensive amount of volunteered geographic information. Users leverage inexpensive smart devices, pervasive Internet connections and social media services to provide data about geospatial locations. Using an enterprise system, it is possible to aggregate this freely available, geospatially enabled data and create a population estimation with high spatiotemporal resolution, via a heat map. This investigation explores the effects of different temporal sampling periods when creating such estimations. Time periods are selected, estimations are generated for several large urban areas in the western United States, and comparisons of the results are shown/discussed.
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Schreiber. (2007). Automatic generation of sensor queries in a WSN for environmental monitoring. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 245–254). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The design of a WSN for environmental data monitoring is a largely ad-hoc human process. In this paper, we propose the automatic generation of queries for sensor data extraction, based on the collection of a number of parameters concerning the physical phenomenon to be controlled, the relevant physical variables, the types of sensors to be deployed and their allocation, the data collection frequencies, and other features.
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Alec Pawling, Tim Schoenharl, Ping Yan, & Greg Madey. (2008). WIPER: An emergency response system. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 702–710). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper describes the WIPER system, a proof of concept prototype, and progress made on its development to date. WIPER is intended to provide emergency response managers with an integrated system that detects possible emergencies from cellular communication data, attempts to predict the development of emergency situations, and provides tools for evaluating possible courses of action in dealing with emergency situations. We describe algorithms for detecting anomalies in streaming cellular communication network data, the implementation of a simulation system that validates running simulations with new real world data, and a web-based front end to the WIPER system. We also discuss issues relating to the real-time aggregation of data from the cellular service provider and its distribution to components of the WIPER system.
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Yan, S. (2005). Design of enterprise crisis predicting system based on cluster and outlier data mining. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management (pp. 143–145). Brussels: Royal Flemish Academy of Belgium.
Abstract: In order to solve such problems as half-structured and non-structured data analysis in enterprise crisis predicting system, a predicting system based on cluster and outlier data mining is put forward. The system organization, frame construction, function and working principles are illustrated. And the working process is showed by an example of cheat predicting. The experimental results show that this method is efficient and it is a new way to solve such problems.
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Axel Schulz, Tung Dang Thanh, Heiko Paulheim, & Immanuel Schweizer. (2013). A fine-grained sentiment analysis approach for detecting crisis related microposts. 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. 846–851). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Real-time information from microposts like Twitter is useful for applications in the crisis management domain. Currently, that potentially valuable information remains mostly unused by the command staff, mainly because the sheer amount of information cannot be handled efficiently. Sentiment analysis has been shown as an effective tool to detect microposts (such as tweets) that contribute to situational awareness. However, current approaches only focus on two or three emotion classes. But using only tweets with negative emotions for crisis management is not always sufficient. The amount of remaining information is still not manageable or most of the tweets are irrelevant. Thus, a more fine-grained differentiation is needed to identify relevant microposts. In this paper, we show the systematic evaluation of an approach for sentiment analysis on microposts that allows detecting seven emotion classes. A preliminary evaluation of our approach in a crisis related scenario demonstrates the applicability and usefulness.
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