James Hilton, & Nikhil Garg. (2023). Rapid Geospatial Processing for Hazard and Risk Management using the Geostack Framework. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 2–7). Palmerston North, New Zealand: Massey Unversity.
Abstract: Operational predictive and risk modelling of landscape-scale hazards such as floods and fires requires rapid processing of geospatial data, fast model execution and efficient data delivery. However, geospatial data sets required for hazard prediction are usually large, in a variety of different formats and usually require a complex pre-processing toolchain. In this paper we present an overview of the Geostack framework, which has been specifically designed for this task using a newly developed software library. The platform aims to provide a unified interface for spatial and temporal data sets, deliver rapid processing through OpenCL and integrate with web APIs or external graphical user interface systems to display and deliver results. We provide examples of hazard and risk use cases, particularly Spark, a Geostack based system for predicting the spread of wildfires. The framework is open-source and freely available to end users and practitioners in the hazard and geospatial space.
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Anouck Adrot, Samuel Auclair, Julien Coche, Audrey Fertier, Cécile Gracianne, & Aurélie Montarnal. (2022). Using Social Media Data in Emergency Management: A Proposal for a Socio-technical Framework and a Systematic Literature Review. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 470–479). Tarbes, France.
Abstract: Data represents an essential resource to the management of emergencies: organizations have been growingly investing in technologies and resources to lever data as an asset before, during, and after disasters and emergencies. However, research on data usage in emergency management remains fragmented, preventing practitioners and scholars from approaching data comprehensively. To address this gap, this research in progress consists of a systematic review of the literature in a two-steps approach: we first propose a socio-technical framework and use it in an exploratory mapping of the main topics covered by the literature. Our preliminary findings suggest that research on data usage primarily focuses on technological opportunities and affordances and, hence, lacks practical implementation aspects in organizations. The expected contribution is double. First, we contribute to a more comprehensive understanding of data usage in emergency management. Second, we propose future avenues for research on data and resilience.
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Firoj Alam, Ferda Ofli, & Muhammad Imran. (2019). CrisisDPS: Crisis Data Processing Services. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Over the last few years, extensive research has been conducted to develop technologies to support humanitarian aid
tasks. However, many technologies are still limited as they require both manual and automatic approaches, and
more importantly, are not ready to be integrated into the disaster response workflows. To tackle this limitation, we
develop automatic data processing services that are freely and publicly available, and made to be simple, efficient,
and accessible to non-experts. Our services take textual messages (e.g., tweets, Facebook posts, SMS) as input to
determine (i) which disaster type the message belongs to, (ii) whether it is informative or not, and (iii) what type of
humanitarian information it conveys. We built our services upon machine learning classifiers that are obtained from
large-scale comparative experiments utilizing both classical and deep learning algorithms. Our services outperform
state-of-the-art publicly available tools in terms of classification accuracy.
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Marcello Cinque, Christian Esposito, Mario Fiorentino, & Francisco Jose Perez Carrasco. (2015). A collaboration platform for data sharing among heterogeneous relief organizations for disaster management. 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: Recently, we are witnessing the progressive increase in the occurrence of large-scale disasters, characterized by an overwhelming scale and number of causalities. After 72 hours from the disaster occurrence, the damaged area is interested by assessment, reconstruction and recovery actions from several heterogeneous organizations, which need to collaborate and being orchestrated by a centralized authority. This situation requires an effective data sharing by means of a proper middleware platform able to let such organizations to interoperate despite of their differences. Although international organizations have defined collaboration frameworks at the higher level, there is no ICT supporting platform at operational level able to realize the data sharing demanded by such collaborative frameworks. This work proposes a layered architecture and a preliminary implementation of such a middleware for messaging, data and knowledge management. We also illustrate a demonstration of the usability of such an implementation, so as to show the achievable interoperability.
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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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