Adrot, A., & Aguerre, M. (2023). The Impact of conflicts on Data Sharing for Disaster Risk Reduction. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 43–53). Omaha, USA: University of Nebraska at Omaha.
Abstract: Cross-border regions are particularly exposed to hazards and require cooperation for Disaster Risk Reduction (DRR). Accordingly, multiple countries have been investing in technology and jointly innovate to share and process data against disasters. However, these countries’ capacity to share data depends on the history and the context of the border itself. Going further, conflicts between countries – may they be past, present or future – can radically question and endanger collaborative efforts to share data for DRR. This collaborative research, involving a cohort of master students and an NGO, explores the influence of conflicts on data sharing and cooperation for DRR. Still in progress, this research examines how conflicts affect data sharing and how DRR actors manage them. It is based on exploratory archive analysis of three distinct cases of cross-border DRR collaboration within the EU borders, as well as experts and actors interviewing. Preliminary findings reveal that conflicts affect DRR at three levels: i) actors, ii) interactions with DRR actors, iii) relations between local DRR actors and institutions. The expected contribution of this research is theoretical, practical and pedagogical.
|
Tiago Badre Marino, Bruno Santos Do Nascimento, & Marcos R. S. Borges. (2012). GIS supporting data gathering and fast decision making in emergencies situations. 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: This proposal rises from the Center for Disasters Scientific Support experience over eleven years supporting over a hundred disasters in Latin America. It also presents a case study applied to landslides assessments in Teresopolis (Brazil) city, when all field-generated knowledge was still registered in paper and later, at the base station, uploaded to database and finally available for managers evaluation and decision. The proposed methodology creates a platform (still under development) which allows online registration from different field agents during their evaluations enabling data upload combining mobile devices and telecommunication network (or Wi-Fi) technologies. Teams can also customize forms for different information classes (i.e. landslide assessment, rescued person, blocked road) and still retain the possibility to attach images, videos, other files related to each inspection. Incoming data are stored into a web database available for a real-time coordinators evaluation wherever they are (sometimes over a thousand of miles away from disaster area). © 2012 ISCRAM.
|
Fiona McNeill, Andriana Gkaniatsou, & Alan Bundy. (2014). Dynamic data sharing for facilitating communication during emergency responses. 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. 369–373). University Park, PA: The Pennsylvania State University.
Abstract: This paper describes the CHAIn system, which is designed to facilitate data sharing between disparate organisations during emergency response situations by resolving mismatches in their data. It uses structured data matching to reformulate failed queries in cases where these failed because of incompatibilities between the query (derived from the source schema) and the schema of the queried datasource (the target schema). This reformulation is done by developing matches between the source schema and the target schema. These matches are then used to reformulate the query and retrieve responses relevant to those expected by the original query. Despite the growing interest in intelligent query answering, integration of data matching into query answering is novel, and allows users to successfully query datasources even if they do not know how the data in that source is organized, which is often the case during emergency responses. We describe the proof-of-concept system we have developed and an encouraging initial evaluation.
|