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Clustering spatial data streams for targeted alerting in disaster response

 
: Mehta, Paras; Voisard, Agnes; Müller, Sebastian

:

Association for Computing Machinery -ACM-; Association for Computing Machinery -ACM-, Special Interest Group on Spatial Information -SIGSPATIAL-:
IWGS 2013, 4th ACM SIGSPATIAL International Workshop on GeoStreaming. Proceedings : November 5, 2013, Orlando, Florida, USA
New York: ACM, 2013
ISBN: 978-1-4503-2532-5
S.66-75
International Workshop on GeoStreaming (IWGS) <4, 2013, Orlando/Fla.>
International Conference on Advances in Geographic Information Systems (GIS) <21, 2013, Orlando/Fla.>
Englisch
Konferenzbeitrag
Fraunhofer FOKUS ()

Abstract
Natural calamities and man-made hazards can occur in an unexpected and unanticipated manner. They cause large-scale damage, create disruptions, and need instant reaction. In the event of sudden onset of a crisis, rapid formulation of a notification strategy, timely dispatch of alerts, and action on those alerts are important elements of early warning systems that can save lives. However, current methods of disaster alerting lack in the area of targeted communication of hazard information. Location data of the population available as a spatial data stream can allow dynamic identification of homogeneous clusters of people. Crisis notifications can then be targeted by personalizing information and instructions for each cluster. In this paper, we present an approach for dynamically partitioning a region into areas around a hazard using clustering of real-time streaming data to aid emergency response management. We lay down important requirements for the clustering technique from the perspective of our scenario and select an algorithm for our implementation after comparison with others. We employ a weighted distance measure and demonstrate the performance of our model in different settings through a series of experiments using a dataset of cell tower locations of users in Ivory Coast in Africa.

: http://publica.fraunhofer.de/dokumente/N-283320.html