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  1. Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS '13)
  2. Clustering spatial data streams for targeted alerting in disaster response
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Towards window stream queries over continuous phenomena
Spatiotemporal transformation of social media geostreams: a case study of Twitter for flu risk analysis
Enhanced database support for location-based services
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Automatic identification of points of interest in global navigation satellite system data: a spatial temporal approach
EHSTC: an enhanced method for semantic trajectory compression
ADTOS: arrival departure tradeoff optimization system
Geo-clouds: visualizing news over geographical maps
Clustering spatial data streams for targeted alerting in disaster response
Continuum: a spatiotemporal data model to represent and qualify filiation relationships
Mining robust neighborhoods for quality control of sensor data

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Clustering spatial data streams for targeted alerting in disaster response

Content Provider ACM Digital Library
Author Müller, Sebastian Mehta, Paras Voisard, Agnès
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.
Starting Page 66
Ending Page 75
Page Count 10
File Format PDF
ISBN 9781450325325
DOI 10.1145/2534303.2534307
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-11-05
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Targeted alerting Early warning Disaster response Stream clustering
Content Type Text
Resource Type Article
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