| Prediction of 9-1-1 call volumes for emergency event detection |
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dg.o; Vol. 228
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Proceedings of the 8th annual international conference on Digital government research: bridging disciplines & domains
table of contents
Philadelphia, Pennsylvania
SESSION: Crisis management
table of contents
Pages: 148 - 154
Year of Publication: 2007
ISBN:1-59593-599-1
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Authors
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Hector Jasso
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University of California, San Diego, La Jolla, CA
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Tony Fountain
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University of California, San Diego, La Jolla, CA
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Chaitan Baru
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University of California, San Diego, La Jolla, CA
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William Hodgkiss
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University of California, San Diego, La Jolla, CA
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Don Reich
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Public Safety Network, Santa Barbara, CA
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Kurt Warner
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Public Safety Network, Santa Barbara, CA
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Downloads (6 Weeks): 3, Downloads (12 Months): 24, Citation Count: 1
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ABSTRACT
A multi-dimensional linear predictor of 9-1-1 (emergency) call volumes was built and used to automatically detect emergency events. This is illustrated by analyzing the emergency calls generated in two emergency events in the San Francisco Bay Area. The predictor can help emergency service providers recognize the occurrence of anomalously large numbers of 9-1-1 calls and subsequently map the spatiotemporal extent of wide-scale emergency events such as earthquakes and fires, complementing the effort of individual 9-1-1 emergency call takers.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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Hector Jasso , Tony Fountain , Chaitan Baru , William Hodgkiss , Don Reich , Kurt Warner, Spatiotemporal analysis of 9-1-1 call stream data, Proceedings of the 2006 international conference on Digital government research, May 21-24, 2006, San Diego, California
[doi> 10.1145/1146598.1146608]
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CITED BY
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Hector Jasso , Chaitan Baru , Tony Fountain , William Hodgkiss , Don Reich , Kurt Warner, Using 9-1-1 call data and the space-time permutation scan statistic for emergency event detection, Proceedings of the 2008 international conference on Digital government research, May 18-21, 2008, Montreal, Canada
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