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ASME Press Select Proceedings
International Conference on Computer and Electrical Engineering 4th (ICCEE 2011)
By
Jianhong Zhou
Jianhong Zhou
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ISBN:
9780791859841
No. of Pages:
698
Publisher:
ASME Press
Publication date:
2011

Hotspot identification is a significant issue in terms of crime mapping and spatial statistical analysis. Hotspot identification helps in the effective deployment of law enforcement personnel in such regions. A crime data warehouse is a spatial data warehouse that is updated infrequently and hence has specific requirements for processing. In several works spatial datasets have been processed with implementation of the indexing structure spatial join index that is represented by data structures such as page-pair graph, bi-partite graph and neighbourhood graph. The inherent complexity in spatial datasets necessitates the utilization of spatial join index, which accelerates the data process. This work aims to process spatial datasets with hypergraph as an indexing structure at a reduced user response time. A self-join index as a spatial join index has been effectively implemented in past works. In this paper, the self-join index that is implemented with hypergraph is derived from a Delaunay triangulation. Further, the efficiency of the identification of crime clusters or hotspots is improved with the utilization of a distance metric in processing the spatial datasets. Such measures aid in bringing down the user response time in a significant manner. A comparative analysis of the various implementations shows the effectiveness of this procedure for hotspot identification.

Abstract
Keywords
1. Introduction
2. Hotspot Identification with Neighbourhood Graph
3. Hotspot Identification with Hypergraph
4. Comparative Analysis
5. Conclusion
References
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