Elliptical hotspot detection: A summary of results

Xun Tang, Emre Eftelioglu, Shashi Shekhar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Given a set of points in Euclidean space, a minimum log likelihood ratio threshold, and a statistical significance threshold, Elliptical Hotspot Detection (EHD) finds elliptical hotspot areas where the concentration of activities inside is significantly higher than outside. The EHD problem is important to many fields, such as criminology, transportation engineering, and epidemiology. Related work (e.g., SatScan) enumerates only circular candidates using activities as centers, and may miss many significant ellipses. EHD problem is challenging for two reasons, namely the large enumeration space and the lack of monotonicity of the log likelihood ratio. To overcome the challenges and limitations of the related work, this paper proposes a novel algorithm for EHD. A case study on real crime data shows that our algorithm is able to find hotspots that cannot be detected by the related work. Experimental evaluation shows that the proposed algorithm saves substantial amount of computation compared to the naïve approach.

Original languageEnglish (US)
Title of host publicationProceedings of the 4th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2015
EditorsVarun Chandola, Ranga Raju Vatsavai
PublisherAssociation for Computing Machinery, Inc
Pages15-24
Number of pages10
ISBN (Electronic)9781450339742
DOIs
StatePublished - Nov 3 2015
Event4th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2015 - Seattle, United States
Duration: Nov 3 2015 → …

Publication series

NameProceedings of the 4th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2015

Other

Other4th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2015
Country/TerritoryUnited States
CitySeattle
Period11/3/15 → …

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. 1029711, IIS-1320580, 0940818 and IIS-1218168, USDOD under Grant No. HM1582-08-1-0017, HM0210-13-1-0005, and University of Minnesota via U-Spatial. We would like to thank Kim Koffolt and the members of the University of Minnesota Spatial Computing Research Group for their comments.

Keywords

  • Hotspot detection
  • Spatial data mining
  • Statistical significance

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