A nondeterministic normalization based scan statistic (NN-scan) towards robust hotspot detection: A summary of results

Yiqun Xie, Shashi Shekhar

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

5 Scopus citations

Abstract

Hotspot detection aims to find sub-regions of a space that have higher probability density of generating certain events (e.g., disease, crimes) than the other regions. Finding hotspots has important applications in many domains including public health, crime analysis, transportation, etc. Existing methods of hotspot detection rely on test statistics (e.g., likelihood ratio, density) that do not consider spatial nondeterminism, leading to false and missing detections. We provide theoretical insights into the limitations of related work, and propose a new framework, namely, Nondeterministic Normalization based scan statistic (NN-scan), to address the issues. We also propose a DynamIc Linear Approximation (DILA) algorithm to improve NN-scan’s efficiency. In experiments, we show that NN-scan can significantly improve the precision and recall of hotspot detection and DILA can greatly reduce the computational cost.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining, SDM 2019
PublisherSociety for Industrial and Applied Mathematics Publications
Pages82-90
Number of pages9
ISBN (Electronic)9781611975673
DOIs
StatePublished - 2019
Event19th SIAM International Conference on Data Mining, SDM 2019 - Calgary, Canada
Duration: May 2 2019May 4 2019

Publication series

NameSIAM International Conference on Data Mining, SDM 2019

Conference

Conference19th SIAM International Conference on Data Mining, SDM 2019
CountryCanada
CityCalgary
Period5/2/195/4/19

Bibliographical note

Funding Information:
This work is supported by the US NSF under Grants No. 1737633, 1541876, 1029711, IIS-1320580, 0940818 and IIS-1218168, the USDOD under Grants HM0210-13-1-0005, ARPA-E under Grant No. DE-AR0000795, USDA under Grant No. 2017-51181-27222, NIH under Grant No. UL1 TR002494, KL2 TR002492 and TL1 TR002493 and the OVPR U-Spatial and Minnesota Supercomputing Institute at the University of Minnesota.

Publisher Copyright:
Copyright © 2019 by SIAM.

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