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 language||English (US)|
|Title of host publication||SIAM International Conference on Data Mining, SDM 2019|
|Publisher||Society for Industrial and Applied Mathematics Publications|
|Number of pages||9|
|State||Published - 2019|
|Event||19th SIAM International Conference on Data Mining, SDM 2019 - Calgary, Canada|
Duration: May 2 2019 → May 4 2019
|Name||SIAM International Conference on Data Mining, SDM 2019|
|Conference||19th SIAM International Conference on Data Mining, SDM 2019|
|Period||5/2/19 → 5/4/19|
Bibliographical noteFunding 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.
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