Eigenspace Method for Spatiotemporal Hotspot Detection

TL;DR

This paper introduces EigenSpot, a novel eigenspace-based method for detecting spatiotemporal hotspots that is more efficient and flexible than traditional methods like STScan, as it does not rely on restrictive assumptions.

Contribution

EigenSpot offers a new eigenspace approach for hotspot detection that improves computational efficiency and flexibility over existing methods like STScan.

Findings

EigenSpot is significantly faster than STScan.

EigenSpot does not assume specific data distributions or hotspot shapes.

Experimental results show EigenSpot effectively detects hotspots in real and simulated data.

Abstract

Hotspot detection aims at identifying subgroups in the observations that are unexpected, with respect to the some baseline information. For instance, in disease surveillance, the purpose is to detect sub-regions in spatiotemporal space, where the count of reported diseases (e.g. Cancer) is higher than expected, with respect to the population. The state-of-the-art method for this kind of problem is the Space-Time Scan Statistics (STScan), which exhaustively search the whole space through a sliding window looking for significant spatiotemporal clusters. STScan makes some restrictive assumptions about the distribution of data, the shape of the hotspots and the quality of data, which can be unrealistic for some nontraditional data sources. A novel methodology called EigenSpot is proposed where instead of an exhaustive search over the space, tracks the changes in a space-time correlation structure. Not only does the new approach presents much more computational efficiency, but also makes no assumption about the data distribution, hotspot shape or the data quality. The principal idea is that with the joint combination of abnormal elements in the principal spatial and the temporal singular vectors, the location of hotspots in the spatiotemporal space can be approximated. A comprehensive experimental evaluation, both on simulated and real data sets reveals the effectiveness of the proposed method.