  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.
