Eigenspace Method for Spatiotemporal Hotspot Detection
This addresses the need for more efficient and assumption-free hotspot detection in fields like disease surveillance, offering a novel alternative to the state-of-the-art STScan method.
The paper tackled the problem of detecting spatiotemporal hotspots, such as disease outbreaks, by proposing EigenSpot, a method that tracks changes in space-time correlation structure instead of exhaustive search, achieving significantly higher computational efficiency without assumptions on data distribution, hotspot shape, or data quality.
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.