  Space and time are two critical components of many real world systems. For
this reason, analysis of anomalies in spatiotemporal data has been a great of
interest. In this work, application of tensor decomposition and eigenspace
techniques on spatiotemporal hotspot detection is investigated. An algorithm
called SST-Hotspot is proposed which accounts for spatiotemporal variations in
data and detect hotspots using matching of eigenvector elements of two cases
and population tensors. The experimental results reveal the interesting
application of tensor decomposition and eigenvector-based techniques in hotspot
analysis.
