  Mapping of spatial hotspots, i.e., regions with significantly higher rates of
generating cases of certain events (e.g., disease or crime cases), is an
important task in diverse societal domains, including public health, public
safety, transportation, agriculture, environmental science, etc. Clustering
techniques required by these domains differ from traditional clustering methods
due to the high economic and social costs of spurious results (e.g., false
alarms of crime clusters). As a result, statistical rigor is needed explicitly
to control the rate of spurious detections. To address this challenge,
techniques for statistically-robust clustering (e.g., scan statistics) have
been extensively studied by the data mining and statistics communities. In this
survey we present an up-to-date and detailed review of the models and
algorithms developed by this field. We first present a general taxonomy for
statistically-robust clustering, covering key steps of data and statistical
modeling, region enumeration and maximization, and significance testing. We
further discuss different paradigms and methods within each of the key steps.
Finally, we highlight research gaps and potential future directions, which may
serve as a stepping stone in generating new ideas and thoughts in this growing
field and beyond.
