An area of connected high concentration indicated on map of interpolated data may indicate the presence of a hot spot and inform or optimize future sampling efforts in this area.  The concentration level which is used to identify a hot-spot should consider regulatory levels, for example a grouping of concentrations that are above a regulatory level may be considered a hot spot, though not detectable as such through the use of other criteria. Some software programs use parametric statistics such as z-score to identify those areas which have values greater than expected (see ArcGIS and VSP).

Advanced methods such as kriging involve fitting a model of spatial correlation to the data. By examining the data on different scales short-range variability may be distinguished from long-range variability and potentially a hot spot may be detected. The short-range variability may be contributed to by contamination. Principal component analysis can help to identify a single contaminant from the larger data set (Goovaerts 1997).