One of the areas ESDA tools focus on is outlier detection, as there are many instances in which so-called outliers are of great interest. In the present context these are spatial objects whose value on one or more attributes is markedly different from others in the set under consideration. The data in question may be correct or may be the result of some form of error (measurement, coding, representation etc.). Such data are of interest since they may represent the most important items in an investigation (e.g. mineral concentrations, a pollution source, an unexpectedly high incidence of a particular disease). Or they might represent data that need to be removed or adjusted (e.g. smoothed) if either the information is known or suspected to be incorrect, or if its retention will adversely affect the results obtained from the application of a particular analytical technique.