  Outlier detection is a significant area in data mining. It can be either used
to pre-process the data prior to an analysis or post the processing phase
(before visualization) depending on the effectiveness of the outlier and its
importance. Outlier detection extends to several fields such as detection of
credit card fraud, network intrusions, machine failure prediction, potential
terrorist attacks, and so on. Outliers are those data points with
characteristics considerably different. They deviate from the data set causing
inconsistencies, noise and anomalies during analysis and result in modification
of the original points However, a common misconception is that outliers have to
be immediately eliminated or replaced from the data set. Such points could be
considered useful if analyzed separately as they could be obtained from a
separate mechanism entirely making it important to the research question. This
study surveys the different methods of outlier detection for spatial analysis.
Spatial data or geospatial data are those that exhibit geographic properties or
attributes such as position or areas. An example would be weather data such as
precipitation, temperature, wind velocity, and so on collected for a defined
region.
