Comparison of local outliers detection techniques in spatial multivariate data

Outlier detection techniques in spatial data should allow to identify two types of outliers:
global and local ones. Local outliers typically have non-spatial attributes that strongly
differ from those observed on their neighbors. Detecting local outliers requires to be
able to work locally, on neighborhoods, in order to take into account the spatial
dependence between the statistical units under consideration, even though the
outlyingness is usually measured on the non-spatial variables. Many procedures
have been outlined in the literature, but their number reduces when one wants to deal
with multivariate non-spatial attributes. In this paper, focus is on the multivariate
context. A review of existing procedures is done. A new approach, based on a two-step
improvement of an existing one, is also designed and compared with the benchmarked
methods by means of examples and simulations.