Distinguishing the analysis of spatial data from classical analysis is only meaningful if the spatial components bring information. Therefore, testing if the spatial autocorrelation is significant may confirm or deny the need to consider spatial analysis over the classical one. Spatial autocorrelation expresses the dependence between values at neighbouring locations. Several measures of spatial autocorrelation are defined in the literature. Moran’s index, Geary’s ratio and Getis-Ord statistic are the most used statistics. Tests based on these measures have been developed in the literature using asymptotic and permutation results. They are used in practice in many fields, for instance in geography, economics, biogeosciences, medicine, ... However, these tests should be cautiously applied because they are not robust. A single contaminated observation can significantly modify their results. The talk has two main objectives. Firstly, the already available tools for measuring spatial autocorrelation will be reviewed with an emphasis on the study and comparison of their robustness. Secondly, alternative methods will be proposed to robustly estimate the spatial autocorrelation
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