The article deals with one of the big analytical issues in spatial data analysis: spatial autocorrelation. This phenomenon is described as a key concept and potential problem of spatial analysis, but especially as a method of spatial analysis. The method is introduced first by reviewing the basic methodological framework including some related issues (the choice of spatial weighting system etc.) and second by presenting empirical examples of its application. Spatial autocorrelation statistics detect the presence of interdependence between the values of data at neighbouring locations. In addition to global spatial autocorrelation, measuring the overall degree of clustering, and calculated, for example, by Moran's I, emphasis is placed on the local analysis of spatial autocorrelation. This local form of spatial autocorrelation is based on the premise that the presence of spatial autocorrelation can vary across the study area, and it is fully in line with contemporary developments in spatial analysis. The results of LISA (local indicators of spatial association, local Moran) can be mapped for the purpose of identifying clusters. The empirical examples based on aggregate statistical data at the municipal level highlight the relevance and usefulness of analysis of spatial autocorrelation and show how these analyses can be used in social research and can improve our understanding of spatial processes.