Summary:

Autocorrelation is a branch of statistics dealing with an analysis of spatial data and with further description and investigation of spatial phenomena. Methods of spatial statistics are also called explorative spatial data analysis – ESDA. Spatial statistics are an efficient method to identify the dependence of individual phenomenon occurrence on geographical space. Measures of spatial autocorrelations show the dependence of variables in respect of spatial localization. Spatial correlation (positive autocorrelation) allows to determine that intensification of a given phenomenon is more perceivable in the adjoining objects than in located far away from one another. Two types of measures are used by spatial statistics: global and local measures. The Authors used a global measure to illustrate the spatial dependence of water supply and sewage disposal infrastructure occurrence. The global measure was computed using R CRAN program. The global measure of Moran’s I statistics was computed for various spatial weight matrices. The data for analysis, evidencing the state of water supply and sewage disposal infrastructure in 2005, were obtained from the Main Statistical Office in Krakow. Moran’s I statistics allows to identify global autocorrelation measures in spatial objects with reference to the assumed weight matrix. The global measure is a one number indicator of spatial dependence or general similarity of regions. The advantage of global measure is its syntheticity, while its disadvantage is its averaging. The article presents possibilities of application of spatial statistics for an analysis of water supply and sewage disposal phenomena. For this reason a diversification of communes in the malopolskie province was investigated in respect of selected elements of technical infrastructure in the water supply and sewage disposal group. The analysis was conducted on the basis of indicators of object area saturation with water main and sewer systems.