Determining infrastructural potential of a municipality is possible by measuring a number of diagnostic features describing this potential in the municipality – the number of these variables depends mainly on the purpose of the analysis. 13 variables considered as diagnostic were used in this paper to illustrate diversification of agricultural infrastructure of farms on the municipality level. The set of these variables marks a point in the multidimensional space, which is characteristic for each investigated object but differentiating objects among themselves. The potential was estimated on the basis of synthetic measure of development which is an aggregate of diagnostic features. The computed synthetic index reduces multidimensional data to a single number and replaces the point in the multidirectional space by a point on an axis in one-dimensional space. The goal of the article is to show if the picture of a population of municipalities in their many dimensions and original shape seen through synthetic coefficient is similar to the original and whether the obtained one-dimensional distance properly reflects distances in the initial space of the studied infrastructure potential. In the presented work the information quality provided by the synthetic coefficient computed for the studied districts has been analysed using multidimensional ...
Apparent diversification of the malopolskie province area in respect of its topography and unequal financial resources at the disposal of individual communes and districts may determine the level of their infrastructure development. At present, when Poland became the European Union member state and entered its financing structures, spatial development of the infrastructural potential is affected by the activities of local self governments. Intensive development and modernization of infrastructure favour local concentration of communes with high values of the indicator. Technical infrastructure is an element strictly connected with space and its level is affected by social, financial and human factors, which provides a basis for an analysis of the influence of local spaces on its development or lack of it. In the article local diversification of communes was made using Local Moran’s Ii statistics. Local statistics my verify whether a commune is surrounded by neighbouring objects (communes) with similar or various values of analyzed variable in relation to random distribution of these values in space. It allows for identification of spatial effects of an agglomeration. Such analysis of local indicators of spatial association LISA was suggested by Anselin [Anselin 1995]. The article aims at presentation of local indicators of spatial dependencies ...
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 ...
