Fulfilment of the increasing quality requirements of sugar beet production can be analysed with sampling of plants and soil at the cultivated area. Analyses of the spatial characteristics of samples require exact geodetic positioning. This is applied in practice using GPS in precision agriculture. The examinations were made in a sample area located in north-western Hungary with sugar beet test plant. According to the traditional sample taking procedure N=60 samples were taken in regular 20 x 20 m grid, where besides the plant micro and macro elements, the sugar industrial quality parameters (Equations 1-2) and the agro-chemical parameters of soils were analysed. Till now, to gain values of mean, weighted mean and standard variance values, geometric analogues used in geography were adapted, which correspond to the mean centre (Equation 3), the spatially weighted mean centre (Equation 4), the standard distance (Equation 5), and the standard distance circle values. Robust spatial statistical values provide abstractions, which can be visually estimated immediately, and applied to analyse several parameters in parallel or in time series (Figure 1). This interpretation technique considers the spatial position of each point to another individually (distance and direction), and the value of the plant and soil parameters. Mapping the sample area in GIS environment, the coordinates of the spatially weighted mean centre values of the measured plant and soil parameters correlated to the mean centre values showed a northwest direction. Exceptions were the total salt and calcium-carbonate contents, and the molybdenum concentration of the soil samples (Table 1). As a new visual analysis, the spatially weighted mean centre values of the parameters as eigenvectors were projected to the mean centre values as origin. To characterize the production yield, the raw and digested sugar contents of the sample area, the absolute rotation angles of the generated vectors were determined, which indicate numerically the inhomogenity of the area (Figure 2). The generated spatial analogues are applicable to characterise visually and quantitatively the spatial positions of sampling points and the measured parameters in a quick way. However, their disadvantage is that they do not provide information on the tightness and direction of the spatial correlation similarly to the original statistical parameters.