The basic concept of geostatistics is that of scales of spatial variation. Spatially independent data show the same variability regardless of the location of data points. However, spatial data in most cases are not spatially independent. Data values which are close spatially show less variability than data values which are farther away from each other. The exact nature of this pattern varies from data set to data set; each set of data has its own unique function of variability and distance between data points. This variability is generally computed as a function called semivariance.

Spatial autocorrelation can be analyzed using correlograms, covariance functions and variograms (=semivariograms).

Relevant topics: statistics, geology, GIS, remote sensing, kriging

References:

- Galli, A., Wackernagel, H.: Multivariate geostatistical methods for spatial data analysis. 1987
- Sharov, A: Quantitative Population Ecology, 1996, http://www.ento.vt.edu/~sharov/PopEcol/popecol.html
- Shine, J.A., Wakefield, G.I.: A comparison of supervised imagery classification using analyst-chosen and geostatistically-chosen training sets, 1999, http://www.geovista.psu.edu/sites/geocomp99/Gc99/044/gc_044.htm