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The first law of geography states that “Everything is related to everything else, but near things are more related than distant things”. For example, it is natural that nearby places have more similar climates than do places which are far apart. Amounts of rainfall and iron ore deposits vary gradually over space. Such natural processes that vary gradually with respect to distance are said to be spatially correlated.
A semivariogram is one of the significant functions to indicate spatial correlation in observations measured at sample locations (Fig. 1). It is commonly represented as a graph that shows the difference in measure and the distance between all pairs of sampled locations. Such a graph is helpful in building a mathematical model that describes the variability of the measure with location. Modeling of the relationship among sample locations to indicate the variability of the measure with a distance of separation between all sampled...
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References
Banerjee S, Carlin BP, Gelfand AE (2004) Hierarchical modeling and analysis for spatial data. Chapmann & Hall/CRC, Boca Raton
Clark I (1979) Practical geostatistics. Applied Science, London
Cressie N (1993) Statistics for spatial data. Wiley-Interscience, New York
de Jong SM, van der Meer FD (2006) Remote sensing image analysis including the spatial domain (remote sensing and digital image processing). Springer, Dordrecht
Recommended Reading
Chica-Olmo M, Abarca-Hernandez F (1999) Computing geostatistical image texture for remotely sensed data classification. Comput Geosci
Ma C (2004) The use of the variogram in construction of stationary time series models. J Appl Probab 41:1093–1103
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Gandhi, V. (2017). Semivariogram Modeling. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_1189
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DOI: https://doi.org/10.1007/978-3-319-17885-1_1189
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