Semivariogram Modeling
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Self Organizing Map Usage
10. Kangas, J., Kohonen, T.: Development and applications of selforganizing map and related algorithms. Mathematics and Computers in Simulations 41, 3–12 (1996) 11. Kohonen T: (2001) SOM-Tool Box Home. http://www.cis.hut.fi/ projects/somtoolbox/. cited 02.04.01 12. Panda, S.: Data Mining Application in Production Management of Crop. Dissertation, North Dakota State University (2003) 13. Honkela T: (1997) Self-organizing map in natural language processing. http://citeseer.ist.psu.edu/123560.html. Accessed 30 Dec 2006
Self Organizing Map Usage Self Organizing Map (SOM) Usage in LULC
Classification
Self-Referential Context Participatory Planning and GIS
Semantic Metadata and Interoperability, Geospatial
Semantic Discord Uncertainty, Semantic
Semantic Geospatial Web
Definition 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 locations is called semivariogram modeling. In addition to summarizing the variation in measurements with distance, semivariogram modeling is also used as a prediction tool to estimate the value of a measure at a new location. Semivariogram modeling is applied in fields related to spatial data, such as ecology (to study the vegetation cover), meteorology (to study the variation of climatic effects such as rainfall), and geology (to study the distribution of minerals such as iron ore and predict the iron ore content at a known location), etc.
Geospatial Semantic Web, Interoperability
Semantic Information Integration Ontology-Based Geospatial Data Integration
Semantic Web Geospatial Semantic Integration Geospatial Semantic Web
Semivariogram Modeling V IJAY G ANDHI Department of Computer Science, University of Minnesota, Minneapolis, MN, USA Synonyms Variogram modeling
Semivariogram Modeling, Figure 1
An example of a semivariogram
Semivariogram Modeling
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Semivariogram modeling is also referred to as variogram modeling. Historical Background The term “semivariogram” became popular after its first use by G. Matheron in 1963. Matheron used semivariogram modeling for the prediction of mining sites in South Africa with optimal ore grades. Such a prediction
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