Spatial Component Models with Artificial Neural Networks for Spatially Constrained Regionalization

Govorov, Michael; Beconytė, Giedrė; Gienko, Gennady

The authors have investigated into different geostatistical point data modeling approaches for regionalization purposes that employ the Artificial Neural Network (ANN) techniques. Regionalization is a spatially constrained adjacency classification problem. In this study, regionalization is viewed as classification of spatial objects (non-uniformly distributed points) into a smaller number of geographic regions defined by their spatial and attributive characteristics or regionalized variables. For regionalization, we take into consideration the non-stationarity and autocorrelation properties of the spatial data.

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Govorov, Michael / Beconytė, Giedrė / Gienko, Gennady: Spatial Component Models with Artificial Neural Networks for Spatially Constrained Regionalization. 2019. Copernicus Publications.

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Rechteinhaber: Michael Govorov et al.

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