Premium
Spatially constrained regionalization with multilayer perceptron
Author(s) -
Govorov Michael,
Beconytė Giedrė,
Gienko Gennady,
Putrenko Viktor
Publication year - 2019
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12557
Subject(s) - contiguity , spatial analysis , homogeneity (statistics) , computer science , pattern recognition (psychology) , artificial intelligence , multilayer perceptron , data mining , point (geometry) , sorting , multivariate statistics , machine learning , artificial neural network , algorithm , mathematics , statistics , geometry , operating system
In this article, multilayer perceptron (MLP) network models with spatial constraints are proposed for regionalization of geostatistical point data based on multivariate homogeneity measures. The study focuses on non‐stationarity and autocorrelation in spatial data. Supervised MLP machine learning algorithms with spatial constraints have been implemented and tested on a point dataset. MLP spatially weighted classification models and an MLP contiguity‐constrained classification model are developed to conduct spatially constrained regionalization. The proposed methods have been tested with an attribute‐rich point dataset of geological surveys in Ukraine. The experiments show that consideration of the spatial effects, such as the use of spatial attributes and their respective whitening, improve the output of regionalization. It is also shown that spatial sorting used to preserve spatial contiguity leads to improved regionalization performance.