Incorporating Spatial Autocorrelation in Machine Learning Models Using Spatial Lag and Eigenvector Spatial Filtering Features

Incorporating Spatial Autocorrelation in Machine Learning Models Using Spatial Lag and Eigenvector Spatial Filtering Features

Applications of machine-learning-based approaches in the geosciences have witnessed a substantial increase over the past few years. Here we present an approach that accounts for spatial autocorrelation by introducing spatial features to the models. In particular, we explore two types of spatial feat...

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Hlavní autoři: Xiaojian Liu, Ourania Kounadi, Raul Zurita-Milla
Médium: Článek
Jazyk:English
Vydáno: MDPI AG 2022-04-01
Edice:ISPRS International Journal of Geo-Information
Témata:
spatial autocorrelation
spatial lag
eigenvector filtering
machine learning
nested cross-validation
geographical prediction
On-line přístup:https://www.mdpi.com/2220-9964/11/4/242

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https://www.mdpi.com/2220-9964/11/4/242

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