Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling-Reference-Cited by-同舟云学术

Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling

Published:2019-06-10 Issue:2 Volume:36 Page:121-136
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Georganos Stefanos¹^ORCID,Grippa Tais¹^ORCID,Niang Gadiaga Assane²,Linard Catherine²,Lennert Moritz¹^ORCID,Vanhuysse Sabine¹,Mboga Nicholus¹,Wolff Eléonore¹,Kalogirou Stamatis³

Affiliation:

1. Department of Geosciences, Environment & Society, Université Libre De Bruxelles (ULB), Bruxelles, Belgium

2. Institute of Life, Earth and Environment, University of Namur, Namur, Belgium

3. Department of Geography, Harokopio University, Kallithea, Greece

Funder

MAUPP

REACT

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://tandfonline.com/doi/pdf/10.1080/10106049.2019.1595177

Reference37 articles.

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3. Lagrange Multiplier Test Diagnostics for Spatial Dependence and Spatial Heterogeneity

4. Borderon M. 2013. Why here and not there? Developing a spatial risk model for malaria in Dakar, Senegal. From social vulnerability to resilience: measuring progress toward disaster risk reduction; p. 108–120.

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