Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling
By Stefanos Georganos, Taïs Grippa, Assane Niang Gadiaga, Catherine Linard, Moritz Lennert, Sabine Vanhuysse, Nicholus Mboga, Eléonore Wolff, Stamatis Kalogirou. 2019
391 citations recorded by the source corpus.
Abstract Machine learning algorithms such as Random Forest (RF) are being increasingly applied on traditionally geographical topics such as population estimation. Even though RF is a well performing and generalizable algorithm, the vast majority of its implementations is still ‘aspatial’ and may not address spatial heterogenous processes. At the same time, remote sensing (RS) data which are commonly used to model population can be highly spatially heterogeneous. From this scope, we present a novel geographical implementation of RF, named Geographical Random Forest (GRF) as both a predictive and exploratory tool to model population as a function of RS covariates. GRF is a disaggregation of RF into geographical space in the form of local sub-models. From the first empirical results, we conclude that GRF can be more predictive when an appropriate spatial scale is selected to model the data, with reduced residual autocorrelation and lower Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values. Finally, and of equal importance, GRF can be used as an effective exploratory tool to visualize the relationship between dependent and independent variables, highlighting interesting local variations and allowing for a better understanding of the processes that may be causing the observed spatial heterogeneity.
Subfields: Economics and Econometrics, Global and Planetary Change
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Citations in the corpus
- Mathematical optimization in classification and regression trees (2021, background): “2020), spatial data (Georganos et al. 2019), functional data (Balakrishnan and Madigan 2006; Möller et al.”
- Random Forest Spatial Interpolation (2020, background): “[23] proposed Geographical Random Forest (GRF) as a function of RS covariates for modelling population density in Dakar, Senegal.”
- Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction (2019, neutral): “Spatial coordinates are used especially often (Li et al., 2011; Langella et al., 2010; Shi et al., 2015; Janatian et al., 2017; Walsh et al., 2017; Jing et al., 2016; Wang et al., 2017; Georganos et al., 2019).”