A Forest of Forests
A Spatially Weighted and Computationally Efficient Formulation of Geographical Random Forests
Bibliographic Data
| ID | 22033369 |
|---|---|
| Authors | Stefanos Georganos (0000-0002-0001-2058, KTH Royal Institute of Technology, corresponding author), Stamatis Kalogirou (0000-0003-1949-6248, Data and Technology for Audit (DATA), European Court of Auditors, 1615 Luxembourg, Luxembourg) |
| Year | 2022 |
| Volume | 11 |
| Issue | 9 |
| Pages | 471 |
| Publication date | 2022-08-31 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi11090471 |
| OpenAlex | W4294325987 |
| Language | EN |
| Citations received | 14 |
| References cited | 22 |
The aim of this paper is to present developments of an advanced geospatial analytics algorithm that improves the prediction power of a random forest regression model while addressing the issue of spatial dependence commonly found in geographical data. We applied the methodology to a simple model of mean household income in the European Union regions to allow easy understanding and reproducibility of the analysis. The results are encouraging and suggest an improvement in the prediction power compared to previous techniques. The algorithm has been implemented in R and is available in the updated version of the SpatialML package in the CRAN repository
Analytics · Business · Cartography · Data mining · Data science · Econometrics · European union · Geographically Weighted Regression · Geography · Geospatial analysis · Machine learning · Random forest · Remote sensing · Spatial analysis · Statistics · Computer Science · Economic and Environmental Valuation · Land Use and Ecosystem Services · Mathematics · Spatial and Panel Data Analysis
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| Citations per year | 3.5 |
|---|---|
| Citation span | 2022 - 2026 (5) |
| Highly cited | No |