विधियों की तुलना करें

	भौगोलिक भारित प्रमुख घटक विश्लेषण (GWPCA)	जियोग्राफिकली वेटेड रैंडम फ़ॉरेस्ट
क्षेत्र	स्थानिक विश्लेषण	स्थानिक विश्लेषण
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2011	2021
प्रवर्तक	Paul Harris, Chris Brunsdon & Martin Charlton	Stefanos Georganos et al.
प्रकार	Local dimensionality reduction	Spatially local ensemble learning method
मौलिक स्रोत	Harris, P., Brunsdon, C., & Charlton, M. (2011). Geographically weighted principal components analysis. International Journal of Geographical Information Science, 25(10), 1717–1736.	Georganos, S., et al. (2021). Geographical random forests: a spatial extension of the random forest algorithm. Geocarto International, 36(2), 121–136.
उपनाम	Local PCA, Spatially Adaptive PCA, Geographically Weighted Factor Analysis, Yerel Coğrafi Ağırlıklı PCA	Geographical Random Forest, GRF, Spatial Random Forest, Cografi Agirlikli Rastgele Orman
संबंधित	2	3
सारांश	Geographically Weighted Principal Component Analysis (GWPCA) is a local dimensionality-reduction method introduced by Harris, Brunsdon, and Charlton in 2011. It extends classical PCA by fitting a separate weighted PCA at every location in a dataset, allowing eigenstructures — the principal components and their loadings — to vary continuously across geographic space rather than being constrained to a single global solution. GWPCA is suited to researchers in environmental science, public health, and regional economics who suspect that multivariate relationships among variables differ by location.	Geographically Weighted Random Forest (GWRF) is a spatially local ensemble learning method that fits an independent Random Forest model at each observation location, weighting nearby training samples more heavily than distant ones through a spatial kernel function. It was introduced by Stefanos Georganos and colleagues in 2019 (published 2021) as an extension of Breiman's Random Forest to handle spatial non-stationarity — the phenomenon where predictor–response relationships vary across geographic space.