Сравнение методов

	Географически взвешенный метод главных компонент (GWPCA)	Регрессия с географически взвешенными коэффициентами (GWR)
Область	Пространственный анализ	Пространственный анализ
Семейство	Machine learning	Regression model
Год появления	2011	2002
Автор метода	Paul Harris, Chris Brunsdon & Martin Charlton	Fotheringham, Brunsdon & Charlton
Тип	Local dimensionality reduction	Local spatial regression
Основополагающий источник	Harris, P., Brunsdon, C., & Charlton, M. (2011). Geographically weighted principal components analysis. International Journal of Geographical Information Science, 25(10), 1717–1736.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Другие названия	Local PCA, Spatially Adaptive PCA, Geographically Weighted Factor Analysis, Yerel Coğrafi Ağırlıklı PCA	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Связанные	2	5
Сводка	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 Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships.