قارن الطرق

	الانحدار الجغرافي الموزون محليًا (GWR)	الارتباط الذاتي المكاني المحلي
المجال	التحليل المكاني	التحليل المكاني
العائلة	Regression model	Regression model
سنة النشأة	1996	1995
صاحب الطريقة	Brunsdon, Fotheringham & Charlton	Luc Anselin
النوع	Spatially varying coefficient regression	Spatial association analysis
المصدر التأسيسي	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168	Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2), 93–115.
الأسماء البديلة	GWR, geographically weighted regression, local spatial regression, spatially varying coefficient model	local spatial association, local SA, LISA methods, local spatial clustering
ذات صلة	5	6
الملخص	Local Geographically Weighted Regression (GWR) estimates a separate regression model at each location in the study area, allowing every coefficient to vary spatially. By weighting nearby observations more heavily than distant ones, GWR reveals how predictor-outcome relationships shift across geographic space rather than forcing a single global estimate on heterogeneous data.	Local Spatial Autocorrelation methods decompose global spatial clustering into location-specific statistics, revealing where in a study area significant clustering or dispersion occurs. Each observation receives its own association score and significance value, enabling the detection of spatial hot spots, cold spots, and spatial outliers rather than reporting a single summary statistic.