Linganisha mbinu

	Modeli wa Lag wa Kinaanga cha Wakati na Nafasi	Usuli wa Kawaida wa Kijiografia (GWR)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	2003-2008	2002
Mwanzilishi	Anselin, Le Gallo & Jayet; Elhorst	Fotheringham, Brunsdon & Charlton
Aina	Spatial panel regression	Local spatial regression
Chanzo asilia	Anselin, L., Le Gallo, J., & Jayet, H. (2008). Spatial Panel Econometrics. In L. Matyas & P. Sevestre (Eds.), The Econometrics of Panel Data (pp. 625-660). Springer.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Majina mbadala	ST-SAR, spatial-temporal lag model, spatiotemporal autoregressive model, space-time SAR model	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Zinazohusiana	5	5
Muhtasari	The Space-Time Spatial Lag Model extends the classic spatial autoregressive (SAR) lag model to panel data, capturing how the outcome in each location at each time point is influenced by the contemporaneous outcomes of neighboring locations, while also controlling for unit-specific and time-specific fixed effects.	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.