方法对比

	局部空间回归	局部空间滞后模型
领域	空间分析	空间分析
方法族	Regression model	Regression model
起源年份	1996	1988 (global); 2000s (local extensions)
提出者	Brunsdon, Fotheringham & Charlton	Anselin (global SLM, 1988); local extension via Fotheringham, Brunsdon & Charlton (GWR framework, 2002)
类型	Spatially varying coefficient regression	Spatially varying regression model
开创性文献	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737215
别名	locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression	local SLM, geographically weighted spatial lag model, GW-SLM, spatially varying lag model
相关	6	5
摘要	Local Spatial Regression fits a separate regression model at each location in a study area, allowing regression coefficients to vary continuously across space. Rather than forcing one global slope on all observations, it reveals where and how the relationship between predictors and an outcome changes geographically — producing a map of coefficients rather than a single number.	The Local Spatial Lag Model extends the classical spatial lag model by allowing both the spatial autocorrelation parameter and the regression coefficients to vary across geographic locations. Instead of one global estimate of how neighboring outcomes influence each observation, the model fits location-specific parameters using kernel-weighted local estimation, revealing spatial heterogeneity in spatial dependence.