Spatial regression or spatial lag model

This lecture discusses spatial regression models, focusing on spatial autocorrelation and the challenges it poses in traditional linear regression. It explores the concept of spatial lag models, which account for the systematic relationship between values in adjacent areas, leading to an overestimation of the relationship between variables. The instructor presents methods like Geographically Weighted Regression (GWR) and spatially weighted linear regression to address these issues, emphasizing the importance of considering local variations in attributes. The lecture also covers the calculation of spatial lag terms, the impact of autoregressive coefficients, and the correction of biases induced by spatial autocorrelation. Through examples and theoretical explanations, students learn how to apply these models to make more accurate inferences in spatial data analysis.