The chapter introduces the concept of spatial econometrics, which is concerned with accounting for spatial dependence in observations collected from points or regions in space. Spatial regression methods are designed to incorporate this dependence, which often arises when observations are not independent but exhibit similar values from nearby locations. The chapter discusses the theoretical motivations for spatial dependence, including physical and human capital externalities, technological interdependence, and unobserved influences. It highlights the importance of relaxing the conventional assumption of independent observations in cross-sectional data and introduces spatial autoregressive processes as a key component of spatial regression models. The chapter also covers estimation methods for these models, model comparison techniques, and the interpretation of parameter estimates. Finally, it presents an applied illustration using commuting time data from US counties to demonstrate the practical application of spatial regression models.The chapter introduces the concept of spatial econometrics, which is concerned with accounting for spatial dependence in observations collected from points or regions in space. Spatial regression methods are designed to incorporate this dependence, which often arises when observations are not independent but exhibit similar values from nearby locations. The chapter discusses the theoretical motivations for spatial dependence, including physical and human capital externalities, technological interdependence, and unobserved influences. It highlights the importance of relaxing the conventional assumption of independent observations in cross-sectional data and introduces spatial autoregressive processes as a key component of spatial regression models. The chapter also covers estimation methods for these models, model comparison techniques, and the interpretation of parameter estimates. Finally, it presents an applied illustration using commuting time data from US counties to demonstrate the practical application of spatial regression models.