Spatial econometric methods help analyze data by considering spatial patterns and differences. They focus on spatial autocorrelation, where nearby observations are related, and spatial heterogeneity, where variables vary across space. These techniques have become more popular in recent years for studying geographical data. This article explains how to include spatial autocorrelation and spatial heterogeneity in regression models and how to estimate and interpret these effects. The first part of the article discusses spatial autocorrelation, while this second part focuses on spatial heterogeneity.