Causation: Causation refers to a cause-and-effect relationship between variables, where changes in one variable directly lead to changes in another variable. It goes beyond correlation by establishing a clear cause-effect link.
Regression Analysis: Regression analysis is a statistical technique used to model the relationship between two or more variables. It helps understand how changes in one variable are associated with changes in another while accounting for other factors.
Spatial Autocorrelation: Spatial autocorrelation measures the similarity or dissimilarity of values among neighboring locations. It helps identify whether patterns or correlations exist at different spatial scales within a dataset.