The article discusses non-standard spatial statistics and spatial econometrics. It explores different statistical models for spatial data and compares various specifications for spatial random effects. The researchers also analyze the role of spatial autocorrelation in prioritizing sites within a geographic landscape. In the second part, they delve into spatial econometrics, including selecting spatial regimes and using qualitative regression for spatial data. Overall, the study provides insights into understanding correlations among spatial random variables and filtering complexity for observational errors and spatial bias.