Getis’s spatial filtering legacy: spatial autocorrelation mixtures in geospatial agricultural datasets

The dual achievements of this paper are: establishing that the Getis spatial filtering technique can uncover latent PSA–NSA mixtures, and uncovering this very mixture property in geospatial agricultural datasets, acknowledging omitted variable complications attributable to its presence. This methodological extension derives from published comments by Getis himself, whereas this agricultural data category augments the existing set comprising georeferenced socio-economic/demographic and disease data. Puerto Rico space–time datasets—for milk, plantain, and sugarcane production—constitute the analyzed empirical specimens, adding consistency across sequential periods in time to the current repertoire of already recognized focal data features that include geographic resolution and scale as well as geographic landscape diversity. This paper also presents comparisons between the proposed novel Getis spatial filtering decomposition with both spatial autoregressive and Moran eigenvector spatial filtering specifications, credibly concluding that, to some degree, all are capable of identifying PSA–NSA mixtures in geotagged data. Its other prominent general conclusion is that PSA–NSA mixtures tend to be latent in geospatial agricultural datasets.