The spatial statistical analysis history is fraught with spatial autocorrelation (SA) ponderings, mostly questioning the nature and degree of this observational correlation type’s impacts. Tacit awareness of its impending complications emerged in the early 1900s, with time series methodology spuriously guiding thought and practice. Initial debates cast SA as a nuisance, encouraging its expulsion from geospatial data. The first spatial filtering models strove to do this. However, value ultimately bestowed upon SA by such procedures as spatial interpolation (e.g., geostatistical kriging) moderated this excessive action, with a suite of spatial autoregressive models emerging that fostered spatial filtering engendering isolation-but-retention of global SA in data analyses. Next, Getis refocused this effort on local SA statistics to devise an alternative spatial filtering model whose capabilities include response variable and covariate decompositions into disjunct spatial and aspatial components, suggestive of the spatial Durbin specification. One weakness of these models is their strict normal curve theory reliance. More recent MESF model formulation and articulation transcends this drawback. Consequently, today, spatial analysts can tap a wide variety of spatial filtering conceptualizations, the subject matter this article reviews. In doing so, it presents an original spatially autocorrelated gamma variate empirical example, itself a novel literature contribution.