If spatial autocorrelation is present it will violate the assumption about the independence of residuals and call into question the validity of hypothesis testing. When the residuals were examined they were obviously non-random, and hence must be spatially autocorrelated. They suggest that this should not be viewed as a wholly negative conclusion. Indeed the inevitable regional groupings which are seen in the residuals can suggest ways in which the model may be improved, in their case by a consideration of the influence of the moist Caribbean air streams. While it is true that spatial autocorrelation makes standard significance tests unreliable researchers are often more interested in the spatial autocorrelation shown by the residuals because these indicate deviations from a trend which presumably identifies discontinuities.
Techniques are available that can 'partial out' the effects of space.
Spatial autocorrelation analysis tests whether the observed value of a variable at one locality is independent of the values of the variable at neighbouring localities. If a dependence exists, the variable is said to exhibit spatial autocorrelation. Spatial autocorrelation measures the level of interdependence between the variables, and the nature and strength of that interdependence. It may be classified as either positive or negative. In a positive case all similar values appear together, while a negative spatial autocorrelation has dissimilar values appearing in close association.