The objective is to combine insights from two research traditions, spatial interaction modelling and spatial autocorrelation modelling, to deal with the issue of spatial autocorrelation in spatial interaction data analysis. *First*, the problem is addressed from an exploratory perspective for which a generalisation of the Getis–Ord *G* statistic is presented. This statistic may yield interesting insights into the processes that give rise to spatial association between residual flows. *Second*, the log-additive spatial interaction model is extended to spatial econometric origin-destination flow models consistent with an error structure that reflects origin, destination or origin-destination autoregressive spatial dependence. The models are formally equivalent to conventional spatial regression models. But they differ in terms of the data analysed and the way in which the spatial weights matrix is defined.