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In this paper a Poisson gravity model is introduced that incorporates spatial dependence of the explained variable without relying on restrictive distributional assumptions of the underlying data generating process. The model comprises a spatially filtered component -including the origin, destination and origin-destination specific variables -and a spatial residual variable that captures origin-and destination-based spatial autocorrelation. We derive a 2-stage nonlinear least squares estimator that is heteroscedasticity-robust and, thus, controls for the problem of over-or underdispersion that is often present in the empirical analysis of discrete data. It can be shown that this estimator has desirable properties for different distributional assumptions, like the observed flows or (spatially) filtered component being either Poisson or Negative Binomial. In our spatial autoregressive model specification, the resulting parameter estimates can be interpreted as the implied total impact effects and, thus, include the indirect spatial feedback effects. Monte Carlo results indicate marginal biases in mean and standard deviation of the parameter estimates and convergence to the true parameter values in finite samples. Finally, patent citation flow data are used to illustrate the application of the model.