A result of Meyer (1971) describes a way of transforming a point process on the line into a Poisson process. This transformation is useful for evaluating models for one-dimensional point processes. Past efforts to generalize Meyer's theorem to higher dimensions are reviewed and a new result is presented. This result is sufficiently general to apply to a wide variety of multi-dimensional point processes. The corresponding model assessment technique is applied to various models for earthquake occurrences using a catalog of 2,402 micro-earthquakes occurring in Parkfield, California between 1988 and 1995. Models characterized by self-exciting behavior, including branching models and short-term exciting long-term correcting (SELC) models, are shown to offer superior fit to some simple Poisson, renewal and Markov models.