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A new point process transition density model is proposed based on the theory of point patterns for predicting the likelihood of occurrence of spatial-temporal random events. The model provides a framework for discovering and incorporating event initiation preferences in terms of clusters of feature values. Components of the proposed model are specified taking into account additional behavioral assumptions such as the "journey to event" and "lingering period to resume act." Various feature selection techniques are presented in conjunction with the proposed model. Extending knowledge discovery into feature space allows for extrapolation beyond spatial or temporal continuity and is shown to be a major advantage of our model over traditional approaches. We examine the proposed model primarily in the context of predicting criminal events in space and time.