From Counts to Choice: Choice-Driven Spatial-Temporal Counting Process Models

Spatio-temporal event data---such as crime incidents or shared-mobility usage---are generated by human decisions in urban environment. Yet most existing models focus on statistical dependencies in time and space, overlooking cognitive and social factors that shape behavior. We argue that uncovering underlying preferences is essential, as they provide a structured link between observed event data and decision processes. We introduce a preference-driven framework that models event distributions through a two-stage ''consider--then--choose'' process: sparse gating captures limited attention, and utility functions guide selection within the consideration set. To capture heterogeneity, we employ a mixture-of-experts design that reveals distinct preference patterns across groups and contexts. The framework incorporates sparse structural design, and we analyze its theoretical properties by establishing approximation and generalization guarantees. Empirical studies on crime and bike-sharing datasets demonstrate competitive predictive accuracy while providing interpretable insights into behavioral drivers. By shifting focus from counts to preferences, our approach offers a behaviorally grounded and socially meaningful perspective for modeling event data, especially useful in urban life.