# Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information Source: [Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:39:29.972Z Authors: Maya Okawa, Tomoharu Iwata, Takeshi Kurashima, Yusuke Tanaka, Hiroyuki Toda, Naonori Ueda Published: 2019-06-21T05:29:40.000Z ## Abstract Predicting when and where events will occur in cities, like taxi pick-ups, crimes, and vehicle collisions, is a challenging and important problem with many applications in fields such as urban planning, transportation optimization and location-based marketing. Though many point processes have been proposed to model events in a continuous spatio-temporal space, none of them allow for the consideration of the rich contextual factors that affect event occurrence, such as weather, social activities, geographical characteristics, and traffic. In this paper, we propose \\textsf{DMPP} (Deep Mixture Point Processes), a point process model for predicting spatio-temporal events with the use of rich contextual information; a key advance is its incorporation of the heterogeneous and high-dimensional context available in image and text data. Specifically, we design the intensity of our point process model as a mixture of kernels, where the mixture weights are modeled by a deep neural network. This formulation allows us to automatically learn the complex nonlinear effects of the contextual factors on event occurrence. At the same time, this formulation makes analytical integration over the intensity, which is required for point process estimation, tractable. We use real-world data sets from different domains to demonstrate that DMPP has better predictive performance than existing methods. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Spatio-temporal Event Modeling on Crimes in Vancouver](): Spatio-temporal event modeling; Crimes in Vancouver; TLL: 60.09. - [Spatio-temporal event modeling on NYC Vehicle Collisions](): Spatio-temporal event modeling; NYC Vehicle Collisions; TLL: 394.54. - [Spatio-temporal event modeling on NYC Complaint Data](): Spatio-temporal event modeling; NYC Complaint Data; TLL: 2.11. ## Original sources and implementation [Original paper]() [PDF]()