A Spatio-Temporal Point Process Model for Ambulance Demand

• Activity Number: 220
• Type: Topic Contributed
• Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Zhengyi Zhou*+ and David Scott Matteson and Dawn B. Woodard and Shane Henderson and Athanasios Micheas
• Companies: Cornell University and Cornell University and Cornell University and Cornell University and University of Missouri-Columbia
• Keywords: Non-homogeneous Poisson point process ; Gaussian mixture model ; Markov chain Monte Carlo ; Autoregressive prior ; Emergency medical services

Abstract

We introduce a parsimonious and novel Bayesian approach for estimating ambulance demand in Toronto, Canada continuously in space for every two-hour interval; such estimates are critical for fleet management and dynamic deployment. This large-scale dataset exhibits complex spatial and temporal patterns and dynamics. We propose to model this time series of spatial densities by finite Gaussian mixture models. We fix the mixture component distributions across all time periods while letting the mixture weights evolve over time. This allows efficient estimation of the underlying spatial structure, yet enough flexibility to capture dynamics over time. We capture temporal patterns such as seasonality by introducing constraints on the mixture weights; we represent location-specific temporal dynamics by applying a separate autoregressive prior on each mixture weight. While estimation may be performed using a fixed number of mixture components, we also extend to estimate the number of components using birth-and-death Markov chain Monte Carlo. We quantify statistical and operational merits of our method over the current industry practice.