14 citations · 74 across the 16 of their papers we have counts for
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stat.AP2015★ 14 cited
Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach
Zhengyi Zhou, David S. Matteson
Predicting ambulance demand accurately at fine time and location scales is critical for ambulance fleet management and dynamic deployment. Large-scale datasets in this setting typi…
stat.AP2015
Predicting Melbourne Ambulance Demand using Kernel Warping
Zhengyi Zhou, David S. Matteson
Predicting ambulance demand accurately in fine resolutions in space and time is critical for ambulance fleet management and dynamic deployment. Typical challenges include data spar…
stat.AP2014★ 2 cited
A Spatio-Temporal Point Process Model for Ambulance Demand
Zhengyi Zhou, David S. Matteson, Dawn B. Woodard +2
Ambulance demand estimation at fine time and location scales is critical for fleet management and dynamic deployment. We are motivated by the problem of estimating the spatial dist…