  Accurately predicting when and where ambulance call-outs occur can reduce
response times and ensure the patient receives urgent care sooner. Here we
present a novel method for ambulance demand prediction using Gaussian process
regression (GPR) in time and geographic space. The method exhibits superior
accuracy to MEDIC, a method which has been used in industry. The use of GPR has
additional benefits such as the quantification of uncertainty with each
prediction, the choice of kernel functions to encode prior knowledge and the
ability to capture spatial correlation. Measures to increase the utility of GPR
in the current context, with large training sets and a Poisson-distributed
output, are outlined.
