  Understanding how ambulance incidents are spatially distributed can shed
light to the epidemiological dynamics of geographic areas and inform healthcare
policy design. Here we analyze a longitudinal dataset of more than four million
ambulance calls across a region of twelve million residents in the North West
of England. With the aim to explain geographic variations in ambulance call
frequencies, we employ a wide range of data layers including open government
datasets describing population demographics and socio-economic characteristics,
as well as geographic activity in online services such as Foursquare. Working
at a fine level of spatial granularity we demonstrate that daytime population
levels and the deprivation status of an area are the most important variables
when it comes to predicting the volume of ambulance calls at an area.
Foursquare check-ins on the other hand complement these government sourced
indicators, offering a novel view to population nightlife and commercial
activity locally. We demonstrate how check-in activity can provide an edge when
predicting certain types of emergency incidents in a multi-variate regression
model.
