Foursquare to the Rescue: Predicting Ambulance Calls Across Geographies

Abstract

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.