title: Detecting activity locations from raw GPS data: a novel kernel-based algorithm.
abstract: BACKGROUND
  Health studies and mHealth applications are increasingly resorting to tracking technologies such as Global Positioning Systems (GPS) to study the relation between mobility, exposures, and health. GPS tracking generates large sets of geographic data that need to be transformed to be useful for health research. This paper proposes a method to test the performance of activity place detection algorithms, and compares the performance of a novel kernel-based algorithm with a more traditional time-distance cluster detection method.
  
  
  METHODS
  A set of 750 artificial GPS tracks containing three stops each were generated, with various levels of noise.. A total of 9,000 tracks were processed to measure the algorithms' capacity to detect stop locations and estimate stop durations, with varying GPS noise and algorithm parameters.
  
  
  RESULTS
  The proposed kernel-based algorithm outperformed the traditional algorithm on most criteria associated to activity place detection, and offered a stronger resilience to GPS noise, managing to detect up to 92.3% of actual stops, and estimating stop duration within 5% error margins at all tested noise levels.
  
  
  CONCLUSIONS
  Capacity to detect activity locations is an important feature in a context of increasing use of GPS devices in health and place research. While further testing with real-life tracks is recommended, testing algorithms' performance with artificial track sets for which characteristics are controlled is useful. The proposed novel algorithm outperformed the traditional algorithm under these conditions.
authors.affiliation: Montreal University Hospital Research Center (CRCHUM), Pavillon Masson - 218 3850, St-Urbain, Montreal, H2W 1T7, Canada.
authors.forename: Benoit
authors.initials: B
authors.lastname: Thierry
authors.forename: Basile
authors.initials: B
authors.lastname: Chaix
authors.forename: Yan
authors.initials: Y
authors.lastname: Kestens
mesh_terms: D000465:Algorithms; D040362:Geographic Information Systems / Q000592:standards / Q000706:statistics & numerical data* / Q000639:trends; D006304:Health Status; D006801:Humans; D013223:Statistics as Topic / Q000379:methods; D013997:Time Factors