title: Linking GPS and travel diary data using sequence alignment in a study of children's independent mobility.
abstract: BACKGROUND
  Global positioning systems (GPS) are increasingly being used in health research to determine the location of study participants. Combining GPS data with data collected via travel/activity diaries allows researchers to assess where people travel in conjunction with data about trip purpose and accompaniment. However, linking GPS and diary data is problematic and to date the only method has been to match the two datasets manually, which is time consuming and unlikely to be practical for larger data sets. This paper assesses the feasibility of a new sequence alignment method of linking GPS and travel diary data in comparison with the manual matching method.
  
  
  METHODS
  GPS and travel diary data obtained from a study of children's independent mobility were linked using sequence alignment algorithms to test the proof of concept. Travel diaries were assessed for quality by counting the number of errors and inconsistencies in each participant's set of diaries. The success of the sequence alignment method was compared for higher versus lower quality travel diaries, and for accompanied versus unaccompanied trips. Time taken and percentage of trips matched were compared for the sequence alignment method and the manual method.
  
  
  RESULTS
  The sequence alignment method matched 61.9% of all trips. Higher quality travel diaries were associated with higher match rates in both the sequence alignment and manual matching methods. The sequence alignment method performed almost as well as the manual method and was an order of magnitude faster. However, the sequence alignment method was less successful at fully matching trips and at matching unaccompanied trips.
  
  
  CONCLUSIONS
  Sequence alignment is a promising method of linking GPS and travel diary data in large population datasets, especially if limitations in the trip detection algorithm are addressed.
authors.affiliation: SHORE and Whariki Research Centre, School of Public Health, Massey University, Auckland, New Zealand. s.mavoa@massey.ac.nz
authors.forename: Suzanne
authors.initials: S
authors.lastname: Mavoa
authors.forename: Melody
authors.initials: M
authors.lastname: Oliver
authors.forename: Karen
authors.initials: K
authors.lastname: Witten
authors.forename: Hannah M
authors.initials: HM
authors.lastname: Badland
mesh_terms: D000465:Algorithms; D016009:Chi-Square Distribution; D002648:Child; D002669:Child Welfare; D003625:Data Collection / Q000295:instrumentation / Q000379:methods; D005240:Feasibility Studies; D005260:Female; D040362:Geographic Information Systems; D006302:Health Services Research; D006801:Humans; D008297:Male; D008499:Medical Records / Q000706:statistics & numerical data*; D015980:Public Health Practice; D013223:Statistics as Topic; D014195:Travel / Q000706:statistics & numerical data*