A novel methodology for identifying environmental exposures using GPS data

Andreea Cetateanu*, Bogdan Luca, Andrei Alin Popescu, Angie S Page, Ashley R Cooper, Andy P Jones

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

13 Citations (Scopus)
571 Downloads (Pure)


Aim: While studies using GPS (Global Positioning Systems) have the potential to refine measures of exposure to the neighbourhood environment in health research, one limitation is that they do not typically identify time spent undertaking journeys in motorised vehicles when contact with the environment is reduced. This paper presents and test a novel methodology to explore the impact of this.

Methods: Using a case study of exposure assessment to food environments, an unsupervised computational algorithm is employed in order to infer two travel modes: motorised and non-motorised, on the basis of which trips were extracted. Additional criteria are imposed in order to improve robustness of the algorithm.

Results: After removing noise in the GPS data and motorised vehicle journeys, 82.43% of the initial GPS points remained. After comparing a sub-sample of trips classified visually of motorised, non-motorised and mixed mode trips with the algorithm classifications, it was found that there was an agreement of 88%. The measures of exposure to the food environment calculated before and after algorithm classification were strongly correlated.

Conclusion: Identifying non-motorised exposures to the food environment makes little difference to exposure estimates in urban children but might be important for adults or rural populations who spend more time in motorised vehicles.
Original languageEnglish
Pages (from-to)1944-1960
Number of pages17
JournalInternational Journal of Geographical Information Science
Issue number10
Early online date24 Feb 2016
Publication statusPublished - Oct 2016


  • food environments
  • Global positioning systems
  • travel mode
  • unsupervised algorithm


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