A novel methodology for identifying environmental exposures using GPS data

Cetateanu, Andreea, Luca, Bogdan Alexandru, Popescu, Andrei Alin, Page, Angie, Cooper, Ashley and Jones, Andy (2016) A novel methodology for identifying environmental exposures using GPS data. International Journal of Geographical Information Science, 30 (10). pp. 1944-1960. ISSN 1365-8816

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Abstract

Aim: While studies using global positioning systems (GPS) 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 tests a novel methodology to explore the impact of this concern. 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. In addition, 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.

Item Type: Article
Additional Information: © 2016 The Author(s). Published by Taylor & Francis. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. Permission is granted subject to the terms of the License under which the work was published. Please check the License conditions for the work which you wish to reuse. Full and appropriate attribution must be given. This permission does not cover any third party copyrighted material which may appear in the work requested.
Uncontrolled Keywords: food environments,global positioning systems,travel mode,unsupervised algorithm
Faculty \ School: Faculty of Science > School of Computing Sciences
Faculty of Medicine and Health Sciences > Norwich Medical School
Faculty of Science
Related URLs:
Depositing User: Pure Connector
Date Deposited: 17 Mar 2016 11:01
Last Modified: 22 Apr 2020 01:10
URI: https://ueaeprints.uea.ac.uk/id/eprint/57521
DOI: 10.1080/13658816.2016.1145682

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