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PLoS Negl Trop Dis. 2014 Jun 12;8(6):e2888. doi: 10.1371/journal.pntd.0002888. eCollection 2014 Jun.

Strengths and weaknesses of Global Positioning System (GPS) data-loggers and semi-structured interviews for capturing fine-scale human mobility: findings from Iquitos, Peru.

Author information

1
Global Health Systems and Development Department, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America.
2
Department of Entomology, University of California, Davis, Davis, California, United States of America; Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.
3
Department of Entomology, University of California, Davis, Davis, California, United States of America; Graduate School of Public Health, San Diego State University, San Diego, California, United States of America.
4
Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America; Department of Environmental Studies, Emory University, Atlanta, Georgia, United States of America.
5
Graduate School of Public Health, San Diego State University, San Diego, California, United States of America.
6
U.S. Navy Medical Research Unit No. 6, Iquitos, Peru.

Abstract

Quantifying human mobility has significant consequences for studying physical activity, exposure to pathogens, and generating more realistic infectious disease models. Location-aware technologies such as Global Positioning System (GPS)-enabled devices are used increasingly as a gold standard for mobility research. The main goal of this observational study was to compare and contrast the information obtained through GPS and semi-structured interviews (SSI) to assess issues affecting data quality and, ultimately, our ability to measure fine-scale human mobility. A total of 160 individuals, ages 7 to 74, from Iquitos, Peru, were tracked using GPS data-loggers for 14 days and later interviewed using the SSI about places they visited while tracked. A total of 2,047 and 886 places were reported in the SSI and identified by GPS, respectively. Differences in the concordance between methods occurred by location type, distance threshold (within a given radius to be considered a match) selected, GPS data collection frequency (i.e., 30, 90 or 150 seconds) and number of GPS points near the SSI place considered to define a match. Both methods had perfect concordance identifying each participant's house, followed by 80-100% concordance for identifying schools and lodgings, and 50-80% concordance for residences and commercial and religious locations. As the distance threshold selected increased, the concordance between SSI and raw GPS data increased (beyond 20 meters most locations reached their maximum concordance). Processing raw GPS data using a signal-clustering algorithm decreased overall concordance to 14.3%. The most common causes of discordance as described by a sub-sample (n=101) with whom we followed-up were GPS units being accidentally off (30%), forgetting or purposely not taking the units when leaving home (24.8%), possible barriers to the signal (4.7%) and leaving units home to recharge (4.6%). We provide a quantitative assessment of the strengths and weaknesses of both methods for capturing fine-scale human mobility.

PMID:
24922530
PMCID:
PMC4055589
DOI:
10.1371/journal.pntd.0002888
[Indexed for MEDLINE]
Free PMC Article

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