Individual Mobility and Uncertain Geographic Context: Real-time Versus Neighborhood Approximated Exposure to Retail Tobacco Outlets Across the US

Thomas R. Kirchner, Hong Gao, Daniel J. Lewis, Andrew Anesetti-Rothermel, Heather A. Carlos, Brian House

Research output: Contribution to journalArticlepeer-review

Abstract

There is growing interest in the way exposure to neighborhood risk and protective factors affects the health of residents. Although multiple approaches have been reported, empirical methods for contrasting the spatial uncertainty of exposure estimates are not well established. The objective of this paper was to contrast real-time versus neighborhood approximated exposure to the landscape of tobacco outlets across the contiguous US. A nationwide density surface of tobacco retail outlet locations was generated using kernel density estimation (KDE). This surface was linked to participants’ (Np = 363) inferred residential location, as well as to their real-time geographic locations, recorded every 10 min over 180 days. Real-time exposure was estimated as the hourly product of radius of gyration and average tobacco outlet density (Nhour = 304, 164 h). Ordinal logit modeling was used to assess the distribution of real-time exposure estimates as a function of each participant’s residential exposure. Overall, 61.3% of real-time, hourly exposures were of relatively low intensity, and after controlling for temporal and seasonal variation, 72.8% of the variance among these low-level exposures was accounted for by residence in one of the two lowest residential exposure quintiles. Most moderate to high intensity exposures (38.7% of all real-time, hourly exposures) were no more likely to have been contributed by subjects from any single residential exposure cluster than another. Altogether, 55.2% of the variance in real-time exposures was not explained by participants’ residential exposure cluster. Calculating hourly exposure estimates made it possible to directly contrast real-time observations with static residential exposure estimates. Results document the substantial degree that real-time exposures can be misclassified by residential approximations, especially in residential areas characterized by moderate to high retail density levels.

Original languageEnglish (US)
Pages (from-to)70-85
Number of pages16
JournalJournal of Healthcare Informatics Research
Volume3
Issue number1
DOIs
StatePublished - Mar 15 2019

Keywords

  • Exposure science
  • Human mobility
  • Retail environment
  • Spatial uncertainty
  • Urban computing

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Information Systems
  • Artificial Intelligence

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