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Geographic Imputation of Missing Activity Space Data from Ecological Momentary Assessment (EMA) GPS Positions

Author

Listed:
  • Jeremy Mennis

    (Department of Geography and Urban Studies, Temple University, Philadelphia, PA 19122, USA)

  • Michael Mason

    (Center for Behavioral Health Research, University of Tennessee, Knoxville, TN 37996, USA)

  • Donna L. Coffman

    (Department of Epidemiology and Biostatistics, Temple University, Philadelphia, PA 19122, USA)

  • Kevin Henry

    (Department of Geography and Urban Studies, Temple University, Philadelphia, PA 19122, USA)

Abstract

This research presents a pilot study to develop and compare methods of geographic imputation for estimating the location of missing activity space data collected using geographic ecological momentary assessment (GEMA). As a demonstration, we use data from a previously published analysis of the effect of neighborhood disadvantage, captured at the U.S. Census Bureau tract level, on momentary psychological stress among a sample of 137 urban adolescents. We investigate the impact of listwise deletion on model results and test two geographic imputation techniques adapted for activity space data from hot deck and centroid imputation approaches. Our results indicate that listwise deletion can bias estimates of place effects on health, and that these impacts are mitigated by the use of geographic imputation, particularly regarding inflation of the standard errors. These geographic imputation techniques may be extended in future research by incorporating approaches from the non-spatial imputation literature as well as from conventional geographic imputation and spatial interpolation research that focus on non-activity space data.

Suggested Citation

  • Jeremy Mennis & Michael Mason & Donna L. Coffman & Kevin Henry, 2018. "Geographic Imputation of Missing Activity Space Data from Ecological Momentary Assessment (EMA) GPS Positions," IJERPH, MDPI, vol. 15(12), pages 1-15, December.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:12:p:2740-:d:187924
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    References listed on IDEAS

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    1. Chiara Bocci & Emilia Rocco, 2014. "Estimates for geographical domains through geoadditive models in presence of incomplete geographical information," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 283-305, June.
    2. Mei-Po Kwan, 2018. "The Limits of the Neighborhood Effect: Contextual Uncertainties in Geographic, Environmental Health, and Social Science Research," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 108(6), pages 1482-1490, November.
    3. McQuoid, Julia & Thrul, Johannes & Ling, Pamela, 2018. "A geographically explicit ecological momentary assessment (GEMA) mixed method for understanding substance use," Social Science & Medicine, Elsevier, vol. 202(C), pages 89-98.
    4. Frank C Curriero & Martin Kulldorff & Francis P Boscoe & Ann C Klassen, 2010. "Using Imputation to Provide Location Information for Nongeocoded Addresses," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-10, February.
    5. Stahler, G.J. & Mennis, J. & Baron, D.A., 2013. "Geospatial technology and the "exposome": New perspectives on addiction," American Journal of Public Health, American Public Health Association, vol. 103(8), pages 1354-1356.
    6. EunHye Yoo & C. Rudra & M. Glasgow & L. Mu, 2015. "Geospatial Estimation of Individual Exposure to Air Pollutants: Moving from Static Monitoring to Activity-Based Dynamic Exposure Assessment," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(5), pages 915-926, September.
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    Cited by:

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    2. Yohan Kim & Scott Kelly & Deepu Krishnan & Jay Falletta & Kerryn Wilmot, 2022. "Strategies for Imputation of High-Resolution Environmental Data in Clinical Randomized Controlled Trials," IJERPH, MDPI, vol. 19(3), pages 1-17, January.

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