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How mobility-based exposure measures may mitigate the underestimation of the association between green space exposures and health

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  • Liu, Yang
  • Kwan, Mei-Po
  • Song, Liuyi
  • Yu, Changda
  • Cui, Yuhan

Abstract

Recent urban green space research highlighted that mobility-based measures of green space exposure may significantly mitigate a particular type of exposure measurement error (contextual errors) of residence-based measures. In this study, we examined an important manifestation of the contextual errors of residence-based measures: neighborhood effect averaging. We analytically illustrated that the contextual errors of residence-based measures may lead to a considerable underestimation of the associations between green space exposures and human health, and the reduction of such underestimation can be quantified through a mitigating factor. We employed data from a cross-sectional survey to assess the usefulness of our analytics. Based on participants' 7-day GPS trajectories, we derived residence-based and mobility-based measures of participants' exposures to green space using a spatiotemporally weighted approach. Logistic regression was employed to estimate the associations between green space exposures and participants’ overall health. We derived consistent and significant mitigating factors based on our analytics from the magnitudes of the estimated associations or the variances of green space exposure distributions. Our results indicate that mobility-based measures reduced about 20.9 % – 52.3 % of the underestimation of the associations between green space exposure and health, which reflected the considerable influence of exposure measurement errors. Our study sheds light on how contextual errors may obfuscate the association between green space exposures and human health, which may also be true for other mobility-dependent environmental factors. This has crucial implications for a broad range of environmental and public health studies that need accurate estimation of health impacts.

Suggested Citation

  • Liu, Yang & Kwan, Mei-Po & Song, Liuyi & Yu, Changda & Cui, Yuhan, 2025. "How mobility-based exposure measures may mitigate the underestimation of the association between green space exposures and health," Social Science & Medicine, Elsevier, vol. 379(C).
    • Handle: RePEc:eee:socmed:v:379:y:2025:i:c:s0277953625005209
    DOI: 10.1016/j.socscimed.2025.118190
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    References listed on IDEAS

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    1. Liu, Yang & Kwan, Mei-Po & Wong, Man Sing & Yu, Changda, 2023. "Current methods for evaluating people's exposure to green space: A scoping review," Social Science & Medicine, Elsevier, vol. 338(C).
    2. Perchoux, Camille & Kestens, Yan & Thomas, Frédérique & Hulst, Andraea Van & Thierry, Benoit & Chaix, Basile, 2014. "Assessing patterns of spatial behavior in health studies: Their socio-demographic determinants and associations with transportation modes (the RECORD Cohort Study)," Social Science & Medicine, Elsevier, vol. 119(C), pages 64-73.
    3. Yu, Changda & Kwan, Mei-Po & Liu, Yang, 2024. "Assessing momentary stress responses to dynamic real-time greenspace exposure: Unveiling algorithmic uncertainty and the temporality of exposure context," Social Science & Medicine, Elsevier, vol. 363(C).
    4. Bertram, Christine & Rehdanz, Katrin, 2015. "The role of urban green space for human well-being," Ecological Economics, Elsevier, vol. 120(C), pages 139-152.
    5. Mei-Po Kwan, 2018. "The Neighborhood Effect Averaging Problem (NEAP): An Elusive Confounder of the Neighborhood Effect," IJERPH, MDPI, vol. 15(9), pages 1-4, August.
    6. Wang, Jianying & Liu, Yang & Kwan, Mei-Po, 2025. "Cross-validation between GPS-derived trajectories and activity-travel diaries for transport geography studies," Journal of Transport Geography, Elsevier, vol. 126(C).
    7. Spielman, Seth E. & Yoo, Eun-hye, 2009. "The spatial dimensions of neighborhood effects," Social Science & Medicine, Elsevier, vol. 68(6), pages 1098-1105, March.
    8. Junghwan Kim & Mei-Po Kwan, 2021. "How Neighborhood Effect Averaging Might Affect Assessment of Individual Exposures to Air Pollution: A Study of Ozone Exposures in Los Angeles," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 111(1), pages 121-140, January.
    9. Jianwei Huang & Mei-Po Kwan, 2022. "Uncertainties in the Assessment of COVID-19 Risk: A Study of People’s Exposure to High-Risk Environments Using Individual-Level Activity Data," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 112(4), pages 968-987, April.
    10. Mei-Po Kwan, 2016. "Algorithmic Geographies: Big Data, Algorithmic Uncertainty, and the Production of Geographic Knowledge," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 106(2), pages 274-282, March.
    11. Yang Liu & Mei-Po Kwan & Changda Yu, 2024. "How Mobility and Temporal Contexts May Affect Environmental Exposure Measurements: Using Outdoor Artificial Light at Night (ALAN) and Urban Green Space as Examples," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 114(5), pages 1098-1117, May.
    12. Liu, Dong & Kwan, Mei-Po & Kan, Zihan & Liu, Yang, 2023. "Examining individual-level tri-exposure to greenspace and air/noise pollution using individual-level GPS-based real-time sensing data," Social Science & Medicine, Elsevier, vol. 329(C).
    13. Yang Liu & Mei-Po Kwan & Zihan Kan, 2023. "Inconsistent Association between Perceived Air Quality and Self-Reported Respiratory Symptoms: A Pilot Study and Implications for Environmental Health Studies," IJERPH, MDPI, vol. 20(2), pages 1-17, January.
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