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

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.