How Neighborhood Effect Averaging Might Affect Assessment of Individual Exposures to Air Pollution: A Study of Ozone Exposures in Los Angeles

References

• American Lung Association. 2018. State of the air. Accessed June 18, 2019. https://www.lung.org/local-content/california/our-initiatives/state-of-the-air/2018/state-of-the-air-2018.html.
   Google Scholar
• Anselin, L. 1988. Lagrange multiplier test diagnostics for spatial dependence and spatial heterogeneity. Geographical Analysis 20 (1):1–17. doi: 10.1111/j.1538-4632.1988.tb00159.x.
   Web of Science ®Google Scholar
• Arcaya, C., R. Tucker-Seeley, R. Kim, A. Schnake-Mahl, M. So, and S. Subramanian. 2016. Research on neighborhood effects on health in the United States: A systematic review of study characteristics. Social Science & Medicine 168:16–29. doi: 10.1016/j.socscimed.2016.08.047.
   PubMed Web of Science ®Google Scholar
• Axhausen, K. W., A. Zimmermann, S. Schönfelder, G. Rindsfüser, and T. Haupt. 2002. Observing the rhythms of daily life: A six-week travel diary. Transportation 29 (2):95–124.
   Web of Science ®Google Scholar
• Barajas, J., D. Chatman, and A. Agrawal. 2018. Immigration, income, and public transit perceptions: Findings from an intercept survey. Journal of Public Transportation 21 (2):1–18. doi: 10.5038/2375-0901.21.2.1.
   Web of Science ®Google Scholar
• Barth, D. 2009. The bright side of sitting in traffic: Crowdsourcing road congestion data. Accessed December 25, 2019. https://googleblog.blogspot.com/2009/08/bright-side-of-sitting-in-traffic.html.
   Google Scholar
• Berman, J., P. Breysse, R. White, D. Waugh, and F. Curriero. 2015. Evaluating methods for spatial mapping: Applications for estimating ozone concentrations across the contiguous United States. Environmental Technology & Innovation 3:1–10. doi: 10.1016/j.eti.2014.10.003.
   Web of Science ®Google Scholar
• Brauer, M., C. Reynolds, and P. Hystad. 2013. Traffic-related air pollution and health in Canada. Canadian Medical Association Journal 185 (18):1557–58. doi: 10.1503/cmaj.121568.
   Web of Science ®Google Scholar
• California Environmental Protection Agency. 2014. California Communities Environmental Health Screening Tool report. Accessed October 14, 2019. https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-version-20.
   Google Scholar
• Chen, X., and M.-P. Kwan. 2015. Contextual uncertainties, human mobility, and perceived food environment: The uncertain geographic context problem in food access research. American Journal of Public Health 105 (9):1734–37. doi: 10.2105/AJPH.2015.302792.
   PubMed Web of Science ®Google Scholar
• Clary, C., S. Matthews, and Y. Kestens. 2017. Between exposure, access and use: Reconsidering foodscape influences on dietary behaviours. Health & Place 44:1–7. doi: 10.1016/j.healthplace.2016.12.005.
   PubMed Web of Science ®Google Scholar
• Cummins, S. 2007. Investigating neighborhood effects on health—Avoiding the local trap. International Journal of Epidemiology 36 (2):355–57. doi: 10.1093/ije/dym033.
   PubMed Web of Science ®Google Scholar
• Dewulf, B., T. Neutens, W. Lefebvre, G. Seynaeve, C. Vanpoucke, C. Beckx, and N. Van de Weghe. 2016. Dynamic assessment of exposure to air pollution using mobile phone data. International Journal of Health Geographics 15 (1):1–14. doi: 10.1186/s12942-016-0042-z.
   Google Scholar
• Diez Roux, A. V. 2001. Investigating neighborhood and area effects on health. American Journal of Public Health 91 (11):1783–89. doi: 10.2105/AJPH.91.11.1783.
   PubMed Web of Science ®Google Scholar
• Dons, E., L. Panis, M. Poppel, J. Theunis, H. Willems, R. Torfs, and G. Wets. 2011. Impact of time–activity patterns on personal exposure to black carbon. Atmospheric Environment 45 (21):3594–3602. doi: 10.1016/j.atmosenv.2011.03.064.
   Web of Science ®Google Scholar
• Elliott, R., and K. Smiley. 2019. Place, space, and racially unequal exposures to pollution at home and work. Social Currents 6 (1):32–50. doi: 10.1177/2329496517704873.
   Google Scholar
• England, K. V. L. 1993. Suburban pink collar ghettos: The spatial entrapment of women? Annals of the Association of American Geographers 83 (2):225–42. doi: 10.1111/j.1467-8306.1993.tb01933.x.
   Web of Science ®Google Scholar
• Federal Highway Administration. 2014. Mobility challenges for households in poverty. Accessed June 19, 2019. https://nhts.ornl.gov/publications.
   Google Scholar
• Federal Highway Administration. 2018. Summary of travel trends: 2017 National Household Travel Survey. Accessed June 18, 2019. https://nhts.ornl.gov/publications.
   Google Scholar
• Federal Highway Administration. 2019. Travel behavior trend analysis of workers and non-workers. Accessed June 19, 2019. https://nhts.ornl.gov/publications.
   Google Scholar
• Frumkin, H. 2006. The measure of place. American Journal of Preventive Medicine 31 (6):530–32. doi: 10.1016/j.amepre.2006.08.022.
   Web of Science ®Google Scholar
• Gilliland, F., E. Avol, P. Kinney, M. Jerrett, T. Dvonch, F. Lurmann, T. Buckley, P. Breysse, G. Keeler, T. de Villiers et al. 2005. Air pollution exposure assessment for epidemiologic studies of pregnant women and children: Lessons learned from the Centers for Children’s Environmental Health and Disease Prevention Research. Environmental Health Perspectives 113 (10):1447–54. doi: 10.1289/ehp.7673.
   Web of Science ®Google Scholar
• Google. 2019. Google Maps directions API. Accessed December 25, 2019. https://developers.google.com/maps/documentation/directions/start.
   Google Scholar
• Gulliver, J., and D. Briggs. 2005. Time–space modeling of journey-time exposure to traffic-related air pollution using GIS. Environmental Research 97 (1):10–25. doi: 10.1016/j.envres.2004.05.002.
   PubMed Web of Science ®Google Scholar
• Ha, S., H. Hu, D. Roussos-Ross, K. Haidong, J. Roth, and X. Xu. 2014. The effects of air pollution on adverse birth outcomes. Environmental Research 134:198–204. doi: 10.1016/j.envres.2014.08.002.
   Web of Science ®Google Scholar
• Hägerstrand, T. 1970. What about people in regional science?. Papers of the Regional Science Association 24 (1):6–21. doi: 10.1007/BF01936872.
   Google Scholar
• Health Effects Institute. 2010. Traffic-related air pollution: A critical review of the literature on emissions, exposure, and health effects. Accessed June 19, 2019. https://www.healtheffects.org/publication/traffic-related-air-pollution-critical-review-literature-emissions-exposure-and-health.
   Google Scholar
• Houston, D., T. Luong, and M. Boarnet. 2014. Tracking daily travel: Assessing discrepancies between GPS-derived and self-reported travel patterns. Transportation Research Part C: Emerging Technologies 48:97–108. doi: 10.1016/j.trc.2014.08.013.
   Web of Science ®Google Scholar
• Houston, D., J. Wu, P. Ong, and A. Winer. 2004. Structural disparities of urban traffic in Southern California: Implications for vehicle-related air pollution exposure in minority and high-poverty neighborhoods. Journal of Urban Affairs 26 (5):565–92. doi: 10.1111/j.0735-2166.2004.00215.x.
   Web of Science ®Google Scholar
• Hurvitz, P. M., and A. V. Moudon. 2012. Home versus nonhome neighborhood. American Journal of Preventive Medicine 42 (4):411–17. doi: 10.1016/j.amepre.2011.11.015.
   PubMed Web of Science ®Google Scholar
• Inagami, S., D. A. Cohen, and B. K. Finch. 2007. Non-residential neighborhood exposures suppress neighborhood effects on self-rated health. Social Science & Medicine 65 (8):1779–91. doi: 10.1016/j.socscimed.2007.05.051.
   PubMed Web of Science ®Google Scholar
• Jerrett, M., R. Burnett, R. Ma, C. Pope, D. Krewski, K. Newbold, G. Thurston, et al. 2005. Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology 16 (6):727–36. doi: 10.1097/01.ede.0000181630.15826.7d.
   PubMed Web of Science ®Google Scholar
• Jones, M., and A. Pebley. 2014. Redefining neighborhoods using common destinations: Social characteristics of activity spaces and home census tracts compared. Demography 51 (3):727–52. doi: 10.1007/s13524-014-0283-z.
   PubMed Web of Science ®Google Scholar
• Kim, H., J. Li, S. Roodman, A. Sen, S. Soot, and E. Christopher. 1993. Factoring household travel surveys. Transportation Research Record 1142:17–22.
   Google Scholar
• Kim, J., and M.-P. Kwan. 2019. Beyond commuting: Ignoring individuals’ activity-travel patterns may lead to inaccurate assessments of their exposure to traffic congestion. International Journal of Environmental Research and Public Health 16 (1):89. doi: 10.3390/ijerph16010089.
   Web of Science ®Google Scholar
• Kim, J., and B. Lee. 2019. More than travel time: New accessibility index capturing the connectivity of transit services. Journal of Transport Geography 78:8–18. doi: 10.1016/j.jtrangeo.2019.05.008.
   Web of Science ®Google Scholar
• Klous, G., L. M. Smit, F. Borlée, R. Coutinho, M. Kretzschmar, D. Heederik, and A. Huss. 2017. Mobility assessment of a rural population in the Netherlands using GPS measurements. International Journal of Health Geographics 16 (1):1–13. doi: 10.1186/s12942-017-0103-y.
   PubMedGoogle Scholar
• Künzli, N., M. Jerrett, W. Mack, B. Beckerman, L. LaBree, F. Gilliland, D. Thomas, J. Peters, and H. Hodis. 2005. Ambient air pollution and atherosclerosis in Los Angeles. Environmental Health Perspectives 113 (2):201–6. doi: 10.1289/ehp.7523.
   PubMed Web of Science ®Google Scholar
• Kwan, M.-P. 1999. Gender, the home-work link, and space-time patterns of nonemployment activities. Economic Geography 75 (4):370–94.
   Web of Science ®Google Scholar
• Kwan, M.-P. 1998. Space-time and integral measures of individual accessibility: A comparative analysis using a point-based framework. Geographical Analysis 30 (3):191–216. doi: 10.1111/j.1538-4632.1998.tb00396.x.
   Web of Science ®Google Scholar
• Kwan, M.-P. 2012. The uncertain geographic context problem. Annals of the Association of American Geographers 102 (5):958–68.
   Web of Science ®Google Scholar
• Kwan, M.-P. 2013. Beyond space (as we knew it): Toward temporally integrated geographies of segregation, health, and accessibility. Annals of the Association of American Geographers 103 (5):1078–86.
   Web of Science ®Google Scholar
• Kwan, M.-P. 2018a. The limits of the neighborhood effect: Contextual uncertainties in geographic, environmental health, and social science research. Annals of the American Association of Geographers 108 (6):1482–90.
   Web of Science ®Google Scholar
• Kwan, M.-P. 2018b. The neighborhood effect averaging problem (NEAP): An elusive confounder of the neighborhood effect. International Journal of Environmental Research and Public Health 15 (9):1841.
   PubMed Web of Science ®Google Scholar
• Kwan, M.-P., J. Wang, M. Tyburski, D. H. Epstein, W. J. Kowalczyk, and K. L. Preston. 2019. Uncertainties in the geographic context of health behaviors: A study of substance users’ exposure to psychosocial stress using GPS data. International Journal of Geographical Information Science 33 (6):1176–95. doi: 10.1080/13658816.2018.1503276.
   Web of Science ®Google Scholar
• Leal, C., and B. Chaix. 2011. The influence of geographic life environments on cardiometabolic risk factors: A systematic review, a methodological assessment and a research agenda. Obesity Reviews 12 (3):217–30. doi: 10.1111/j.1467-789X.2010.00726.x.
   PubMed Web of Science ®Google Scholar
• Lee, K., and M.-P. Kwan. 2018. Automatic physical activity and in-vehicle status classification based on GPS and accelerometer data: A hierarchical classification approach using machine learning techniques. Transactions in GIS 22 (6):1522–49. doi: 10.1111/tgis.12485.
   Web of Science ®Google Scholar
• Lenntorp, B. 1976. Paths in time space environments: A time geographic study of movement possibilities of individuals. Lund, Sweden: Gleerup.
   Google Scholar
• Lenntorp, B. 1999. Time-geography—At the end of its beginning. GeoJournal 48 (3):155–58.
   Google Scholar
• Levy, J., T. Carrothers, J. Tuomisto, J. Hammitt, and J. Evans. 2001. Assessing the public health benefits of reduced ozone concentrations. Environmental Health Perspectives 109 (12):1215–26. doi: 10.1289/ehp.011091215.
   PubMed Web of Science ®Google Scholar
• Lu, Y., and T. Fang. 2015. Examining personal air pollution exposure, intake, and health danger zone using time geography and 3D geovisualization. ISPRS International Journal of Geo-Information 4:32–46.
   Google Scholar
• Lurmann, F., E. Avol, and F. Gilliland. 2015. Emissions reduction policies and recent trends in Southern California’s ambient air quality. Journal of the Air & Waste Management Association 65 (3):324–35. doi: 10.1080/10962247.2014.991856.
   PubMed Web of Science ®Google Scholar
• Ma, J., Y. Tao, M.-P. Kwan, and Y. Chai. 2020. Assessing mobility-based real-time air pollution exposure in space and time using smart sensors and GPS trajectories in Beijing. Annals of the American Association of Geographers 110 (2):434–48. doi: 10.1080/24694452.2019.1653752.
   Web of Science ®Google Scholar
• Ma, X., X. Li, M.‐P. Kwan, and Y. Chai. 2020. Who could not avoid exposure to high levels of residence‐based pollution by daily mobility? Evidence of air pollution exposure from the perspective of the neighborhood effect averaging problem (NEAP). International Journal of Environmental Research and Public Health 17 (4):1223. doi: 10.3390/ijerph17041223.
   Web of Science ®Google Scholar
• Macintyre, S., and A. Ellaway. 2003. Neighborhoods and health: An overview. In Neighborhoods and health, ed. I. Kawachi and L. F. Berkman, 20–42. Oxford, UK: Oxford University Press.
   Google Scholar
• Matthews, S. A. 2008. The salience of neighborhood: Some lessons from sociology. American Journal of Preventive Medicine 34 (3):257–59. doi: 10.1016/j.amepre.2007.12.001.
   PubMed Web of Science ®Google Scholar
• Matz, J., D. Stieb, and O. Brion. 2015. Urban-rural differences in daily time-activity patterns, occupational activity and housing characteristics. Environmental Health 14 (1):1–11. doi: 10.1186/s12940-015-0075-y.
   PubMedGoogle Scholar
• McLafferty, S., and V. Preston. 1996. Spatial mismatch and employment in a decade of restructuring. The Professional Geographer 48 (4):420–31. doi: 10.1111/j.0033-0124.1996.00420.x.
   Web of Science ®Google Scholar
• Miller, H. J. 1999. Measuring space-time accessibility benefits within transportation networks: Basic theory and computational procedures. Geographical Analysis 31 (1):187–212. doi: 10.1111/j.1538-4632.1999.tb00976.x.
   Web of Science ®Google Scholar
• National Renewable Energy Laboratory. 2019. Transportation Secure Data Center. Accessed June 19, 2019. https://www.nrel.gov/transportation/secure-transportation-data/.
   Google Scholar
• Nyhan, M., I. Kloog, R. Britter, C. Ratti, and P. Koutrakis. 2019. Quantifying population exposure to air pollution using individual mobility patterns inferred from mobile phone data. Journal of Exposure Science & Environmental Epidemiology 29 (2):238–47. doi: 10.1038/s41370-018-0038-9.
   Web of Science ®Google Scholar
• O’Brien, R. 2007. A caution regarding rules of thumb for variance inflation factors. Quality & Quantity 41 (5):673–90. doi: 10.1007/s11135-006-9018-6.
   Web of Science ®Google Scholar
• Park, Y., and G. Akar. 2019. Why do bicyclists take detours? A multilevel regression model using smartphone GPS data. Journal of Transport Geography 74:191–200. doi: 10.1016/j.jtrangeo.2018.11.013.
   Web of Science ®Google Scholar
• Park, Y., and M.-P. Kwan. 2017. Individual exposure estimates may be erroneous when spatiotemporal variability of air pollution and human mobility are ignored. Health & Place 43:85–94. doi: 10.1016/j.healthplace.2016.10.002.
   PubMed Web of Science ®Google Scholar
• Parkes, D. N., and N. Thrift. 1975. Timing space and spacing time. Environment and Planning A: Economy and Space 7 (6):651–70. doi: 10.1068/a070651.
   Google Scholar
• Phillips, D. L., E. H. Lee, A. A. Herstrom, W. E. Hogsett, and D. T. Tingey. 1997. Use of auxiliary data for spatial interpolation of ozone exposure in southeastern forests. Environmetrics 8 (1):43–61. doi: 10.1002/(SICI)1099-095X(199701)8:1<43::AID-ENV237>3.0.CO;2-G.
   Web of Science ®Google Scholar
• Rapino, M. A., and T. J. Cooke. 2011. Commuting, gender roles, and entrapment: A national study utilizing spatial fixed effects and control groups. The Professional Geographer 63 (2):277–94. doi: 10.1080/00330124.2010.547790.
   Web of Science ®Google Scholar
• Sampson, R. J. 2019. Neighbourhood effects and beyond: Explaining the paradoxes of inequality in the changing American metropolis. Urban Studies 56 (1):3–32. doi: 10.1177/0042098018795363.
   Web of Science ®Google Scholar
• Sampson, R. J., J. D. Morenoff, and F. Earls. 1999. Beyond social capital: Spatial dynamics of collective efficacy for children. American Sociological Review 64 (5):633–60. doi: 10.2307/2657367.
   Web of Science ®Google Scholar
• Sampson, R. J., S. W. Raudenbush, and F. Earls. 1997. Neighborhoods and violent crime: A multilevel study of collective efficacy. Science 277 (5328):918–24. doi: 10.1126/science.277.5328.918.
   PubMed Web of Science ®Google Scholar
• Setton, E., J. Marshall, M. Brauer, K. Lundquist, P. Hystad, P. Keller, and D. Cloutier-Fisher. 2011. The impact of daily mobility on exposure to traffic-related air pollution and health effect estimates. Journal of Exposure Science & Environmental Epidemiology 21 (1):42–48. doi: 10.1038/jes.2010.14.
   Web of Science ®Google Scholar
• Shafran-Nathan, R.,  Yuval, and D. Broday. 2018. Impacts of personal mobility and diurnal concentration variability on exposure misclassification to ambient pollutants. Environmental Science & Technology 52 (6):3520–26. doi: 10.1021/acs.est.7b05656.
   Web of Science ®Google Scholar
• Shafran-Nathan, R.,  Yuval, I. Levy, and D. Broday. 2017. Exposure estimation errors to nitrogen oxides on a population scale due to daytime activity away from home. Science of the Total Environment 580:1401–409. doi: 10.1016/j.scitotenv.2016.12.105.
   PubMed Web of Science ®Google Scholar
• Shareck, M., K. Frohlich, and Y. Kestens. 2014. Considering daily mobility for a more comprehensive understanding of contextual effects on social inequalities in health: A conceptual proposal. Health & Place 29:154–60. doi: 10.1016/j.healthplace.2014.07.007.
   PubMed Web of Science ®Google Scholar
• Shareck, M., Y. Kestens, and K. Frohlich. 2014. Moving beyond the residential neighborhood to explore social inequalities in exposure to area-level disadvantage: Results from the interdisciplinary study on inequalities in smoking. Social Science & Medicine 108:106–14. doi: 10.1016/j.socscimed.2014.02.044.
   PubMed Web of Science ®Google Scholar
• Singh, V., C. Carnevale, G. Finzi, E. Pisoni, and M. Volta. 2011. A cokriging based approach to reconstruct air pollution maps, processing measurement station concentrations and deterministic model simulations. Environmental Modelling & Software 26 (6):778–86. doi: 10.1016/j.envsoft.2010.11.014.
   Web of Science ®Google Scholar
• Tang, W., and D. Levinson. 2018. Deviation between actual and shortest travel time paths for commuters. Journal of Transportation Engineering, Part A: Systems 144 (8):04018042. doi: 10.1061/JTEPBS.0000161.
   Web of Science ®Google Scholar
• van Ham, M., D. Manley, N. Bailey, L. Simpson, and D. Maclennan. 2012. Neighbourhood effects research: New perspectives. In Neighbourhood effects research: New perspectives, ed. M. van Ham, D. Manley, N. Bailey, L. Simpson, and D. Maclennan, 1–21. Dordrecht, The Netherlands: Springer.
   Google Scholar
• Wang, J., and M.-P. Kwan. 2018. An analytical framework for integrating the spatiotemporal dynamics of environmental context and individual mobility in exposure assessment: A study on the relationship between food environment exposures and body weight. International Journal of Environmental Research and Public Health 15 (9):2022. doi: 10.3390/ijerph15092022.
   Web of Science ®Google Scholar
• Widener, M., L. Minaker, S. Farber, J. Allen, B. Vitali, P. Coleman, and B. Cook. 2017. How do changes in the daily food and transportation environments affect grocery store accessibility? Applied Geography 83:46–62. doi: 10.1016/j.apgeog.2017.03.018.
   Web of Science ®Google Scholar
• Widener, M., and J. Shannon. 2014. When are food deserts? Integrating time into research on food accessibility. Health & Place 30:1–3. doi: 10.1016/j.healthplace.2014.07.011.
   PubMed Web of Science ®Google Scholar
• Wilson, W. J. 1987. The truly disadvantaged: The inner city, the underclass, and public policy. Chicago: University of Chicago Press.
   Google Scholar
• Yoo, E., C. Rudra, M. Glasgow, and L. Mu. 2015. Geospatial estimation of individual exposure to air pollutants: Moving from static monitoring to activity-based dynamic exposure assessment. Annals of the Association of American Geographers 105 (5):915–26. doi: 10.1080/00045608.2015.1054253.
   Web of Science ®Google Scholar
• Yu, H., A. Russell, J. Mulholland, and Z. Huang. 2018. Using cell phone location to assess misclassification errors in air pollution exposure estimation. Environmental Pollution 233:261–66. doi: 10.1016/j.envpol.2017.10.077.
   PubMed Web of Science ®Google Scholar
• Yu, H., A. Russell, J. Mulholland, T. Odman, Y. Hu, H. Chang, and N. Kumar. 2018. Cross-comparison and evaluation of air pollution field estimation methods. Atmospheric Environment 179:49–60. doi: 10.1016/j.atmosenv.2018.01.045.
   Web of Science ®Google Scholar
• Zhao, P., M.-P. Kwan, and S. Zhou. 2018. The uncertain geographic context problem in the analysis of the relationships between obesity and the built environment in Guangzhou. International Journal of Environmental Research and Public Health 15 (2):308. doi: 10.3390/ijerph15020308.
   Web of Science ®Google Scholar
• Zhu, S., and D. Levinson. 2015. Do people use the shortest path? An empirical test of Wardrop’s first principle. PLoS ONE 10 (8):e0134322. doi: 10.1371/journal.pone.0134322.
   PubMed Web of Science ®Google Scholar