@article{b8619bffbd7e4e258c8bf854e06c52a8,
title = "Street Audits to Measure Neighborhood Disorder: Virtual or In-Person?",
abstract = "Neighborhood conditions may influence a broad range of health indicators, including obesity, injury, and psychopathology. In particular, neighborhood physical disorder - a measure of urban deterioration - is thought to encourage crime and high-risk behaviors, leading to poor mental and physical health. In studies to assess neighborhood physical disorder, investigators typically rely on time-consuming and expensive in-person systematic neighborhood audits. We compared 2 audit-based measures of neighborhood physical disorder in the city of Detroit, Michigan: One used Google Street View imagery from 2009 and the other used an in-person survey conducted in 2008. Each measure used spatial interpolation to estimate disorder at unobserved locations. In total, the virtual audit required approximately 3% of the time required by the in-person audit. However, the final physical disorder measures were significantly positively correlated at census block centroids (r = 0.52), identified the same regions as highly disordered, and displayed comparable leave-one-out cross-validation accuracy. The measures resulted in very similar convergent validity characteristics (correlation coefficients within 0.03 of each other). The virtual audit-based physical disorder measure could substitute for the in-person one with little to no loss of precision. Virtual audits appear to be a viable and much less expensive alternative to in-person audits for assessing neighborhood conditions.",
keywords = "Detroit, Michigan, Google Street View, data collection, epidemiologic methods, social environment, spatial analysis, urban health",
author = "Mooney, {Stephen J.} and Bader, {Michael D.M.} and Lovasi, {Gina S.} and Teitler, {Julien O.} and Koenen, {Karestan C.} and Aiello, {Allison E.} and Sandro Galea and Emily Goldmann and Sheehan, {Daniel M.} and Rundle, {Andrew G.}",
note = "Funding Information: Author affiliations: Harborview Injury Prevention & Research Center, University of Washington, Seattle, Washington (Stephen J. Mooney); Department of Epidemiology, Dornsife School of Public Health, Drexel University, Philadelphia, Pennsylvania (Gina S. Lovasi); Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York (Daniel M. Sheehan, Andrew G. Rundle); Department of Sociology, American University, Washington, DC (Michael D. M. Bader); Center on Health, Risk, and Society, American University, Washington, DC (Michael D. M. Bader); School of Social Work, Columbia University, New York, New York (Julien O. Teitler); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts (Karestan C. Koenen); Department of Epidemiology, Gillings School of Public Health, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina (Allison E. Aiello); School of Public Health, Boston University, Boston, Massachusetts (Sandro Galea); Division of Social Epidemiology, New York University College of Global Health, New York, New York (Emily Goldmann). Funding for this project was provided by awards R21HD062965, P2CHD058486, and T32HD057822 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development and by R01DA022720 from the National Institute on Drug Abuse. Publisher Copyright: {\textcopyright} The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.",
year = "2017",
month = aug,
day = "1",
doi = "10.1093/aje/kwx004",
language = "English (US)",
volume = "186",
pages = "265--273",
journal = "American Journal of Epidemiology",
issn = "0002-9262",
publisher = "Oxford University Press",
number = "3",
}