Literature DB >> 28899028

Street Audits to Measure Neighborhood Disorder: Virtual or In-Person?

Stephen J Mooney, Michael D M Bader, Gina S Lovasi, Julien O Teitler, Karestan C Koenen, Allison E Aiello, Sandro Galea, Emily Goldmann, Daniel M Sheehan, Andrew G Rundle.   

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.
© 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.

Entities:  

Keywords:  Detroit, Michigan; Google Street View; data collection; epidemiologic methods; social environment; spatial analysis; urban health

Mesh:

Year:  2017        PMID: 28899028      PMCID: PMC5860155          DOI: 10.1093/aje/kwx004

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  22 in total

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Review 4.  Built environments and obesity in disadvantaged populations.

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8.  Validity of an ecometric neighborhood physical disorder measure constructed by virtual street audit.

Authors:  Stephen J Mooney; Michael D M Bader; Gina S Lovasi; Kathryn M Neckerman; Julien O Teitler; Andrew G Rundle
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10.  Systematic social observation of children's neighborhoods using Google Street View: a reliable and cost-effective method.

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Authors:  Stephen J Mooney; Michael D M Bader; Gina S Lovasi; Julien O Teitler; Karestan C Koenen; Allison E Aiello; Sandro Galea; Emily Goldmann; Daniel M Sheehan; Andrew G Rundle
Journal:  Am J Epidemiol       Date:  2017-08-01       Impact factor: 4.897

3.  Neighborhood Physical Disorder and Adverse Pregnancy Outcomes among Women in Chicago: a Cross-Sectional Analysis of Electronic Health Record Data.

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6.  Invited Commentary: Observing Neighborhood Physical Disorder in an Age of Technological Innovation.

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Journal:  Am J Epidemiol       Date:  2017-08-01       Impact factor: 4.897

7.  Unique Views on Obesity-Related Behaviors and Environments: Research Using Still and Video Images.

Authors:  Jordan A Carlson; J Aaron Hipp; Jacqueline Kerr; Todd S Horowitz; David Berrigan
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8.  Analyzing Associations Between Chronic Disease Prevalence and Neighborhood Quality Through Google Street View Images.

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Journal:  Epidemiology       Date:  2020-03       Impact factor: 4.860

10.  Drop-And-Spin Virtual Neighborhood Auditing: Assessing Built Environment for Linkage to Health Studies.

Authors:  Jesse J Plascak; Andrew G Rundle; Riddhi A Babel; Adana A M Llanos; Celine M LaBelle; Antoinette M Stroup; Stephen J Mooney
Journal:  Am J Prev Med       Date:  2020-01       Impact factor: 6.604

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