ArticleAmerican journal of epidemiology2017
Street Audits to Measure Neighborhood Disorder: Virtual or In-Person?
Article in American journal of epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Neighborhood Physical Disinvestment and Incident Diabetes between visits 1 and 2 of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).Journal of urban health : bulletin of the New York Academy of Medicine · 2026Article
- Neighborhood disorder and ovarian cancer survival in Black women.American journal of epidemiology · 2025Article
- Built Environment Change over Time Using Google Street View Assessments of Hispanic Community Health Study/Study of Latinos (HCHS/SOL) Cities.Journal of urban health : bulletin of the New York Academy of Medicine · 2025Article
- Virtual Assessment of Physical Activity-Related Built Environment in Soweto, South Africa: What Is the Role of Contextual Familiarity?Journal of urban health : bulletin of the New York Academy of Medicine · 2024Article
- The Lot Assessment of Neighborhood Disorder Virtual Audit Tool: A Valid and Reliable Five-Item Physical Neighborhood Disorder Measure.Translational issues in psychological science · 2024Article
- Lessons Learned from a Citywide Abandoned Housing Experiment.Journal of the American Planning Association. American Planning Association · 2024Article
- Neighborhood conditions in a Swedish context-Two studies of reliability and validity of virtual systematic social observation using Google Street View.Frontiers in psychology · 2023Article
- Measuring changes in neighborhood disorder using Google Street View longitudinal imagery: a feasibility study.Cities & health · 2023Article
- Validating a spatio-temporal model of observed neighborhood physical disorder.Spatial and spatio-temporal epidemiology · 2022Article
- Validation of desk-based audits using Google Street ViewInternational journal of health geographics · 2022Article
- Machine Learning Approaches for Measuring Neighborhood Environments in Epidemiologic Studies.Current epidemiology reports · 2022Review
- Associations between neighborhood disinvestment and breast cancer outcomes within a populous state registry.Cancer · 2022Article
- Examining the Built Environment for Healthy Living via Virtual Street Audits.Environmental health insights · 2022Article
- Predicting Perceptions of the Built Environment using GIS, Satellite and Street View Image Approaches.Landscape and urban planning · 2021Article
- Google street view image availability in the Bronx and San Diego, 2007-2020: Understanding potential biases in virtual audits of urban built environments.Health & place · 2021Article
- Human Mobility and Crime: Theoretical Approaches and Novel Data Collection Strategies.Annual review of criminology · 2021Article
- Did the UK COVID-19 Lockdown Modify the Influence of Neighbourhood Disorder on Psychological Distress? Evidence From a Prospective Cohort Study.Frontiers in psychiatry · 2021Article
- Using universal kriging to improve neighborhood physical disorder measurement.Sociological methods & research · 2020Article
- Spatial predictive properties of built environment characteristics assessed by drop-and-spin virtual neighborhood auditing.International journal of health geographics · 2020Article
- Development and Validation of a Google Street View Pedestrian Safety Audit Tool.Epidemiology (Cambridge, Mass.) · 2020Article
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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.
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