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1.
Am J Epidemiol ; 186(3): 265-273, 2017 Aug 01.
Artículo en Inglés | MEDLINE | ID: mdl-28899028

RESUMEN

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.


Asunto(s)
Ciudades , Características de la Residencia , Medio Social , Ciudades/estadística & datos numéricos , Recolección de Datos , Humanos , Michigan , Características de la Residencia/estadística & datos numéricos , Factores Socioeconómicos , Análisis Espacial
2.
J Maps ; 12(1): 53-60, 2016.
Artículo en Inglés | MEDLINE | ID: mdl-27482283

RESUMEN

Neighborhood physical disorder, or the deterioration of urban environments, is associated with negative mental and physical health outcomes. Eleven trained raters used CANVAS, a web-based system for conducting reliable virtual street audits, to collect data on nine indicators of physical disorder using Google Street View imagery of 532 block faces in New York City, New York, USA. We combined the block face indicator data into a disorder scale using item response theory; indicators ranged in severity from presence of litter, a weak indicator of disorder, to abandoned cars, a strong indicator. Using this scale, we estimated disorder at the center point of each sampled block. We then used ordinary kriging to interpolate estimates of disorder levels throughout the city. The resulting map condenses a complex estimation process into an interpretable visualization of the spatial distribution of physical disorder in New York City.

3.
Am J Epidemiol ; 180(6): 626-35, 2014 Sep 15.
Artículo en Inglés | MEDLINE | ID: mdl-25122584

RESUMEN

Neighborhood physical disorder is thought to affect mental and physical health, but it has been difficult to measure objectively and reliably across large geographical areas or multiple locales. Virtual street audits are a novel method for assessing neighborhood characteristics. We evaluated the ecometric properties of a neighborhood physical disorder measure constructed from virtual street audit data. Eleven trained auditors assessed 9 previously validated items developed to capture physical disorder (e.g., litter, graffiti, and abandoned buildings) on 1,826 block faces using Google Street View imagery (Google, Inc., Mountain View, California) dating from 2007-2011 in 4 US cities (San Jose, California; Detroit, Michigan; New York, New York; and Philadelphia, Pennsylvania). We constructed a 2-parameter item response theory scale to estimate latent levels of disorder on each block face and defined a function using kriging to estimate physical disorder levels, with confidence estimates, for any point in each city. The internal consistency reliability of the resulting scale was 0.93. The final measure of disorder was positively correlated with US Census data on unemployment and housing vacancy and negatively correlated with data on owner-occupied housing. These results suggest that neighborhood physical disorder can be measured reliably and validly using virtual audits, facilitating research on possible associations between physical disorder and health.


Asunto(s)
Ciudades/clasificación , Monitoreo del Ambiente/métodos , Monitoreo del Ambiente/estadística & datos numéricos , Características de la Residencia/clasificación , Interfaz Usuario-Computador , Ciudades/estadística & datos numéricos , Recolección de Datos , Reproducibilidad de los Resultados , Medio Social , Análisis Espacial , Estados Unidos , Salud Urbana/clasificación
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