Dataset Information

Assessing the built environment using omnidirectional imagery.

ABSTRACT: Observational audits commonly are used in public health research to collect data on built environment characteristics that affect health-related behaviors and outcomes, including physical activity and weight status. However, implementing in-person field audits can be expensive if observations are needed over large or geographically dispersed areas or at multiple points in time. A reliable and more efficient method for observational audits could facilitate extendibility (i.e., expanded geographic and temporal scope) and lead to more standardized assessment that strengthens the ability to compare results across different regions and studies. The purpose of the current study was to evaluate the degree of agreement between field audits and audits derived from interpretation of three types of omnidirectional imagery. Street segments from St. Louis MO and Indianapolis IN were stratified geographically to ensure representation of neighborhoods with different socioeconomic characteristics in both cities. Audits were conducted in 2008 and 2009 using four methods: field audits, and interpretation of archived imagery, new imagery, and Google Street View™ imagery. Agreement between field audits and image-based audits was assessed using observed agreement and the prevalence-adjusted bias-adjusted kappa statistic (PABAK). Data analysis was conducted in 2010. When measuring the agreement between field audits and audits from the different sources of imagery, the mean PABAK statistic for all items on the instrument was 0.78 (archived); 0.80 (new); and 0.81 (Street View imagery), indicating substantial to nearly perfect agreement among methods. It was determined that image-based audits represent a reliable method that can be used in place of field audits to measure several key characteristics of the built environment important to public health research.

SUBMITTER: Wilson JS

PROVIDER: S-EPMC3263366 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Assessing the built environment using omnidirectional imagery.

Wilson Jeffrey S JS Kelly Cheryl M CM Schootman Mario M Baker Elizabeth A EA Banerjee Aniruddha A Clennin Morgan M Miller Douglas K DK

American journal of preventive medicine 20120201 2

Observational audits commonly are used in public health research to collect data on built environment characteristics that affect health-related behaviors and outcomes, including physical activity and weight status. However, implementing in-person field audits can be expensive if observations are needed over large or geographically dispersed areas or at multiple points in time. A reliable and more efficient method for observational audits could facilitate extendibility (i.e., expanded geographic ...[more]

PMID: 22261217

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Project description:Background:The built environment has been shown to influence health in studies of disadvantaged populations using different measurement methods. This study determined whether environmental exposures derived from GigaPan® images could serve as valid predictors of body mass index (BMI), walking and moderate to vigorous physical activity (MVPA) in a longitudinal study of low-income adults living in two primarily African American neighborhoods in Pittsburgh, Pennsylvania, USA. GigaPan® is a robotic system used to obtain high-resolution, panoramic images of environments. Methods:Microscale environmental features along 481 streets were audited in 2015-2016 using an audit form. Environmental exposures were estimated for 731 adult participants, using a sample of street segments within a 0.4?km (0.25 mile) network distance from each participant's residential address. Summary environmental exposures were constructed using factor analysis. We tested associations between participant-level environmental exposures and objectively measured BMI, self-reported walking and objectively measured MVPA in regression models controlling for baseline health and demographic variables. Results:Three factors representing participants' environmental exposures were constructed: pedestrian bicycle-amenities; hilly-vacant-boarded; physical activity-recreation/low housing density. Environments with infrastructure and amenities supportive of walking and bicycling were associated with lower BMI (Coef.?=??0.47, p?=?0.02). Frequent walking was less likely in environments with more physical activity and recreation venues/low housing density (OR?=?0.81, 95% CI [0.67, 0.96]). MVPA was not associated with any of the environmental measures and the hilly-vacant-boarded factor was not associated with any of the outcomes. Conclusions:Predictive validity was demonstrated for an environmental exposure factor that captured features supportive of walking and cycling in a model predicting BMI, using built environment audit data from GigaPan® imagery. A complementary analysis found lower odds of frequent walking in the neighborhood among participants with exposure to more physical activity and recreational features, but fewer types and lower density of housing.

Dataset Information

Assessing the built environment using omnidirectional imagery.

Publications

Assessing the built environment using omnidirectional imagery.

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