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Systematic neighborhood observations at high spatial resolution: methodology and assessment of potential benefits.

Leonard TC, Caughy MO, Mays JK, Murdoch JC - PLoS ONE (2011)

Bottom Line: There is a growing body of public health research documenting how characteristics of neighborhoods are associated with differences in the health status of residents.In addition, we collected data on the health status of individuals residing in this neighborhood.Furthermore, these data facilitate a demonstration of the predictive accuracy of self-reported health status.

View Article: PubMed Central - PubMed

Affiliation: School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, Texas, United States of America. Leonard@utdallas.edu

ABSTRACT
There is a growing body of public health research documenting how characteristics of neighborhoods are associated with differences in the health status of residents. However, little is known about how the spatial resolution of neighborhood observational data or community audits affects the identification of neighborhood differences in health. We developed a systematic neighborhood observation instrument for collecting data at very high spatial resolution (we observe each parcel independently) and used it to collect data in a low-income minority neighborhood in Dallas, TX. In addition, we collected data on the health status of individuals residing in this neighborhood. We then assessed the inter-rater reliability of the instrument and compared the costs and benefits of using data at this high spatial resolution. Our instrument provides a reliable and cost-effect method for collecting neighborhood observational data at high spatial resolution, which then allows researchers to explore the impact of varying geographic aggregations. Furthermore, these data facilitate a demonstration of the predictive accuracy of self-reported health status. We find that ordered logit models of health status using observational data at different spatial resolution produce different results. This implies a need to analyze the variation in correlative relationships at different geographic resolutions when there is no solid theoretical rational for choosing a particular resolution. We argue that neighborhood data at high spatial resolution greatly facilitates the evaluation of alternative geographic specifications in studies of neighborhood and health.

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Related in: MedlinePlus

Comparison of Parcel Aesthetic and Block Group Average Aesthetic.
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Related In: Results  -  Collection


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pone-0020225-g001: Comparison of Parcel Aesthetic and Block Group Average Aesthetic.

Mentions: To further examine the impact of aggregation on the data, we compared the parcel values for Aesthetic to the block group average values of Aesthetic; results are displayed in Figure 1. Parcels shaded black have a block group average value for Aesthetic that differ by one or more standard deviations (one SD = 1.14) from the actual parcel value. Over 25% of the parcels are mischaracterized by the block group average value of Aesthetic by at least one standard deviation. This is important when considering causal relationships between individual outcomes and neighborhood. An individual may influence the level of Aesthetic of his/her own parcel, but may have little control over Aesthetic at the face block (or higher) level.


Systematic neighborhood observations at high spatial resolution: methodology and assessment of potential benefits.

Leonard TC, Caughy MO, Mays JK, Murdoch JC - PLoS ONE (2011)

Comparison of Parcel Aesthetic and Block Group Average Aesthetic.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3108600&req=5

pone-0020225-g001: Comparison of Parcel Aesthetic and Block Group Average Aesthetic.
Mentions: To further examine the impact of aggregation on the data, we compared the parcel values for Aesthetic to the block group average values of Aesthetic; results are displayed in Figure 1. Parcels shaded black have a block group average value for Aesthetic that differ by one or more standard deviations (one SD = 1.14) from the actual parcel value. Over 25% of the parcels are mischaracterized by the block group average value of Aesthetic by at least one standard deviation. This is important when considering causal relationships between individual outcomes and neighborhood. An individual may influence the level of Aesthetic of his/her own parcel, but may have little control over Aesthetic at the face block (or higher) level.

Bottom Line: There is a growing body of public health research documenting how characteristics of neighborhoods are associated with differences in the health status of residents.In addition, we collected data on the health status of individuals residing in this neighborhood.Furthermore, these data facilitate a demonstration of the predictive accuracy of self-reported health status.

View Article: PubMed Central - PubMed

Affiliation: School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, Texas, United States of America. Leonard@utdallas.edu

ABSTRACT
There is a growing body of public health research documenting how characteristics of neighborhoods are associated with differences in the health status of residents. However, little is known about how the spatial resolution of neighborhood observational data or community audits affects the identification of neighborhood differences in health. We developed a systematic neighborhood observation instrument for collecting data at very high spatial resolution (we observe each parcel independently) and used it to collect data in a low-income minority neighborhood in Dallas, TX. In addition, we collected data on the health status of individuals residing in this neighborhood. We then assessed the inter-rater reliability of the instrument and compared the costs and benefits of using data at this high spatial resolution. Our instrument provides a reliable and cost-effect method for collecting neighborhood observational data at high spatial resolution, which then allows researchers to explore the impact of varying geographic aggregations. Furthermore, these data facilitate a demonstration of the predictive accuracy of self-reported health status. We find that ordered logit models of health status using observational data at different spatial resolution produce different results. This implies a need to analyze the variation in correlative relationships at different geographic resolutions when there is no solid theoretical rational for choosing a particular resolution. We argue that neighborhood data at high spatial resolution greatly facilitates the evaluation of alternative geographic specifications in studies of neighborhood and health.

Show MeSH
Related in: MedlinePlus