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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

Moran's I at Parcel, Face Block and Block Group Aggregation.
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pone-0020225-g002: Moran's I at Parcel, Face Block and Block Group Aggregation.

Mentions: Table 6 presents the percentage of geographic units with statistically significant spatial clustering at each aggregation level. The degree to which spatial clustering occurs—measured as a percentage of total geographic units–decreases considerably at higher levels of aggregation; an obvious consequence of aggregation. Figure 2 illustrates this observation by focusing on a small quadrant in a residential section of the neighborhood. This quadrant being analyzed is indicated in Figure 1. Spatial clustering of like values is indicated in black, while spatial clustering of dissimilar values is shown in grey. Un-shaded areas do not exhibit statistically significant spatial clustering. Observing only the block group level data, one would conclude that there is little spatial clustering in this section of the neighborhood, when in fact many of the parcels are spatially clustered. A similar pattern emerges throughout the neighborhood. The role of spatial clustering of observational data is very important from a policy perspective because it can provide guidance as to whether policy should tackle small concentrated areas of high concern or should be applied on a larger, less concentrated scale.


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

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

Moran's I at Parcel, Face Block and Block Group Aggregation.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0020225-g002: Moran's I at Parcel, Face Block and Block Group Aggregation.
Mentions: Table 6 presents the percentage of geographic units with statistically significant spatial clustering at each aggregation level. The degree to which spatial clustering occurs—measured as a percentage of total geographic units–decreases considerably at higher levels of aggregation; an obvious consequence of aggregation. Figure 2 illustrates this observation by focusing on a small quadrant in a residential section of the neighborhood. This quadrant being analyzed is indicated in Figure 1. Spatial clustering of like values is indicated in black, while spatial clustering of dissimilar values is shown in grey. Un-shaded areas do not exhibit statistically significant spatial clustering. Observing only the block group level data, one would conclude that there is little spatial clustering in this section of the neighborhood, when in fact many of the parcels are spatially clustered. A similar pattern emerges throughout the neighborhood. The role of spatial clustering of observational data is very important from a policy perspective because it can provide guidance as to whether policy should tackle small concentrated areas of high concern or should be applied on a larger, less concentrated scale.

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