BackgroundMost studies on the local food environment have used secondary sources to describe the food environment, such as government food registries or commercial listings (e.g., Reference USA). Most of the studies exploring evidence for validity of secondary retail food data have used on-site verification and have not conducted analysis by data source (e.g., sensitivity of Reference USA) or by food outlet type (e.g., sensitivity of Reference USA for convenience stores). Few studies have explored the food environment in American Indian communities. To advance the science on measuring the food environment, we conducted direct, on-site observations of a wide range of food outlets in multiple American Indian communities, without a list guiding the field observations, and then compared our findings to several types of secondary data.MethodsFood outlets located within seven State Designated Tribal Statistical Areas in North Carolina (NC) were gathered from online Yellow Pages, Reference USA, Dun & Bradstreet, local health departments, and the NC Department of Agriculture and Consumer Services. All TIGER/Line 2009 roads (>1,500 miles) were driven in six of the more rural tribal areas and, for the largest tribe, all roads in two of its cities were driven. Sensitivity, positive predictive value, concordance, and kappa statistics were calculated to compare secondary data sources to primary data.Results699 food outlets were identified during primary data collection. Match rate for primary data and secondary data differed by type of food outlet observed, with the highest match rates found for grocery stores (97%), general merchandise stores (96%), and restaurants (91%). Reference USA exhibited almost perfect sensitivity (0.89). Local health department data had substantial sensitivity (0.66) and was almost perfect when focusing only on restaurants (0.91). Positive predictive value was substantial for Reference USA (0.67) and moderate for local health department data (0.49). Evidence for validity was comparatively lower for Dun & Bradstreet, online Yellow Pages, and the NC Department of Agriculture.ConclusionsSecondary data sources both over- and under-represented the food environment; they were particularly problematic for identifying convenience stores and specialty markets. More attention is needed to improve the validity of existing data sources, especially for rural local food environments.

BackgroundPublished studies of geocoding accuracy often focus on a single geographic area, address source or vendor, do not adjust accuracy measures for address characteristics, and do not examine effects of inaccuracy on exposure measures. We addressed these issues in a Women's Health Initiative ancillary study, the Environmental Epidemiology of Arrhythmogenesis in WHI.ResultsAddresses in 49 U.S. states (n = 3,615) with established coordinates were geocoded by four vendors (A-D). There were important differences among vendors in address match rate (98%; 82%; 81%; 30%), concordance between established and vendor-assigned census tracts (85%; 88%; 87%; 98%) and distance between established and vendor-assigned coordinates (mean ρ [meters]: 1809; 748; 704; 228). Mean ρ was lowest among street-matched, complete, zip-coded, unedited and urban addresses, and addresses with North American Datum of 1983 or World Geodetic System of 1984 coordinates. In mixed models restricted to vendors with minimally acceptable match rates (A-C) and adjusted for address characteristics, within-address correlation, and among-vendor heteroscedasticity of ρ, differences in mean ρ were small for street-type matches (280; 268; 275), i.e. likely to bias results relying on them about equally for most applications. In contrast, differences between centroid-type matches were substantial in some vendor contrasts, but not others (5497; 4303; 4210) pinteraction < 10-4, i.e. more likely to bias results differently in many applications. The adjusted odds of an address match was higher for vendor A versus C (odds ratio = 66, 95% confidence interval: 47, 93), but not B versus C (OR = 1.1, 95% CI: 0.9, 1.3). That of census tract concordance was no higher for vendor A versus C (OR = 1.0, 95% CI: 0.9, 1.2) or B versus C (OR = 1.1, 95% CI: 0.9, 1.3). Misclassification of a related exposure measure – distance to the nearest highway – increased with mean ρ and in the absence of confounding, non-differential misclassification of this distance biased its hypothetical association with coronary heart disease mortality toward the null.ConclusionGeocoding error depends on measures used to evaluate it, address characteristics and vendor. Vendor selection presents a trade-off between potential for missing data and error in estimating spatially defined attributes. Informed selection is needed to control the trade-off and adjust analyses for its effects.

Programs for geospatial support at academic libraries have evolved over the past decade in response to changing campus needs and developing technologies. Geospatial applications have matured tremendously in this time, emerging from specialty tools to become broadly used across numerous disciplines. At many universities, the library has served as a central resource allowing students and fac ulty across academic departments access to GIS resources. Today, as many academic libraries evaluate their spaces and services, GIS and data services are central in discussions on how to further en gage with patrons and meet increasingly diverse researcher needs. As library programs evolve to support increasingly technical data and GIS needs, many universities are faced with similar challenges and opportunities. To explore these themes, data and GIS services librarians and GIS specialists from five universities-the

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