The measurement of area-level attributes remains a major challenge in studies of neighborhood health effects. Even when neighborhood survey data are collected, they necessarily have incomplete spatial coverage. We investigated whether interpolation of neighborhood survey data was aided by information on spatial dependencies and supplementary data. Neighborhood "availability of healthy foods" was measured in a population-based survey of 5186 persons in Baltimore, New York, and Forsyth County (North Carolina). The following supplementary data were compiled from Census 2000 and InfoUSA, Inc.: distance to supermarkets, density of supermarkets and fruit and vegetable stores, housing density, distance to a high-income area, and percent of households that do not own a vehicle. We compared 4 interpolation models (ordinary least squares, residual kriging, spatial error regression, and thin-plate splines) using error statistics and Pearson correlation coefficients (r) from repeated replications of cross-validations. There was positive spatial autocorrelation in neighborhood availability of healthy foods (by site, Moran coefficient range = 0.10-0.28; all P < 0.0001). Prediction performances were generally similar for the evaluated models (r ≈ 0.35 for Baltimore and Forsyth; r ≈ 0.54 for New York). Supplementary data accounted for much of the spatial autocorrelation and, thus, spatial modeling was only advantageous when spatial correlation was at least moderate. A variety of interpolation techniques will likely need to be utilized in order to increase the data available for examining health effects of residential environments. The most appropriate method will vary depending on the construct of interest, availability of relevant supplementary data, and types of observed spatial patterns.Growing evidence supports the concept that residential neighborhoods play a role in determining individual behaviors that are linked to health outcomes. [1][2][3] However, there is a scarcity of relevant data for characterizing neighborhoods that continues to challenge researchers. One option for obtaining information on neighborhoods is to conduct surveys of residents who can provide information about their residential environment. However, survey data necessarily have incomplete spatial coverage. Routinely collected data such as census AHA designed the study, conducted all analyses, and drafted the manuscript. ADR provided survey data used in this study, assisted with planning analyses, and critically edited the manuscript. DGB assisted in designing the study, interpreted findings, and critically reviewed the manuscript. TER assisted with formulating statistical analyses and critically reviewed the manuscript. CAE critically reviewed the manuscript. Geologists and other natural scientists have used geostatistical methods to interpolate when there is incomplete spatial sampling 4-7 and have used supplementary data at colocated sites to try to improve predictions at the unsampled locations. 8,9 While spatial modeling in itself may pre...