Howard et al. BMC Public Health 2011, 11:68 http://www.biomedcentral.com/1471-2458/11/68 located in large or mid-size cities (based on U.S. Census Bureau classifications) and compared them to schools that were not in these areas, defined as non-urban. Another publicly available dataset was purchased from Environmental Systems Research Institute, Inc. (ESRI), It was used to construct variables for the presence/ absence of three classes of retailers near schools: 1) fast food restaurants, 2) convenience stores, and 3) super- markets. ESRI provided geocoded information for data that was first collected by the marketing firm InfoUSA, in 2007. We then selected retailers based initially on the following 8-digit National American Industry Classifica- tion System (NAICS) codes: Limited Service Restaurants (72221105), Convenience Stores (44512001), Supermar- kets and Grocery Stores (44511001). As reported in another study however [31], the NAICS codes were inconsistently applied. For instance, there were many recognizable convenience stores identified with the NAICS code for Grocer Retail (44511003). To reduce misclassification, retail locations in this category that contained the terms ‘Quick-,’ ‘Mini-,’ and ‘Liquor-’ in the company name were recoded into the “convenience stores” category (n = 668). Supermarkets were defined as retailers in the Supermarket and Grocery Stores cate- gory with $2 million or more in annual sales, based on the Food Marketing Institute’s definition [32]. Because Limited Service Restaurants (72221105) was also an overly broad category, those with five or more locations were selected as “fast food restaurants,” or business chains that provide low price meals without table ser- vice. This category included all of the major fast food chains (e.g. McDonald’s, Burger King, Taco Bell, Domi- no’s). For the entire state of California we identified 3,646 supermarkets, 4,069 convenience stores, and 20,668 fast food restaurants. GIS analysis The point locations of the schools were geocoded with the Streetmap USA (2006) dataset provided by ESRI, based on street addresses. We validated the accuracy of geocoded locations by spot-checking 10% of the schools. We found the addresses of these schools through web searches and re-geocoded them using Mapquest, an online geocoding service owned by America Online, Inc. [33]. Our spot-checking indicated more than 99% accu- racy in our geocoded school locations. Most previous studies of food proximity have not incorporated the actual pedestrian walking network into their analyses of proximity or food access. Instead they rely on Euclidean (straight-line, circular radius) buffers that do not account for the street network, sidewalks, and other elements of the areas surrounding schools where walking is most feasible [24,31,34,35]. The result is that the area covered by Euclidean buffers can be Page 3 of 8 substantially more than the area covered by equivalent distance network buffers. This can lead to erroneous or misleading results. To improve accuracy in this study we utilized network buffers along the actual street net- work for the entire state of California. We used ESRI’s Network Analyst extension to Arc- View 9.1 to create 800 m network buffers around the final geocoded school points. This distance was selected because it is approximately a ten-minute walk, and is commonly used in other studies of retail food access near schools [24,31,35,36]. These network buffers included both the street lines making up the 800 m street networks around each school, as well as polygons that contained the area encompassing these networked buffers. Once the network buffers were created, we per- formed a spatial join to calculate the number of fast food, convenience and supermarket retailers located within each of these network buffers. The final result of our GIS analysis was a matrix denoting the presence or absence of each of three classes of retailers within 800 m along the street network for each school. These data were exported out of the GIS for statistical analysis. Statistical analysis We analyzed all data with SPSS (version 15.0.1). Our analysis included descriptive statistics, correlations (Ken- dall’s tau-b), and linear regression. Our first model regressed the dependent variable of school rate of over- weight ninth grade students on the three types of nearby retailers assessed in the GIS analysis. Our second model included the additional school-level variables (ethnic, socioeconomic and gender composition, and urban/non- urban location) for comparison. Prior to regression, we applied logarithm transforma- tions to school ethnic composition variables, with the exceptions of the two largest groups (Hispanic/Latino and White, non-Hispanic), in order to meet assumptions of normality. We also employed multiple imputation as a strategy for dealing with missing data for two indepen- dent variables: percentage of students receiving free & reduced price meals, and urban/non-urban school loca- tion. This involved utilizing information from the exist- ing data to generate plausible values for missing data, while also representing uncertainty by generating five different data sets with imputed values. These values were imputed with the software Amelia II (version 1.2), and combined for analysis using the procedures detailed by King et al. [37] Results Descriptive statistics Table 1 shows the descriptive characteristics of the study. The sample consisted of 879 public schools in California with scores for 100 or more ninth grade