Urban availability of fast food stores and restaurants in the living environment of children is assumed to contribute to an obesogenic environment. In particular, it is hypothesized that food outlets and fast food restaurants are clustered in school-neighborhoods and that a higher spatial availability of unhealthy food results in increased consumption in children. However, previous findings strongly differ with regard to study design, main outcome variables, food store definitions, and measures of food availability [1–3]. For example, Powell et al.  found a positive association between the availability of convenience stores and body mass index (BMI) in a large sample of U.S. adolescents. Furthermore, a study based on ninth grade students from 879 public schools in California showed that the presence of a convenience store within a 10-minute walking distance of a school was associated with a higher rate of overweight students compared to schools without nearby convenience stores, but nearby fast food restaurants and supermarkets were not associated with the prevalence of overweight in schools .
However, another national study of U.S. adolescents showed that fast food availability was not associated with weekly frequency of fast food consumption in non-urban and low- or high-density urban areas . Investigating youths in 179 Canadian schools, Seliske et al. showed that the exposure to various types of food retailers in school neighborhoods was not associated with an increased likelihood of being overweight . For elementary school children in the U.S., Sturm and Datar also found no effects of food outlet density on changes in BMI at the neighborhood level .
The great heterogeneity in study designs, measures etc. limits a comparison of findings regarding the community food environment [9–11]. Furthermore, these findings can hardly be transferred to European communities, because structures of the studied communities differ in many aspects from European communities . For instance, communities in the U.S. and Europe differ with regard to urban patterns, location of stores, and range of products, as well as travel behavior and eating behavior of consumers . Portion size of fast food restaurants also strongly differs between the U.S. and Europe .
Because the community food environment in Europe is an understudied area [3, 9, 11], we conducted a pilot study of the food environment in one German study region of the IDEFICS study (Identification and prevention of dietary- and lifestyle-induced health effects in children and infants) . Our main objective was to adopt two methods: the K-function that was used to investigate clustering of food supply around schools and the density approach to assess the spatial availability of food supply at the community level.
Using the K-function, Austin et al. found that fast food restaurants in Chicago were significantly clustered in areas within a short walking distance from schools with about 3 to 4 times as many fast food restaurants within 1.5 km from schools than would be expected if the restaurants were randomly distributed throughout the city . Another study in New Zealand also found a significant spatial clustering of fast food outlets within a 1.5 km radius around schools . In these studies, the K-function [17, 18] compared the observed clustering of fast food restaurants to the expected clustering under the assumption of complete spatial randomness (CSR). However, the probability of the location of food stores is not the same throughout an entire study area. Thus, an inhomogeneous K-function is more appropriate for urban analyses [19, 20].
Density of food stores was measured differently to assess the availability of or the accessibility to food stores or fast food restaurants [9, 10]. Commonly used measures are, for example, simple density approaches, i.e. number per area [21, 22] or per capita , or kernel density approaches that give a weighting to stores or restaurants depending on the distance to the point of observation .
Using these methods, we first analyzed the spatial distribution of food stores around primary schools to investigate whether junk food choices are clustered around child-serving institutions. Second, we calculated food supply using the kernel density of food stores adjusted for residential density to eventually compare environmental food supply to dietary data assessed by Food Frequency Questionnaires (FFQ) and 24-hour dietary recalls (24-HDR) in the German study region of the IDEFICS study.