Fast-food outlets play a significant role in the nutrition of British children who get more food from such shops than the school canteen. To reduce young people’s access to fast-food meals during the school day, many British cities are implementing zoning policies. For instance, cities can create buffers around schools, and some have used 200 meters buffers while others used 400 meters. But how close is too close? Using the road network is needed to precisely computing the distance between fast-food outlets (for policies limiting the concentration), or fast-food outlets and the closest school (for policies using buffers). This estimates how much of the fast-food landscape could be affected by a policy, and complementary analyses of food utilization can later translate the estimate into changes on childhood nutrition and obesity. Network analyses of retail and urban forms are typically limited to the scale of a city. However, to design national zoning policies, we need to perform this analysis at a national scale. Our study is the first to perform a nation-wide analysis, by linking large datasets (e.g., all roads, fast-food outlets and schools) and performing the analysis over a high performance computing cluster. We found a strong spatial clustering of fast-food outlets (with 80% of outlets being within 120 of another outlet), but much less clustering for schools. Results depend on whether we use the road network on the Euclidean distance (i.e. ‘as the crow flies’): for instance, half of the fast-food outlets are found within 240 m of a school using an Euclidean distance, but only one-third at the same distance with the road network. Our findings are consistent across levels of deprivation, which is important to set equitable national policies. In line with previous studies (at the city scale rather than national scale), we also examined the relation between centrality and outlets, as a potential target for policies, but we found no correlation when using closeness or betweenness centrality with either the Spearman or Pearson correlation methods.

中文翻译：

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使用大规模网络分析来捕获英格兰的快餐业格局。

快餐店在英国儿童的营养中起着重要作用，英国儿童从此类商店获得的食物比学校食堂更多。为了减少年轻人在上学期间获得快餐的机会，许多英国城市正在实施分区政策。例如，城市可以在学校周围创建缓冲区，有些使用200米的缓冲区，而另一些使用400米的缓冲区。但是太近了太近了吗？需要使用道路网络来精确计算快餐店（用于限制集中度的政策）或快餐店与最近的学校（用于使用缓冲区的政策）之间的距离。这估计了一项政策可能会影响多少快餐业，并且对食物利用的补充分析可以在以后将估计值转化为儿童营养和肥胖症的变化。零售和城市形式的网络分析通常仅限于城市规模。但是，要设计国家分区政策，我们需要在全国范围内进行此分析。我们的研究是第一个通过链接大型数据集（例如，所有道路，快餐店和学校）并在高性能计算集群上进行分析来进行全国分析的研究。我们发现快餐店在空间上有很强的集群性（其中80％的门店位于另一个网点的120个之内），而学校的集群则少得多。结果取决于我们是否在欧氏距离上使用道路网络（即“乌鸦飞翔”）：例如，使用欧氏距离在学校的240 m内发现了一半的快餐店，但只有一个-第三，与路网距离相同。我们的研究结果在贫困程度上是一致的，这对于制定公平的国家政策很重要。与先前的研究（在城市规模而不是国家规模）相一致，我们还研究了集中度和出口之间的关系，将其作为政策的潜在目标，但我们发现将亲密关系或中间性中心与Spearman或Pearson都没有关联相关方法。