In the era of big data analysis, it is of interest to develop diagnostic tools for preliminary scanning of large spatial databases. One problem is identification of locations where certain characteristics exceed a given norm, e.g. timber volume or mean tree diameter exceeding a user-defined threshold. Some of the challenges are, large size of the database, randomness, complex shape of the spatial mean surface, heterogeneity and others. In a step-by-step procedure, we propose a method for achieving this for large spatial data sets. For illustration, we work through a simulated spatial data set as well as a forest inventory data set from Alaska (source: USDA Forest Services). Working within the framework of nonparametric regression modeling, the proposed method can attain a high degree of flexibility regarding the shape of the spatial mean surface. Taking advantage of the large sample size, we also provide asymptotic formulas that are easy to implement in any statistical software.

在大数据分析时代，开发用于大型空间数据库的初步扫描的诊断工具非常重要。一个问题是识别某些特征超出给定标准的位置，例如木材体积或平均树木直径超过用户定义的阈值。挑战包括数据库规模大，随机性，空间平均表面的复杂形状，异质性等。在分步过程中，我们提出了一种针对大型空间数据集实现此目标的方法。为了说明，我们研究了模拟的空间数据集以及阿拉斯加的森林清单数据集（来源：USDA Forest Services）。在非参数回归建模的框架内工作，所提出的方法可以在空间平均曲面的形状方面获得高度的灵活性。