If a map is constructed through prediction with a statistical or non-statistical model, the sampling design used for selecting the sample on which the model is fitted plays a key role in the final map accuracy. Several sampling designs are available for selecting these calibration samples. Commonly, sampling designs for mapping are compared in real-world case studies by selecting just one sample for each of the sampling designs under study. In this study, we show that sampling designs for mapping are better compared on the basis of the distribution of the map quality indices over repeated selection of the calibration sample. In practice this is only feasible by subsampling a large dataset representing the population of interest, or by selecting calibration samples from a map depicting the study variable. This is illustrated with two real-world case studies. In the first case study a quantitative variable, soil organic carbon, is mapped by kriging with an external drift in France, whereas in the second case a categorical variable, land cover, is mapped by random forest in a region in France. The performance of two sampling designs for mapping are compared: simple random sampling and conditioned Latin hypercube sampling, at various sample sizes. We show that in both case studies the sampling distributions of map quality indices obtained with the two sampling design types, for a given sample size, show large variation and largely overlap. This shows that when comparing sampling designs for mapping on the basis of a single sample selected per design, there is a serious risk of an incidental result. Highlights: We provide a method to compare sampling designs for mapping. Random designs for selecting calibration samples should be compared on the basis of the sampling distribution of the map quality indices.

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如何比较用于映射的抽样设计？

如果地图是通过统计或非统计模型预测构建的，那么用于选择拟合模型的样本的抽样设计对最终地图的准确性起着关键作用。有多种抽样设计可用于选择这些校准样品。通常，在实际案例研究中比较映射的抽样设计，方法是为所研究的每个抽样设计仅选择一个样本。在这项研究中，我们表明，根据地图质量指数在重复选择校准样本时的分布情况，可以更好地比较用于绘图的抽样设计。在实践中，这只能通过对表示感兴趣的总体的大型数据集进行二次采样，或通过从描绘研究变量的地图中选择校准样本来实现。这通过两个真实世界的案例研究来说明。在第一个案例研究中，定量变量土壤有机碳通过克里金法和法国的外部漂移绘制，而在第二个案例中，分类变量土地覆盖由法国某个地区的随机森林绘制。比较了两种映射抽样设计的性能：简单随机抽样和条件拉丁超立方抽样，在不同的样本量下。我们表明，在这两个案例研究中，对于给定的样本量，使用两种抽样设计类型获得的地图质量指数的抽样分布表现出很大的变化和很大程度的重叠。这表明，当基于每个设计选择的单个样本比较用于映射的抽样设计时，存在偶然结果的严重风险。强调：我们提供了一种方法来比较映射的抽样设计。选择校准样本的随机设计应根据地图质量指标的抽样分布进行比较。