The preferential sampling of locations chosen to observe a spatio-temporal process has been identified as a major problem across multiple fields. Predictions of the process can be severely biased when standard statistical methodologies are applied to preferentially sampled data without adjustment. Detecting preferential sampling is currently a technically demanding task. As a result, the problem is often ignored in data analyses. This paper offers a general, intuitive, and computationally-fast solution. A novel approach for testing if a spatio-temporal dataset was preferentially sampled is presented. We refer to the test as a perceptron as it attempts to capture the numerous factors behind the human decision-making that selected the sampled locations and times. Importantly, the method can also help with the discovery of a set of informative covariates that can sufficiently control for the preferential sampling. The discovery of these covariates can justify the continued use of standard methodologies. A thorough simulation study is presented to demonstrate both the power and validity of the test in various data settings. The test is shown to attain high power for non-Gaussian data with sample sizes as low as 50. Finally, two previously-published case studies are revisited and new insights into the nature of the informative sampling are gained. The test can be implemented with the R package PStestR.

选择观察地点的时空过程的位置的优先采样已被确定为跨多个领域的主要问题。当将标准统计方法应用于优先采样的数据而不进行调整时，该过程的预测可能会严重偏差。目前，检测优先采样是一项技术要求很高的任务。结果，该问题通常在数据分析中被忽略。本文提供了一种通用，直观且计算速度快的解决方案。提出了一种测试时空数据集是否被优先采样的新颖方法。我们将测试称为感知器，因为它试图捕获选择了采样位置和时间的人类决策背后的众多因素。重要的，该方法还可以帮助发现可以充分控制优先采样的一组信息性协变量。这些协变量的发现可以证明继续使用标准方法是合理的。进行了全面的仿真研究，以证明在各种数据设置下测试的功能和有效性。结果表明，对于非高斯数据（样本量低至50），该测试具有很高的功效。最后，重新审视了两个先前发表的案例研究，并获得了对信息性抽样本质的新见解。该测试可以使用R包来实现 进行了全面的仿真研究，以证明在各种数据设置下测试的功能和有效性。结果表明，对于非高斯数据（样本量低至50），该测试具有很高的功效。最后，重新审视了两个先前发表的案例研究，并获得了对信息性抽样本质的新见解。该测试可以使用R包来实现 进行了全面的仿真研究，以证明在各种数据设置下测试的功能和有效性。结果表明，对于非高斯数据（样本量低至50），该测试具有很高的功效。最后，重新审视了两个先前发表的案例研究，并获得了对信息性抽样本质的新见解。该测试可以使用R包来实现PStestR。