Transportation distance information is a powerful resource, but location records are often censored due to privacy concerns or regulatory mandates. We suggest numerical methods to approximate, sample from, and compare distributions of distances between censored location pairs, a task with applications to public health informatics, logistics, and more. We validate empirically via simulation and demonstrate applicability to practical geospatial data analysis tasks. Our code is available on GitHub.

翻译：交通距离信息是一种强大的资源，但位置记录常因隐私问题或法规要求而被删失。我们提出了数值方法，用于逼近、采样并比较删失位置对之间的距离分布，该任务在公共卫生信息学、物流等领域具有应用价值。通过模拟实验进行实证验证，并展示了在实际地理空间数据分析任务中的适用性。我们的代码已发布于GitHub。