Transportation distance information is a powerful resource, but location records are often censored due to privacy concerns or regulatory mandates. We consider the problem of transportation event distance distribution reconstruction, which aims to handle this obstacle and has applications to public health informatics, logistics, and more. We propose numerical methods to approximate, sample from, and compare distributions of distances between censored location pairs. We validate empirically and demonstrate applicability to practical geospatial data analysis tasks. Our code is available on GitHub.

翻译：运输距离信息是一种强大的资源，但位置记录常因隐私顾虑或监管要求而被删失。我们考虑运输事件距离分布重建问题，该问题旨在克服这一障碍，并应用于公共卫生信息学、物流等领域。我们提出数值方法来近似、采样并比较删失位置对之间的距离分布。我们通过实证验证并展示了其在实用地理空间数据分析任务中的适用性。我们的代码已在GitHub上公开。