Nonparametric Estimation and Comparison of Distance Distributions from Censored Data

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.