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 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.