Filtering Spatial Point Patterns Using Kernel Densities.

ABSTRACT: Understanding spatial inhomogeneity and clustering in point patterns arises in many contexts, ranging from disease outbreak monitoring to analyzing radiologically-based emphysema in biomedical images. This can often involve classifying individual points as being part of a feature/cluster or as being part of a background noise process. Existing methods for this task can struggle when there are differences in the size and/or density of individual clusters. In this work, we propose employing kernel density estimates of the underlying point process intensity function, using an existing data-driven approach to bandwidth selection, to separate feature points from noise. This is achieved by constructing a null distribution, either through asymptotic properties or Monte Carlo simulation, and compa

SUBMITTER: Vestal BE 

PROVIDER: S-EPMC7781288 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature