We review recent advances in Object Oriented Spatial Statistics, a system of ideas,
algorithms and methods that allows the analysis of high dimensional and complex data …

Point processes are finding growing applications in numerous fields, such as neuroscience,
high frequency finance and social media. So classic problems of classification and …

Identifying structural differences among observed point patterns from several populations is
of interest in several applications. We use deep convolutional neural networks and employ a …

A spatial point pattern is a collection of points in space, representing, eg observed locations
of trees, bird nests, centers of cells in a histological sample, etc. When several independent …

Many spatiotemporal data record the time of birth and death of individuals, along with their
spatial trajectories during their lifetime, whether through continuous-time observations or …

Similarity of point patterns is critical to geographic information retrieval. Many methods
depend on measuring the similarity between point patterns within the spatial database …

We introduce the transport–transform and the relative transport–transform metrics between
finite point patterns on a general space, which provide a unified framework for earlier point …

In this paper, we propose to generalize the notion of depth in temporal point process
observations. The new depth is defined as a weighted product of two probability terms:(1) …

Despite the widespread occurrence of classification problems and the increasing collection
of point process data across many disciplines, study of error probability for point process …

This paper addresses the problem of comparing the spatial distribution of two point patterns.
A formal statistical test is proposed to decide whether two observed patterns share the same …