The statistical methodology for the analysis of replicated spatial point patterns in complex designs such as those including replications is fairly undeveloped. A mixed model is developed in conjunction with maximum pseudolikelihood and generalized linear mixed modeling by extending Baddeley and Turner's (2000, Australian and New Zealand Journal of Statistics 42, 283-322) work on pseudolikeliho...

In this article I present a reproducible review of recent research to investigate the spatial positioning of neurons in the nervous system. In particular, I focus on the relative spatial positioning of pairs of cell types within the retina. I examine three different cases by which two types of neurons might be arranged relative to each other. (1) Cells of different type might be effectively ind...

There is considerable interest in cell biology in determining whether, and to what extent, the spatial arrangement of nuclear objects affects nuclear function. A common approach to address this issue involves analyzing a collection of images produced using some form of fluorescence microscopy. We assume that these images have been successfully pre-processed and a spatial point pattern represent...

spatstat is a package for analyzing spatial point pattern data. Its functionality includes exploratory data analysis, model-fitting, and simulation. It is designed to handle realistic datasets, including inhomogeneous point patterns, spatial sampling regions of arbitrary shape, extra covariate data, and ‘marks’ attached to the points of the point pattern. A unique feature of spatstat is its gen...

We address the problem of analyzing spatial correlation between event types in large point data sets. Collocation rules are unsatisfactory, when confidence is not a sufficiently accurate interestingness measure, and Monte Carlo testing is infeasible, when the number of event types is large. We introduce an algorithm for mining correlation patterns, based on a non-parametric bootstrap test that,...