ldmppr: Location Dependent Marked Point Processes in R

TL;DR

The paper introduces ldmppr, an R package for modeling, simulating, and evaluating location-dependent marked spatial point processes, addressing the limitation of independence assumptions in traditional models.

Contribution

It provides a practical framework and tools for generating and assessing dependent marked point processes in spatial data analysis.

Findings

Enables realistic simulation of location-dependent marks.

Facilitates goodness-of-fit assessment for spatial point process models.

Addresses limitations of independence assumptions in spatial modeling.

Abstract

In this article, we present $\textbf{ldmppr}$, an R package for estimating, evaluating, simulating from, and visualizing location-dependent marked spatial point processes. To date, it has commonly been assumed that the marks associated with a point process are independent of the locations. However, when dealing with many point processes, such as those arising in forestry applications, the independence assumption proves unreasonable. We introduce a practical framework for generating marked point processes with dependence between the marks and locations. We provide a brief discussion of the theory underpinning our modeling approach and outline the use of the package in a typical scenario involving real data. We highlight the functionality of the package for both generating from and assessing the goodness-of-fit of a given model, enabling users to generate realistic point patterns given a reference pattern or parameter values of interest.