Local Indicators of Mark Association for Spatial Marked Point Processes

TL;DR

This paper introduces local indicators of mark association (LIMA) functions for spatial marked point processes, which more accurately identify mark associations and variations than traditional global methods, across various real-world applications.

Contribution

The paper develops LIMA functions that effectively detect all types of mark associations in spatial marked point processes, overcoming limitations of existing global correlation measures.

Findings

LIMA functions outperform global mark correlation functions in simulations.

LIMA accurately identifies significant mark associations at specific interpoint distances.

Applications demonstrate LIMA's effectiveness in forestry, criminology, and urban mobility contexts.

Abstract

The emergence of distinct local mark behaviours is becoming increasingly common in the applications of spatial marked point processes. This dynamic highlights the limitations of existing global mark correlation functions in accurately identifying the true patterns of mark associations/variations among points as distinct mark behaviours might dominate one another, giving rise to an incomplete understanding of mark associations. In this paper, we introduce a family of local indicators of mark association (LIMA) functions for spatial marked point processes. These functions are defined on general state spaces and can include marks that are either real-valued or function-valued. Unlike global mark correlation functions, which are often distorted by the existence of distinct mark behaviours, LIMA functions reliably identify all types of mark associations and variations among points. Additionally, they accurately determine the interpoint distances where individual points show significant mark associations. Through simulation studies, featuring various scenarios, and four real applications in forestry, criminology, and urban mobility, we study spatial marked point processes in $\R^2$ and on linear networks with either real-valued or function-valued marks, demonstrating that LIMA functions significantly outperform the existing global mark correlation functions.