Ordinal Patterns Based Testing of Spatial Independence in Irregular Spatial Structures

TL;DR

This paper introduces a nonparametric, ordinal-pattern based test for spatial independence applicable to irregular spatial data, demonstrating robustness, accuracy, and power in various models, and extending previous methods from regular grids to general spatial supports.

Contribution

It develops a novel ordinal-pattern based test for spatial independence on irregular point clouds, with theoretical guarantees and practical validation, extending prior lattice-based approaches.

Findings

Test controls size at nominal level

Power increases with dependence strength

Detects linear and nonlinear spatial dependence

Abstract

We propose a nonparametric test of spatial independence for data observed on irregular, non-lattice point clouds $\mathcal{V}_{n}\subset\mathbb{R}^{2}$. For each location $v\in\mathcal{V}_{n}$, we encode the local spatial configuration through the ordinal pattern of the $m$ nearest-neighbour observations, obtaining a symbolic representation that is invariant under strictly monotone transformations and robust to outliers. Under the null hypothesis of spatial independence, the local ordinal patterns are i.i.d.\ and uniformly distributed over the symmetric group $\mathcal{S}_{m}$, regardless of the unknown marginal distribution $F$. We exploit this characterisation to construct a test statistic $L_{n}$ based on the additive log-ratio (ALR) transformation of the empirical ordinal-pattern frequencies. Invoking a central limit theorem for graph-dependent processes under a graph-based $\alpha$-mixing condition, we establish that $L_{n}$ converges in distribution to a $\chi^{2}_{m!-1}$ random variable, yielding an asymptotically pivotal procedure with no nuisance parameters. An extensive Monte Carlo study confirms that the $\chi^{2}_{m!-1}$ approximation is accurate already at moderate sample sizes, that the test controls size at the nominal level, and that power increases monotonically with the strength of spatial dependence. Notably, the test detects dependence in both linear and nonlinearly transformed spatial autoregressive models, illustrating the robustness that is characteristic of ordinal-pattern methods. Our framework extends the spatial ordinal-pattern testing paradigm from regular lattices to general spatial supports, opening the door to ordinal-pattern inference in the many applied settings where observations are irregularly located.