A Robust Nonparametric Framework for Detecting Repeated Spatial Patterns

TL;DR

This paper introduces a nonparametric framework combining constrained clustering and MMD-based reassignment to detect repeated spatial patterns, addressing limitations of existing methods in spatial statistics.

Contribution

It presents a novel two-stage, nonparametric approach that robustly identifies spatially distant yet similar distributional patterns, with proven asymptotic consistency.

Findings

Demonstrates robustness across various spatial dependence scenarios

Shows effectiveness in detecting patterns in spatial proteomics data

Provides a flexible method for complex spatial datasets

Abstract

Identifying spatially contiguous clusters and repeated spatial patterns (RSP) characterized by similar underlying distributions that are spatially apart is a key challenge in modern spatial statistics. Existing constrained clustering methods enforce spatial contiguity but are limited in their ability to identify RSP. We propose a novel nonparametric framework that addresses this limitation by combining constrained clustering with a post-clustering reassigment step based on the maximum mean discrepancy (MMD) statistic. We employ a block permutation strategy within each cluster that preserves local attribute structure when approximating the null distribution of the MMD. We also show that the MMD$^2$ statistic is asymptotically consistent under second-order stationarity and spatial mixing conditions. This two-stage approach enables the detection of clusters that are both spatially distant and similar in distribution. Through simulation studies that vary spatial dependence, cluster sizes, shapes, and multivariate dimensionality, we demonstrate the robustness of our proposed framework in detecting RSP. We further illustrate its applicability through an analysis of spatial proteomics data from patients with triple-negative breast cancer. Overall, our framework presents a methodological advancement in spatial clustering, offering a flexible and robust solution for spatial datasets that exhibit repeated patterns.