Problem
Research questions and friction points this paper is trying to address.
spatial time series
spatial association
climate anomalies
temporal co-evolution
spatial dependence
Eliciting core spatial association from spatial time series: a random matrix approach
🤖 AI Summary
Traditional approaches struggle to disentangle temporal co-variation from genuine spatial dependence in climate spatiotemporal data. This study proposes a novel framework grounded in random matrix theory that, for the first time, integrates Hilbert space-filling curves with Bergsma’s statistical dependence measure to effectively isolate dominant temporal signals and accurately extract intrinsic spatial correlation structures. Applied to daily temperature range data across India from 1951 to 2022, the method successfully uncovers evolving spatial dependence patterns driven by topography, mesoscale climatic processes, and urbanization. The resulting approach offers a new tool for climate prediction and policy formulation, combining strong physical interpretability with methodological innovation.
📝 Abstract
Spatial time series (STS) data are fundamental to climate science, yet conventional approaches often conflate temporal co-evolution with genuine spatial dependence, obscuring subtle but critical climatic anomalies. We introduce a Random Matrix Theory (RMT)-based framework to isolate "core spatial association" by suitably trimming out strong but routine temporal signals while preserving spatial signals.
Our pipeline introduces Hilbert space filling curve technique and Bergsma's correlation measure of statistical dependence, to climate modelling. Applied to the diurnal temperature range (DTR) data of India (1951-2022), the method reveals distinct spatial anomalies shaped by topography, mesoclimate, and urbanization. The approach uncovers temporal evolution in spatial dependence and demonstrates how regional climate variability is structured by both physical geography and anthropogenic influences. Beyond the Indian application, the framework is broadly applicable to diverse spatio-temporal datasets, offering a robust statistical foundation for predictive modelling, resilience planning, and policy design in the context of accelerating climate change.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Random Matrix Theory
spatial time series
core spatial association
Hilbert space filling curve
Bergsma correlation
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