A Systematic Review of Spatio-Temporal Statistical Models: Theory, Structure, and Applications

TL;DR

This systematic review comprehensively analyzes recent spatio-temporal statistical models across disciplines, classifying their structures, applications, and highlighting gaps in reproducibility and cross-domain transfer.

Contribution

It introduces a classification scheme for spatio-temporal models, reviews recent literature across fields, and identifies key trends, gaps, and opportunities for improving transparency and interdisciplinary research.

Findings

Hierarchical models are most frequently used.

Most models incorporate additive components for dependencies.

Research is concentrated in few disciplines with limited reproducibility.

Abstract

Data with spatial-temporal attributes are prevalent across many research fields, and statistical models for analyzing spatio-temporal relationships are widely used. Existing reviews focus either on specific domains or model types, creating a gap in comprehensive, cross-disciplinary overviews. To address this, we conducted a systematic literature review following the PRISMA guidelines, searched two databases for the years 2021-2025, and identified 83 publications that met our criteria. We propose a classification scheme for spatio-temporal model structures and highlight their application in the most common fields: epidemiology, ecology, public health, economics, and criminology. Although tasks vary by domain, many models share similarities. We found that hierarchical models are the most frequently used, and most models incorporate additive components to account for spatial-temporal dependencies. The preferred model structures differ among fields of application. We also observe that research efforts are concentrated in only a few specific disciplines, despite the broader relevance of spatio-temporal data. Furthermore, we notice that reproducibility remains limited. Our review, therefore, not only offers inspiration for comparing model structures in an interdisciplinary manner but also highlights opportunities for greater transparency, accessibility, and cross-domain knowledge transfer.