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

2025-11-01

AI Summary

Existing surveys predominantly focus on single domains or model types, lacking interdisciplinary and systematic syntheses of spatiotemporal statistical models. Method: Guided by the PRISMA framework, we systematically identified and structurally analyzed 83 high-quality publications from 2021–2025 across epidemiology, ecology, public health, economics, and criminology. Contribution/Results: We propose a novel, unified classification framework for spatiotemporal models, revealing the ubiquity of hierarchical structures and additive spatiotemporal dependence. The analysis uncovers domain-specific modeling preferences and pronounced imbalances in research distribution. Critically, we find widespread deficiencies in reproducibility and methodological transparency—impeding cross-domain knowledge transfer. Our framework provides both a theoretical foundation and practical pathway for interdisciplinary model comparison, methodological borrowing, and principled model refinement.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Security and Privacy: Data transparency and provenance

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.

Problem

• Conducting systematic review of spatio-temporal statistical models
• Proposing classification scheme for spatio-temporal model structures
• Identifying limited reproducibility and cross-disciplinary applications

Innovation

• Systematic literature review following PRISMA guidelines
• Classification scheme for spatio-temporal model structures
• Hierarchical models with additive dependency components