Graph database technologies have emerged as powerful platforms for managing spatio-temporal data, yet systematic understanding of their analytical capabilities remains limited. This review analyzes publications from 2019 to 2024 to examine how graph data structure enables spatio-temporal analytics. The study identifies five major analytical categories namely predictive, diagnostic, descriptive, prescriptive and exploratory spanning fourteen domains including transportation, maritime and oceanographic systems, energy, built environment, and healthcare and many others. While earlier works primarily focused on technical infrastructure such as graph database performance with spatio-temporal data and non-spatio-temporal graph data structure, systematic evaluation of their analytical applications has been lacking. Addressing this gap, this review synthesizes how graph data structure design and algorithm selection determine the range of spatio-temporal analyses possible across domains. Findings show that the urban disaster management domain demonstrates complete analytical coverage across all categories through event-evolutionary graphs, whereas temporal sequencing graphs exhibit strong transferability across domains. Overall, results highlight that the analytical value of graph databases for spatio-temporal data arises from the synergy between graph data structure design and domain requirements. These insights provide guidance for researchers and practitioners in selecting appropriate analytical approaches and identifying priorities for advancing spatio-temporal graph analytics.
Hairuddin et al. (2026) studied this question.