Abstract—A high-dimensional representation is required to represent connected information that reflects real events and caters to spatio-temporal dimension. Graph data structures have shown potential for integration into smart city data management frameworks and have evolved to handle spatio-temporal data. To investigate the advanced techniques used in managing spatio-temporal data in graph databases, a systematic literature review of related research papers published from 2019 to 2024 was conducted. The review examines the evolution from basic graphs to specialized structures like dynamic attributed graphs and fuzzy spatio-temporal Resource Description Framework (RDF) and also summarizes algorithms used—including graph representation learning, pattern matching, clustering, and centrality algorithms—that enable sophisticated multi-domain analyses. The research provides five key contributions: (1) the state of graph data structure development and algorithms across various fields; (2) insights on spatio-temporal data inputs used in graph structures; (3) algorithms for spatio-temporal data management and analytics; (4) spatio-temporal analyses conducted using graph-structured databases; and (5) future research trajectories. From the review, we identify challenges in graph-based implementation with spatio-temporal data such as structural graph complexity, temporal representation, semantics, and data quality, while outlining future directions in graph representation techniques, temporal-semantic innovations, scalability solutions, and comprehensive data management.
 
Keywords—graph data structure, graph database, spatio-temporal data, algorithms, spatio-temporal analysis


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