• Interactivity as the core driver of visual analytics, emphasizing focusing/linking, user-driven exploration, and scalable interfaces that support knowledge discovery in high‑dimensional or distributed data [1], [2], [5], [16], [7].

• Graph and network visualization patterns emphasize analyzing structure and dynamics—large graphs, evolving digraphs, and readability via clustering and interactive inspection [4], [12], [11], [3].

• Visualization as knowledge management and decision-support: frameworks for knowledge representation, information retrieval tooling, strategic decision visualization, and business analytics representations [6], [19], [20], [17], [18].

• Movement/spatio-temporal data visualization emphasizes aggregation, space-time representations, and movement data analytics to support scalable exploration of dynamic processes [9], [16], [13], [8], [14].

• Empirical evaluation and readability studies underpin visualization methods, with experiments on cluster identification, path-preserving clustering readability, and steering/evaluation practices [10], [11], [15].