As line datasets grow larger, the demand for effective visual data analysis becomes increasingly important. Understanding large‑scale datasets remains a fundamental challenge. A critical trade‑off is presented by existing line selection methods: they either produce efficiency, accuracy, or human interpretability, rarely achieving all three simultaneously. This gap is addressed by the development of human‑guided and context‑aware brushing techniques, which are supported by manual, semi‑automatic and automatic refinement methods. Through empirical evaluation via two user studies, it was found that, whilst context‑aware brushes offer theoretical promise, statistical superiority over conventional brushing approaches is not demonstrated. However, selection accuracy is consistently improved by refinement techniques, with manual refinement yielding the highest accuracy gains (12.6\%) followed by semi‑automatic refinement (9.8\%). Notably, efficiency gains from refinement remain dataset‑dependent, with no single technique universally dominating across varied data characteristics. Manual and semi‑automatic refinements are preferred by users seeking high‑accuracy improvements. Although similar efficiency scores are exhibited by manual and semi‑automatic refinements, the lowest variance is observed for the semi‑automatic method; consequently, it is recommended for users prioritising efficiency. The findings emphasise a fundamental design principle: Interpretability and user agency should be prioritised over full automation.

This research paper proposes a discrete agent-based model to simulate territorial development among micro-organisms. The model involves two species that interact through marker signals left behind by agents as they move through a three-dimensional lattice. The study builds on previous research that established a phase transition from a well-mixed to a well-segregated state for two-dimensional lattices. This research extends the finding to three dimensions and confirms that the properties observed in the two-dimensional model are also present in the three-dimensional model. We conclude that the addition of more mass or the ratio between gamma and lambda behaves similarly to the two-dimensional model. However, the three-dimensional model needs a larger mass to reach the same critical point as the two-dimensional one.