Arc diagrams were conceptualized to map relationships along a linear sequence, a task recently pushed into physical form through projects like *Balaton Borders*. In research, these "arcs" represent ecological shifts and land-use transitions, turning abstract data into tangible boundaries that spark reflection on how human impact disrupts continuous landscapes. While this research emphasizes the sensory and reflective experience of data "edges," practitioners in fields like Digital Humanities typically use digital arc diagrams to visualize influence and citation networks among authors. By placing entities on a single line, they highlight complex clusters of interaction without the spatial clutter of a full 2D network graph.

However, the transition from research to standard practice often strips away the intentionality of the linear axis. In many implementations, the ordering of nodes is randomized or alphabetical, which obscures the sequential or spatial logic that makes an arc diagram valuable. Without a meaningful underlying sequence—such as time, geographic proximity, or professional seniority—the arcs become a visual tangle rather than a story of progression. To preserve the analytical value found in high-concept designs, practitioners should prioritize "axis integrity." If you are mapping author influence, order your nodes chronologically by their first publication. This allows the height and span of the arcs to reveal genuine patterns of long-range influence and historical shifts rather than haphazard connections.