Guidelines
Suggest edit

Use a moderate number of sample paths in static ensemble path displays

For point-location judgments in static uncertainty forecasts, prefer a moderate number of visible sample paths on ensemble path maps to improve fidelity and mitigate overweighting of a single overlapping path for novice viewers.

  • purpose:refine
  • basis:empirical
  • chart:map
  • data:geospatial
  • quality:fidelity
  • lever:encoding
  • operator:uncertainty
  • literacy:novice

advice

Increase displayed path count

Increase the number of visible sample paths until a single path overlap has less influence on location judgments, but stop before the display looks exhaustive. For example, displays with 17–33 visible paths produced less overlap bias than a 9-path display, while a denser 65-path display performed worse than 17–33 because viewers were more likely to read it as showing all possible paths.

reason

Why moderate path counts work

A sparse ensemble path display makes each visible line look more meaningful than it should. Adding more sample paths makes the display read more like a distribution and less like a set of individually decisive routes, but pushing density too high can make the display seem exhaustive.

Mechanism: Moderate path counts shift attention from any one visible line toward the overall distribution of paths, which reduces the tendency to overestimate risk when a location happens to sit on a displayed line.

Evidence: In static hurricane ensemble maps, the difference between judgments for points on a displayed line versus equally distant points off a displayed line was smaller with 17 and 33 paths than with 9 paths. A 65-path display still reduced the bias relative to 9 paths, but performed worse than 17 and 33 paths, and more viewers treated it as showing all possible paths (Padilla et al., 2020).

Notes: The best-performing count in these stimuli was about 30 paths, but the paper notes that the practical maximum can vary with spread, line thickness, and other display parameters.

context

Use when all are true

  • User Goal: Judge how much risk or damage a specific location faces.
  • Task: Read uncertainty from a static path ensemble rather than from an animation.
  • Data: Geospatial path uncertainty shown as multiple sampled lines.
  • Chart Setting: A single-image forecast display where viewers can see individual paths.
  • Audience: Novice or non-expert viewers with little prior instruction.
  • Success Criterion: Smaller judgment differences between equal-distance locations that differ only by touching a displayed line.

exceptions

Do not use when any are true

Break it when: The added path count makes the display look like the full set of possible outcomes or makes gaps in the distribution newly salient. Why: Very dense displays can restore misinterpretation by making viewers think the map shows all possible paths.

costs

Tradeoffs of increasing path count

Sacrifice: Individual paths become less separable as the display gets denser.
Risk: If density is pushed too far, viewers may infer that the display is exhaustive rather than sampled.
Mitigation: Increase path count from sparse to moderate, then stop short of a display that reads as the full set of outcomes.

mistakes

Common failure modes

  • Mistake: Leaving only a sparse handful of visible sample paths. Why it fails: Viewers give too much weight to whether one displayed line touches the location.
  • Mistake: Continuing to add paths until the map looks full. Why it fails: Viewers become more likely to think the display shows all possible paths.

check

How to test it

Failure Sign: A location on a displayed line is judged much riskier than an equally distant location off the line.
Quick Check: Create matched probe views where the target location stays at the same distance from the bundle center and only line overlap changes.
Stronger Test: Compare the on-line minus off-line judgment gap across candidate path counts and keep the count with the smaller gap.

fix

What to change

  • Increase a very sparse path display to a moderate count of visible sample paths.
  • Re-test the display with matched on-line and off-line probe locations after the change.
  • If the display now looks exhaustive, remove paths until viewers no longer read it as showing all possible outcomes.

References

Padilla, L. M. K., Creem-Regehr, S. H., & Thompson, W. (2020). The powerful influence of marks: Visual and knowledge-driven processing in hurricane track displays. Journal of Experimental Psychology: Applied, 26(1), 1–15. https://doi.org/10.1037/xap0000245