Bibliographic record
Abstract
When it comes to designing animated maps, the bottleneck is no longer the hardware, the software, or the data – it is the limited visual and cognitive processing capabilities of the map reader. Only sporadic progress has been made within GIScience in answering even the most basic questions: Under what conditions and for what kinds of map-reading tasks are animated maps effective, and how can their effectiveness be increased? Fortunately, over the past 20 years cognitive researchers in psychology and education have created a comprehensive set of theories that explain how people look at and learn from dynamic images, under what conditions these images work or fail, and why. Moreover, numerous controlled experiments, often designed to replicate and build upon previous studies, have validated these theories (something that is rare in cartography). This article presents a synthesis of this research and shows how it (1) directly informs mapping practices, (2) explains important cognitive differences between static and animated maps, (3) provides much-needed empirical support for emerging cartographic practices (where testing has yet to be done), and (4) generally confirms results from previous map studies. The article outlines solutions to split attention, retroactive inhibition, and cognitive overload. It also champions a perceptual–cognitive approach to cartography that would allow us to can explain why our designs work and not merely whether they work. Given the sizeable investment and number of animated maps in use today, such insights seem highly relevant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".