Interactivity and Cartography: A Contemporary Perspective on User Interface and User Experience Design from Geospatial Professionals
Bibliographic record
Abstract
This article reports on a semi-structured interview study with 21 geospatial professionals to provide a contemporary snapshot of expert opinion on the design and use of interactive maps and map-based systems (treated together as “cartographic interfaces”). Interview questions were based on key themes regarding interaction discussed within cartography and across the related fields of human-computer interaction, information visualization, usability engineering, and visual analytics, enabling a comparison of the current states of science and practice regarding user interface (UI) and user experience (UX) design in cartography. The results are organized according to five broad topics germane to UI/UX design in cartography: (1) the meaning of cartographic interaction in both research and practice (what?), (2) the purpose of cartographic interaction and the value it provides (why?), (3) the times when interaction positively supports work/play and therefore should be provided (when?), (4) the way in which user differences impact the success of the cartographic interaction (who?), and (5) the opportunities for or limitations on cartographic interaction imposed by the computing device supporting the interaction (where?). The interview study is significant for two reasons: first, it charts current trends in interactive mapping from the perspective of expert professionals, a population often missed in quantitative cartographic scholarship, and, second, it enables a reflection on future trends in UI/UX design in cartography, both those resulting from existing gaps between science and practice and those arising from emerging conceptual and technological developments.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".