A Qualitative Evaluation of MapTime, A Program For Exploring Spatiotemporal Point Data
Bibliographic record
Abstract
The purpose of this paper is twofold: (1) to provide a user evaluation of MapTime, a software package for exploring spatiotemporal data associated with point locations, and (2) to examine some cognitive issues associated with the display of a dynamic geographic phenomenon - the change in population for cities over time. The methodology consists of a combination of individual interviews and focus groups conducted for three distinct groups of participants: novices, geography students, and domain experts. Some of the key findings are (1) that people do not naturally think of time lines in association with time (clocks and calendars are more common), which raises questions about the use of a linear time line for controlling animations; (2) that pictographic symbols tend to be preferred over geometric symbols for static maps, but pictographic symbols are apt to be too complex for animated maps; (3) that animations, small multiples, and change maps all have important roles to play in examining spatiotemporal data - animations for examining general trends, small multiples for comparing arbitrary time periods, and change maps for explicitly depicting change; (4) that automatic animations are useful for examining trends in pattern, while user-controlled animations are useful for focusing on details within a pattern; and (5) that individual interviews are particularly useful in obtaining users' reactions to software (as opposed to having them learn the software on their own) because the interviewer can steer the interview based on the user's responses.
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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 | 0.031 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".