MapTime: Software for Exploring Spatiotemporal Data Associated with Point Locations
Bibliographic record
Abstract
We introduce MapTime, a software package for exploring spatiotemporal data associated with point locations. Three basic exploration methods are available in Map-Time: animation, small multiples, and change maps. Animated maps can be presented either automatically (at a specified frame rate) or under user control (by dragging a scroll box along a scroll bar). We found the user-controlled approach most effective, but this and other Map-Time features ultimately need to be evaluated by map users. Potential research issues related to animation include developing a temporal legend that can facilitate understanding animations (a key problem is associating the correct dates with changes in the spatiotemporal pattern) and selecting an appropriate frame rate for the automatic display of various phenomena. Small multiples involve presenting multiple temporal elements simultaneously; they are thus useful for comparing individual temporal elements with one another. We argue that small multiples could be particularly useful as guided discovery tools through which students learn about physical geography principles by comparing temporal map elements with one another. Change maps are single, static maps that display the change over time in one of three forms: raw magnitude, percent, or rate of change. Using change maps as individual elements of a small multiple is particularly interesting, as they permit users to "see" changes that may not be apparent during an animation. A limitation of MapTime is that only proportional circles can be used to symbolize point data. This is problematic because users may have difficulty (1) in interpreting the correct relation between circle areas, (2) in associating these abstract symbols with particular phenomena, and (3) in associating the areas of these circles with point locations of phenomena. Therefore, MapTime should ultimately include a greater variety of point symbols (for example, squares, pictographs, and three-dimensional bars).
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.018 |
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".