MapTime: Software for Exploring Spatiotemporal Data Associated with Point Locations
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it