A Virtual Globe Using a Discrete Global Grid System to Illustrate the Modifiable Areal Unit Problem
Bibliographic record
Abstract
In the same way that discrete global grid systems (DGGS) are used to index data on the spherical Earth, they can aggregate point data, with their spherical polygons serving as bins. DGGS are particularly useful at multiple map scales because they are spatially hierarchical and exist on the sphere or ellipsoid, allowing large or small scale binning without projection distortion. We use DGGS in a free and open-source pedagogical tool for teaching students about the modifiable areal unit problem (MAUP). Our software application uses Dutton’s quaternary triangular mesh (QTM) to bin global data points geodesically with counts or measures of any theme at multiple levels. Users can interactively select the level to which the data are binned by the QTM, as well as translate the whole tessellation east or west so that points fall into and out of different bins. These two functions illustrate the scaling and zoning aspects of the MAUP with dynamically-drawn choropleths on the surface of a virtual globe that the user can zoom and rotate, allowing visualization at virtually any cartographic scale. Users may also select various quantile classifications to further explore issues in visualizing aggregate data. In addition to presenting this new tool, we highlight the importance, especially at smaller scales, of using geodesic point-in-polygon intersection detection, rather than the projected 2D methods typically used by geographic information systems.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".