An Automated Approach for Clipping Geographic Data before Projection that Maintains Data Integrity and Minimizes Distortion for Virtually Any Projection Method
Bibliographic record
Abstract
Selecting a map projection is key to minimizing distortion and thus clear communication of spatial data and accurate spatial analysis. Methods exist for selecting projections based on the intended area of use but not for finding polygons that can be used to clip geographic data to ensure the data are projected correctly and within desired distortion limits. The projection methods available in the Proj library were examined to determine the nature of the errors and distortions they created based on global data and a wide variety of available settings. Approaches were then identified for each projection including simple bounding boxes and more complex clipping polygons. To make sure that errors were not introduced into the projected data, data integrity polygons (DIPs) were created by placing a grid of cells over the Earth and then finding a cell near the origin that was within the specified criteria. Adjacent cells were added to the DIPs that met the criteria until no additional cells could be added. The criteria included projected cell sides could not intersect with themselves or other cells, the order of the cell corners could not be reversed, and distortion within the cell had to be within specified limits. I found that up to two DIPs with a limit on length distortion of a factor of 4 provided a general solution for all but three projection methods. Limitations included the time to find DIPs at high resolution. Clipping polygons and visualizations of the results were made available on a website.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.012 |
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".