Using the k-Means Clustering Algorithm to Classify Features for Choropleth Maps
Bibliographic record
Abstract
Common methods for classifying choropleth map features typically form classes based on a single feature attribute. This technical note reviews the use of the k -means clustering algorithm to perform feature classification using multiple feature attributes. The k -means clustering algorithm is described and compared to other common classification methods, and two examples of choropleth maps prepared using k -means clustering are provided. Abstract: Les methodes courantes de classification des entites des cartes choroplethes forment habituellement des classes basees sur un seul attribut d’entite. Cette note technique passe en revue l’utilisation de l’algorithme de classification automatique a k -moyenne pour classer les entites au moyen d’attributs d’entites multiples. L’auteur decrit l’algorithme de classification automatique a k -moyenne, le compare a d’autres methodes de classification courantes et fournit deux exemples de cartes choroplethes preparees par classification automatique a k moyenne.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".