Smart Cartographic Functionality for Improving Data Visualization in Map Mashups
Bibliographic record
Abstract
Thanks to the growth of geoportal products and online cartographic platforms, access to spatial data has never been so easy for so many people. But access to cartographic knowledge for laypersons using such data is lagging behind. Platforms that allow users to create map mashups from diverse data sources can lead to unsatisfactory cartographic visualization, which reduces the map's legibility and the usefulness of such functions. This article's focus is on creating a framework that supports smart cartographic functions to improve the quality of map mashups. First, we assess the state of cartographic conflicts due to map mashup, using examples from existing geoportals. Afterwards, we describe a framework that allows us to define cartographic functions, focusing on symbology changes and based on a client-side approach. We do not aim to fully model the complex decision-making process of a professional cartographer, but rather to provide a set of smart functions that use appropriate assumptions and constraints based on cartographic principles and semantic information. As proof of concept, the framework and functions are then integrated within a geoportal.
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How this classification was reachedexpand
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".