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 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".