Relevance of the uMap Collaborative Platform as Support for Choropleth Mapping: A Traffic‒Light Statistical Signal Atlas of All-Cause Mortality—First French Lockdown (Preprint)
Bibliographic record
Abstract
BACKGROUND: The growing need for and interest in geomatics in the medical sector, as well as the pandemic crisis, led us to create a France-wide geomatics project aimed at producing several atlases of all-cause mortality at the municipal and submunicipal district levels via uMap France, a free and open-source collaborative map-sharing platform. In 2020, we decided to circumvent the obstacle of accessing detailed COVID-19 data by adopting a mortality-based approach to map the consequences of the crisis. OBJECTIVE: The aim of the uMap study was to provide a webmapping platform with original visualization and knowledge, as well as decision-making aids that complement existing information and are relevant to public and healthcare professionals. Our main hypotheses are as follows: 1- the medical sector could develop a private uMap platform dedicated to health; 2- interest in a municipal mortality atlas for France linked to the pandemic crisis will increase, even if it is produced after the pandemic; and 3- sharing the atlases with the uMap community will enhance their appeal and inspire the creation of similar atlases, owing to the new "experimental choropleth layer" recently developed by the uMap team. METHODS: This approach focuses on three main parts-data management (data collection, cleansing and scheduling) and geomatic engineering-through a two-step geomatic action plan to create atlases of the first lockdown period in France, displayed on the uMap platform. A logarithmically transformed variable allows us to obtain an immediate statistical signal of excess mortality or submortality via the Traffic-Light Atlas. RESULTS: The uMap Traffic-Light display provides instant statistical signals at a glance owing to the semantic interplay of colors. The atlas's double legends make it easy to compare specific regions (northeast, northwest, southeast, and southwest) to all of France. The atlas revealed excess mortality in 42% of the municipalities (14,503) out of 34,833. Thirty-five percent are in the green class (close to average to twice the average), 5% are in the orange class (2-4 times the average), and 2% are in the red class (4-11 times higher than average). CONCLUSIONS: We innovated, enriched, and reinforced the value of uMap for visual rendering by instantiating colored choropleth map atlases and double legends and showed its relevance to the healthcare sector. We focused on the Traffic-Light Atlas, which is the most relevant because of the instant message it conveys and its interpretability for all audiences. The uMap community can share our all-cause mortality atlases. A second version of the atlas encompassing four periods in 2020 and containing a minor error will be updated using either the "experimental choropleth layer" feature recently developed by the uMap team or, if this feature proves insufficient, the geomatic optimization process via the R-project. CLINICALTRIAL: Not applicable.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".