GeoHexViz: A Python package for the visualizinghexagonally binned geospatial data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Geospatial visualization is often used in military operations research to convey analyses to both analysts and decision makers.For example, it has been used to help commanders coordinate units within a geographic region (Feibush et al., 2000), to depict how terrain impacts vehicle performance (Laskey et al., 2010), and inform training decisions in order to meet mission requirements (Goodrich et al., 2019).When such analyses include a large amount of point-like data, combining geospatial visualization and binning -in particular, hexagonal binning given its properties such as having the same number of neighbours as sides, the centre of each hexagon being equidistant from the centres of its neighbours, and that hexagons tile densely on curved surfaces (Carr et al., 1992;Sinha, 2019) -is an effective way to summarize and communicate the data.Recent examples in the military and public safety domains include assessing the impact of infrastructure on Arctic operations (Hunter et al., 2021) and communicating the spatial distribution of COVID-19 cases (Shaito & Elmasri, 2021) respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.011 | 0.004 |
| 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 it