Bibliographic record
Abstract
The volume and coverage of spatial data has increased dramatically in recent years, with Earth observation programmes producing dozens of GB of data on a daily basis. The term Big Spatial Data is now applied to data sets that impose real challenges to researchers and practitioners alike. The difficulties are partly related to a lack of tools supporting appropriate Coordinate Reference Systems (CRS). As rule, these data are provided in highly irregular geodesic grids, defined along equal intervals of latitude and longitude. Compounding the problem, users of such data end up taking geodesic coordinates in these grids as a Cartesian system, implicitly applying Marinus of Tyre's projection. A first approach towards the compactness of global geo-spatial data is to work in a Cartesian system produced by an equal-area projection. There are a good number to choose from, but those commonly supported by GIS software invariably relate to the sinusoidal or pseudo-cylindrical families, that impose important distortions of shape and distance. The land masses of Antarctica, Alaska, Canada, Greenland and Russia are particularly distorted with such projections. A more effective approach is to store and work with data in modern cartographic projections, in particular those defined with the Platonic and Archimedean solids. In spite of various attempts at open source software supporting these projections, in practice they remain today largely out of reach to GIS practitioners. This communication reviews persisting difficulties in working with worldwide big spatial data, current strategies to address such difficulties, the compromises they impose and the remaining gaps in open source software.
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.
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 | 0.052 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.014 | 0.033 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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".