An Operation-Based Communication of Spatial Data Quality
Bibliographic record
Abstract
Improving users' awareness of the imperfections in spatial data has been a research issue explored within Geographic Information Sciences (GIS) for more than 30 years. However, little practical progress has been made toward this objective. Currently, most spatial data producers document information about spatial data quality as part of metadata. However, this information remains largely ignored by GIS users, which leads to the risk of users making poor decisions based on spatial data. As users increasingly make use of various GIS functionalities, GIS still lack the necessary mechanisms to effectively warn the users of the existence of quality issues in the spatial data being used. This becomes even more problematic in a web environment where data and services of unknown qualities can be shared and combined in the same application. In this paper we present an approach which aims at improving the use of quality information by providing it to the users in a more efficient way than existing approaches used for consulting metadata. We use an operation-based approach to link data quality information to the individual operations used in GIS applications. A conceptual framework for associating quality information with GIS operations is presented. Then, a prototype implementing this concept into a GIS software is described and discussed.
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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".