Developing geographic information systems platforms for multijurisdictional transportation analyses: framework and techniques for spatial data sharing
Bibliographic record
Abstract
Geographic information systems (GISs) have been presented as a powerful analysing tool for civil engineers to help their decision-making processes. Building GIS platforms for transportation analysis involving multiple jurisdictions has been challenging, however, because of the complexity and difficulty associated with conducting data sharing and ensuring spatial data interoperability among GISs for transportation (GIS-T) data sets. In the context of western Canadian urban and rural areas, this paper investigates the issues related to GIS-T data sharing, establishes a conceptual framework, develops techniques supporting the framework by solving recurring data-sharing problems, and constructs a number of GIS-T platforms facilitating comprehensive multijurisdictional transportation analyses. In addition, based on the knowledge gained through solving real-world problems, the authors propose an open GIS-T platform consisting of a series of customized base maps, each being tailored to suit the needs of individual application and, as a whole, linked together by interoperability to better support transportation applications.Key words: transportation engineering analysis, GIS, GIS-T, spatial data, interoperability, integration, data sharing.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".