Visual Evaluation of Regional Tourism Attractiveness Based On Transportation: a GIS Quantitative Analysis Method
Bibliographic record
Abstract
Abstract The research in evaluating regional transportation tourism attractiveness has mainly based on qualitative or semi-quantitative analysis, and it has not been achieved quantitative and visual evaluation. The quantitative evaluation method of regional tourism attractiveness was established by employing the analytic hierarchy process and Delphi method in this paper. Moreover, the evaluation of tourism attractiveness was divided into four factors: natural condition index, tourism condition index, transportation condition index, and social economy index. The index weight was obtained by the judgment matrixes, and the weighted overlay analysis method using ArcGIS was adapted to study the coordinated development of tourism transportation. The results show that it was scientific and feasible to make a quantitative and visual tourism attractiveness evaluation based on the four aspects of natural condition, tourism condition, transportation condition, and social economy through the verification of a case study of the Great Jiuzhai Ring Tourist Area. The research revealed that the transportation system and the tourism system interacted with each other and had an excellent positive feedback mechanism, which could further promote the integrated development of transportation and tourism, and play theoretical and methodological guiding significance on regional tourism transportation network planning based on tourism resources.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".