Analysis on Spatial Structure of A-Grade Scenic Spots in China Based on Quantitative Geography Model
Bibliographic record
Abstract
The study of the spatial structure of tourism is receiving increasing attention but methodology so far has used qualitative rather than quantitative methods.Based on an investigation of 2 424 National A-grade tourist attractions and using GIS and some quantitative analysis methods,such as Nearest Neighbor Index(NNI),Gini Coefficient,quadrat analysis,hot spot clustering,and the spatial structure of scenic spots were investigated.Based on matrix raster data covering the whole space,this paper calculates spatial accessibility of all A-grade scenic spots in China using cost weighted distance method and ArcGIS as platforms.Service range of each scenic spot at 4A level and above in China was delimitated by using cost allocation method.The results show that the distribution of A-grade scenic spot in China is a type of agglomeration and spatial distribution equilibrium is low.Agglomeration of human scenic spots is higher than that of natural scenic spots,while the agglomeration of scenic spots at 4A level and above is less than that of scenic spots below 4A level.Service range of each scenic spot at 4A level and above in China was more advanced in south-eastern region than that in north-western region,whose spatial structure were closely related with traffic accessibility layout in China.First-order hotspots areas were mainly concentrated in the east side of the line formed by in Deqin-Alxa Left Banner.The second hotspots areas were composed of 11 region,while the third-order hot spots areas including Beijing,Tianjin,Central Plains and the Yangtze River Delta.This research can provide a new reference for tourist spatial structure study methodologically.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".