Correlation Fractal Dimension Analysis Mountainous Traditional Village Settlement Spatial Form –Case Study of Qiandongnan in Guizhou
Bibliographic record
Abstract
Objective: A study carried out in the traditional villages of Qiandongnan in Guizhou province, located in China, to portray 409 traditional villages’ spatial distribution and fractal charicteristic, as well as its implications for the preservation of the group of traditional villages. Theoretical framework: From previous research, traditional village quantitative research on spatial area focuses on the statistics of mathematical models and spatial analyses of GIS, which indirectly reflect the complexity of the spatial patterns of traditional villages. Therefore, applying fractal theory to the spatial complexity of villages can provide a relatively objective and direct assessment for studying traditional villages with different formation backgrounds. Method: Data were collected in Google Earth map data and spatial data were obtained, which were synthesized and analyzed using spatial, statistical and mathematics analysis to verify the distribution factors and know about villages’ spatial fractal characteristics. Results and conclusion: The mountain settlements in Qiandongnan are concentrated in Leishan based on the Miao national group and “Liping-Congjiang-Rongjiang” based on the Dong national group. The q values of the explanatory power of each factor for the spatial distribution of traditional villages in the Qiandongnan area are, in descending order, intangible cultural heritage > GDP > distance from roads > height > the proportion of ethnic minorities > urbanization rate > average annual temperature > average annual precipitation > distance to rivers. Research implications: Villages contribute significantly to the enhancement of traditional Chinese culture, especially the preservation of historical buildings and distinctive local cultures. Furthermore, Knowing the factors of traditional village centralized contiguous protection mode and rural planning based on the policy of rural revitalization in China. Originality/value: Collected traditional villages’ distribution data from the official website and marked on the Google Earth map. Spatial analysis distribution of villages and emphasizes the importance of spatial correlation analysis in understanding the relationship between land use and rural population distribution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".