Bibliographic record
Abstract
The challenge of measuring at municipal level tourism density has been a daunting task for both statisticians and geographers. The reason of this is enforced by the fact that administrative areas, such as municipalities, tend to be large spatial administrative units, sharing a large demographic asymmetry of tourist demand within the municipality. The rationale is that geographic characteristics such as coastal line, climate and vegetation, play a crucial role in tourist offer, leaning towards the conclusion that traditional census at administrative level are simply not enough to interpret the true distribution of tourism data. A more quantifiable method is necessary to assess the distribution of socio-economic data. This is developed by means of a dasymetric approach adding on the advantages of multi-temporal comparison. This paper adopts a dasymetric approach for defining tourism density per land use types using the CORINE Land Cover dataset. A density map for tourism is calculated, creating a modified areal weighting (MAW) approach to assess the distribution of tourism density per administrative municipality. This distribution is then assessed as a bidirectional layer on the land use datasets for two temporal stamps: 2000 and 2006, which leads to (i) a consistent map on a more accurate distribution of tourism in Algarve, (ii) the calculation of tourism density surfaces, and (iii) a multi-locational and temporal assessment through density cross-tabulation. Finally a geovisual interpretation of locational analysis of tourism change in Algarve for the last decade is created. This integrative spatial methodology offers unique characteristics for more accurate decision making at regional level, bringing an integrative methodology to the forefront of linking tourism with the spatio-temporal clusters formed in rapidly changing economic regions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".