Predicting Tourists' Accommodation Location Scores Using Spatial Machine Learning Techniques A Case Study of Middle Vancouver Island
Bibliographic record
Abstract
This study develops a predictive framework to optimize site selection for tourist accommodations - including hotels, motels, resorts, and guest houses (HMRG) - across the central and northern regions of Vancouver Island, aiming to reduce investor uncertainty through data-driven decision support. Unlike traditional models that focus on price prediction, this research emphasizes predicting location scores, a less explored yet highly relevant metric for assessing accommodation desirability. Despite a relatively small sample size, the framework offers promising insights for early-stage modeling in emerging markets. By integrating geospatial analytics and customer sentiment data, the study evaluates three techniques - Ordinary Least Squares Regression (OLSR), Random Forest (RF) regression, and Multilayer Perceptron (MLP) regression - to identify key determinants of location suitability. A four-phase methodology was employed: (1) variable selection and preprocessing, prioritizing tourism-relevant spatial features extracted from user-generated content and refined through spatial data engineering; (2) evaluation of predictor effect sizes, directional relationships, and multicollinearity; (3) iterative model optimization through feature engineering and hyperparameter tuning; and (4) comparative validation using robustness metrics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".