Decision Factors to Walk from House to a Park in Housing Estate Projects: Case of Chiang Mai, Thailand
Bibliographic record
Abstract
Walking distance is an essential factor for the service design of any public park, including the park within the housing estate projects.This study aims to determine the resident's walking distance from the house to the nearest park through the artificial neural networks model.The model was formulated on 554 data sets collected from housing estate projects in Chiang Mai, Thailand.The dependent variable was the binomial variable which represented the potential to walk.In contrast, the independent variable is the distance from the house to the nearest and the personal data such as gender, age, and insurance.The study finds that individuals in various age ranges who earn an income exceeding 50,000 baht and age 35 years, a proportion of 50%, display a limited acceptable distance of no more than 445 meters, representing the minimum walking distance compared to other age groups.Hence, it is crucial to adopt a deliberate approach in designing park locations within housing estates to ensure that no more than 445 meters separate 50% of the residential area from the public park.The results provide a proper guideline for the service area planning of the park, which will support use efficiently for the housing estates' residents.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".