Window View Access in Architecture: Spatial Visualization and Probability Evaluations Based on Human Vision Fields and Biophilia
Bibliographic record
Abstract
This paper presents a computational method for spatial visualization and probability evaluations of window view access in architecture based on human eyes’ vision fields and biophilic recommendations. Window view access establishes occupants’ visual connections to outdoors. Window view access has not, yet, been discussed in terms of the typical vision fields and related visual experiences. Occupants’ views of outdoors could change from almost blocked and poor to good, wide, and immersive visions in relation to the binocular focus to monocular (far-) peripheral sights of human eyes. The proposed methodological framework includes spatial visualizations and cumulative distribution functions of window view access based on visual experiences of occupants. The framework is integrated with biophilic recommendations and existing rating systems for view evaluations. As a pilot study, the method is used to evaluate occupants’ view access in a space designed with 15 different configurations of windows and overhangs. Results characterize likelihood of experiencing various field of views (FOVs) in case studies. In particular, window-to-wall-area ratios of between 40% and 70% offer optimum distributions of view access in space by offering 75% likelihoods of experiencing good to wide views and less than 25% probabilities of exposing to poor and almost blocked views. Results show the contribution of the proposed method to informative decision-making processes in architecture.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".