Microscale Walkability Modelling. The Case of Athens City Centre
Bibliographic record
Abstract
To date, several macro-level walkability measures have been proposed, but microscale and audit-based walkability approaches prove to be highly effective to support realistic, quick and cheap mechanisms for pedestrian-friendly environments. Yet, walkability audits are time- and cost-intensive solutions, because they require several streetscape observations. This study aims to investigate whether a multiple linear regression model of urban form- and function-related variables can effectively predict an audit-based average walkability indicator. For this purpose, we use a virtual, brief and reliable audit tool (MAPS-Mini) in Athens city centre in order to collect street-level data and in turn to construct a microscale walkability indicator (dependent variable). Moreover, our approach suggests a flexible statistical model of open-source data, with six exploratory variables of the macro-level built environment: angular integration, population density, transit stop density, pedestrian street density, retail and entertainment activity density, and building height. The results indicate that audit-based average walkability scores can be effectively estimated, as the regression model can explain about 82% of the variation. Furthermore, the density of retail and entertainment activities was indicated as the strongest correlate of more walking-friendly streetscapes, while some urban policy implications include the promotion of footpath repairs and better-engineered crossings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".