Using Geospatial Technologies for Assessing Accessibility of Urban Spaces for People with Motor Disabilities: Theoretical Framework of an Approach Centered on Users’ Perception
Bibliographic record
Abstract
The quality of life of people with disabilities strongly depends on their ability to access urban spaces and conduct their daily activities without any restriction. Unfortunately, there is a significant gap between traditional urban design and the way people with disabilities live in urban environments, which significantly limits their mobility and hence their social participation. In recent years, several governments and administrations have issued norms and guidelines that aim to ensure the construction of environments that are accessible and barrier-free in order to facilitate the mobility of these people. However, the goal and the means to improve mobility and quality of access to urban environments are still misunderstood by the public authorities and the actors involved. In order to help people with disabilities overcome the existing environmental barriers, we need to better understand how they perceive the accessibility of an urban environment while taking into account the heterogeneity of their profiles. In this paper, we present a theoretical framework of a new approach to assess accessibility of urban environments centered on users’ perception. To take into consideration the diversity of users’ profiles, the proposed framework combines the principles of the Disability Creation Process model and ‘Cognitive Design’. These two paradigms provide a solid background for the definition of experimental protocols for assessing the level of accessibility of urban spaces that may contain diverse obstacles and facilitators. In addition, this paper illustrates the importance of geospatial technologies for the implementation of such protocols.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".