Making Accessibility Visible: Visualizing Spatial Accessibility Through Multi-Dimensional Scaling Model
Bibliographic record
Abstract
Accessibility has progressively claimed a central role in policy discourse and planning in the Global South. However, availability of approaches for its assessment is still limited in practice. Multidimensional Scaling (MDS) is a statistical tool aimed at explaining relations of distances, such as the analyzed in accessibility, through the construction of a new space of projections. That way, results are easily visualized and interpreted. Our research explores the use of MDS for the visualization of spatial accessibility. Taking the cities of Pereira and Dosquebradas, which belong to the Metropolitan Area of the Centre-West (MACW) of Colombia, we calculate the shortest path from each intersection to the rest assuming trips in motorized and non-motorized transport modes. This approach allows to visually re-configure the spatial distribution of intersections in the transport network, graphically representing accessibility levels for different zones in the metropolitan region of analysis. The use of MDS enables a more intuitive interpretation of accessibility and the exploration of underlying factors that can influence spatial inequalities, as well as to visualize changes generated by different transport and land-use interventions. Results allow to visualize the configuration of the two municipalities in the metropolitan area in an easily interpretable fashion, identifying areas with limited accessibility and establishing comparisons between mode choices. The tool seeks to contribute to better-informing transport policy and accessibility appraisals and identifying potential spatial inequalities in relation to transport in urban areas, which was tested in various forums with local decision-makers and non-specialists in Colombia.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".