Insights from self-organizing maps for predicting accessibility demand for healthcare infrastructure
Bibliographic record
Abstract
As urban populations grow worldwide, it becomes increasingly important to critically analyse accessibility – the ease with which residents can reach key places or opportunities. The combination of ‘big data’ and advances in computational techniques such as machine learning (ML) could be a boon for urban accessibility studies, yet their application remains limited in this field. In this study, we aim to more robustly relate socio-economic factors to healthcare accessibility across a city experiencing rapid population growth, using a novel combination of clustering methods. We applied a powerful ML clustering tool, the self-organising map (SOM), in conjunction with principal component analysis (PCA), to examine how income shifts over time (2016–2022) could affect accessibility equity to healthcare for senior populations (65+ years) in the City of Surrey, Canada. We characterised accessibility levels to hospitals and walk-in clinics using door-to-door travel times, and combined this with high-resolution census data. Higher income clusters are projected to become more prevalent across the city over the study period, in some cases incurring into previously low income areas. However, low income clusters have on average much better accessibility to healthcare facilities than high income clusters, and their accessibility levels are projected to increase between 2016 and 2022. By attributing temporal differences through cross-term analysis, we show that population growth will be the biggest accessibility challenge in neighbourhoods with existing access to healthcare, whereas income change (both positive and negative) will be most challenging in poorly connected neighbourhoods. A dual accessibility problem may therefore arise in Surrey. First, large senior populations will reside in areas with access to numerous, and close-by, clinics, putting pressure on existing facilities for specialised services. Second, lower-income seniors will increasingly reside in areas poorly connected to healthcare services; since these populations are likely to be highly reliant on public transportation, accessibility equity may suffer. To our knowledge, this study is the first to apply a combination of PCA and SOM techniques in the context of urban accessibility, and it demonstrates the value of this clustering approach for drawing planning policy recommendations from large multivariate datasets.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".