How does the spatial scale impact accessibility analysis and equity? The case of accessibility to supermarkets in Montreal
Bibliographic record
Abstract
In place-based accessibility analyses, larger spatial scales, such as census tracts or traffic analysis zones, are often chosen for their practicality and ease of data management. However, larger spatial scales can introduce errors or biases, a phenomenon commonly known as the Modifiable Areal Unit Problem (MAUP). Although MAUP has been addressed in accessibility studies, most compare biases between larger spatial scales and do not use building lots as the reference unit. In this paper, we consider building lots to be the smallest unit of analysis for transport and urban planning. Those that do often fail to estimate the direction and magnitude of overestimation or underestimation or quantify and identify which population groups are most affected. This paper addresses this gap by quantifying the misestimation of accessibility to supermarkets in Montreal, using the cumulative opportunities measure and time to the nearest amenity, by comparing residential building lots with dissemination blocks, dissemination areas, and census tracts. We also measured the cumulative opportunities using three spatial approaches: centroid-to-centroid within census geographies, centroid-to-amenity, and average accessibility. We found that the larger the spatial scale, the higher the misestimation. Census tracts (CTs), the larger spatial scale, show a mean overestimation of 132 % to 154 %, dissemination areas (DAs) range from 111 % to 136 %, and dissemination blocks (DBs) range from 99 % to 131 %. Overestimation affects 14–54 % of dwellings in census tracts, 9–47 % in dissemination areas, and 5–46 % in dissemination blocks, depending on the mode of transport. The centroid-to-amenity generally performs the best in terms of misestimation, and we found mixed results across income groups. These findings can inform decision-makers about the importance of using small spatial scales and optimal approaches to minimize bias and improve city resource allocation.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".