Modifiable Areal Unit Problem and Modifiable Temporal Unit Problem effects on accessibility to supermarkets in Montreal, Canada
Bibliographic record
Abstract
Accessibility is the ease of reaching a location within a given time or distance by a specific mode of transport, such as walking, cycling, public transport, or cars. Every accessibility measure uses a specific spatial scale, such as traffic analysis zones (TAZ), census tracts (CTs), dissemination areas (DAs), or dissemination blocks (DB); various zoning schemes, such as hexagons or squares. Also a specific temporal resolution or granularity is chosen, such as measuring every minute, every five minutes, every 10 minutes, etc; a specific temporal segmentation, such as using peak-hour or off-peak hours; or a temporal boundary, which is how long the process is (e.g. a time threshold of 15 minutes or 30 minutes). However, many studies choose a temporal and spatial resolution or temporal segmentation arbitrarily or because it is the only one available. Most of the time, it is different from reality. The bias or error of using non-ideal spatial and temporal components is known as the Modifiable Areal Unit Problem and the Modifiable Temporal Unit Problem, respectively. Despite the importance of these two problems, previous studies have only focused on understanding one problem or the other, and no studies consider both effects simultaneously. Understanding both effects has yet to be studied or structured in general literature and transport studies and has been named the Modifiable Spatio-Temporal Unit Problem (MTSUP). This research measured the misestimation levels of these problems on the accessibility of supermarkets in the city of Montreal, Canada. I compared accessibility at various spatial and temporal resolutions and temporal segmentations to the finest temporal and spatial unit of analysis at the ideal temporal segmentation: building lots every minute from 10:00 am to 11:00 am. The results indicated that the coarser the temporal and spatial resolution and a temporal segmentation that do not represent the hour in which most of the trips to supermarkets are made, the higher the misestimation (overestimation and underestimation). Likewise, differences were found between various socioeconomic groups. Studying this problem is essential in urban planning because if a specific temporal and spatial resolution has higher levels of accessibility, than the accessibility at the building lot level (overestimates accessibility), it indicates that the building lot does not need transportation and land use intervention
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".