Study of spatial analysis methods and illustration with urban microdata from the Greater Montreal Area
Bibliographic record
Abstract
This paper relates to urban spatial data or point patterns. It focuses on methods allowing to synthesise data sets and to reveal similarities, trends, contrasts and knowledge. First perceived as bundles of data, urban spatial data sets develop into information on behaviours and trends when educated with appropriate methods.This paper discusses issues related to the use of large spatial datasets, Origine-Destination survey data and Canadian censuses data for instance. A number of spatial analysis methods are illustrated in order to further the information that can be drawn from these datasets. Actually, these methods can clarify the influence of space (absolute spatial location, local proximity, neighbourhood effects) on the nature and intensity of urban behaviours and features. Cet article s’intéresse aux données spatiales urbaines, conceptuellement représentées par des points, plus précisément à certaines méthodes permettant de les synthétiser et de révéler certaines similarités, tendances, contrastes et connaissances. D’abord perçues comme des ensembles sans cohérence, les bases de données spatiales deviennent des révélateurs de comportements et tendances lorsque disciplinées selon des méthodes appropriées.Cet article discute des enjeux relatifs à l’exploitation de gros ensembles de données urbaines, par exemple les données issues des enquêtes Origine-Destination montréalaises et des recensements canadiens. Différentes méthodes d’analyse spatiale, assistant la construction d’une connaissance spatialisée plus approfondie des phénomènes urbains, sont illustrées. En fait, ces méthodes permettent d’apprécier l’incidence de l’espace (localisation spatiale absolue, proximité locale, effet de voisinage) sur la nature et l’intensité des comportements et attributs urbains.
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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 | 0.016 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".