Study of spatial analysis methods and illustration with urban microdata from the Greater Montreal Area
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it