Street Network Morphologies: On the Characterization and Quantification of Street Systems. A Case Study in Montréal.
Bibliographic record
Abstract
This study aims at understanding the spatial dynamics of the street system in residential sectors of Montréal. Different periods of development have produced street networks displaying a diversity of characteristics and configurations. Yet all these different pieces are spatially interconnected implying that they are part of a functional whole. In the course of the historical evolution of the city the new pieces of the network are connected to pre-existing rural roads from which they often stem. However, while responding to a set of internal functional rules and technical requirements, the street system does not deploy in autarchy. Rather, it is integrated within a broader spatial framework comprised of natural and human-made features such as the hydrographic system, the topography, the agricultural allotment system and in more recent times, of components of technical systems such as canals, railroads and high-capacity transportation infrastructure. By delving into the differing street networks geometries as well as into the barriers and boundaries that spatial discontinuities, the project sets about identifying the “parts” in order to understand their inner characteristics as well as the modalities of their articulation to the “whole.” We hence define neighbourhoods as areas predominantly residential which exhibit some degree of internal homogeneity in regards to block geometry, street network configuration and refer to these areas as “morphological neighbourhood areas,” or simply, MNAs. A variety of quantitativea and qualitative methods are mobilized with the purpose of delimiting MNAs in the Island of Montréal. Subsequently, with MNAs as our unit of reference, a classification of urban neighbourhoods is proposed based on quantifiable spatial properties of the urban tissue, which include attributes pertaining local street network geometries, and part-to-whole topological relationships.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".