Interpreting urban space through cognitive map sketching and sequence analysis
Bibliographic record
Abstract
Traditionally, analysis of sketch maps of urban areas has focused on the interpretation of hand‐drawn renditions of features that are most familiar to individuals. Few researchers have investigated the sequence that sketchers use to identify features on the urban landscape and how these features are linked together to form a coherent ‘picture’ of an area. This article builds upon previous research by exploring the sequential pattern of sketch map creation. Two research questions are proposed, namely, can a repetitive sequential order in element inclusion be identified for different individuals sketching the same urban environment? If so what features are mapped in which order to create the sketchers' image of the city? Findings suggest that three distinct groups of cognitive maps exist, namely, sequential, spatial and hybrid, and that the map elements of each group are organised in a distinctive manner with paths and landmarks as principal elements. It is suggested that insights into this process provide more substance to understanding how individuals interpret and structure urban space and use this information to navigate both known and new environments.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".