Interpreting urban space through cognitive map sketching and sequence analysis
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| 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