Electronic behaviour mapping and GIS application for Stavanger Torget, Norway
Bibliographic record
Abstract
In Norway, there is a growing interest in urban life as opposed to previously urban space.For urban planners and designers this means that methods of spatial registration and analysis need to be extended to include user behaviour, perception and experience.Therefore, user observation and behavioural surveys have become more prominent.To this end, behaviour mapping can be used as a tool to investigate the current use of a space.While manual behaviour mapping has limitations, this paper describes the development and testing of a GIS-based application using electronic maps for registration and analysis of observed behaviour in an urban public space.The behaviour mapping application has been tested and used in a study of Stavanger Torget, the most central public space in Stavanger, Norway.The collection and analysis of data was executed to investigate the amount of users and types of activities in the space throughout the week.Here, the electronic map application proved to be helpful in terms of making registration more efficient, instantly generating digital maps of the observations and providing tabular data for further analysis.This strongly improves urban analysis with regard to behavioural observation and makes related data collection and analysis much more efficient.This ultimately allows for the creation of a GIS database regarding the relationship between physical characteristics and user behaviour, something that is particularly relevant with the growing awareness for quality in public space and urban life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".