Spatial data and workflow automation for understanding densification patterns and transport energy networks in urban areas: The cases of Bergen, Norway, and Zürich, Switzerland
Bibliographic record
Abstract
A better understanding of how the spatial configuration of cities, understood as urban structure and forms, can achieve sustainable development is needed. This paper presents spatial data and an automated workflow for studying the urban structures (i.e., road and transportation networks) and forms (i.e., building size, position, function and density) of two medium-sized European cities - Bergen, Norway and Zürich, Switzerland. The data focuses on examining correlations between the densification patterns and transport energy usage of these cities de Koning et al., (2020). Spatial and tabular datasets for (i) urban structures, (ii) urban forms, (iii) building density, (iv) road centre lines and (v) transport energy usage are obtained as georeferenced files from OpenStreetMap (OSM) and upon request from collaborating local and national authorities. Transport energy data is derived from traffic data collected from the Norwegian Public Road Authorities or simulated via a traffic model. Open-source data is used wherever possible. Data gaps within proprietary data are supplemented with proxies or open-source data. Hand-drawn axial maps drawn by the authors using the Space Syntax methods and analysed via depthmapX software are a crucial dataset presented here. All analysed data are then returned to a Geographical Information System (GIS) platform and processed via an automated workflow of 19 steps built via the ModelBuilderTM tool in ESRI® ArcGIS. The automated workflow allows for repetitive cross-city comparison and the compilation of diverse spatial data sources for analysis. In combination with the novel workflow, the dataset can be used for future comparative studies in spatial planning, transport planning and management of energy systems to facilitate informed decision-making towards more sustainable developments.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".