One tree matters: reflections on data as a design material in urban forestry
Bibliographic record
Abstract
This exploratory study explores the potential contexts and opportunities for emerging visual data in urban forest design. Forestry experts deploy drone-mounted digital sensors to capture detailed visual and spatial data urban vegetation. These sensors generate point clouds that not only inform ecological analysis but also visually construct urban environments from a pedestrian perspective. Even though many data sources and visualisation tools such as GIS are available, how visualised data should be integrated into design practice is still unclear. Using a prototype multi-sourced data visualisation, we conducted eight semi-structured interviews with urban forestry experts to elicit reflections of the analytical and cultural roles of data visualisations in the domain. Thematic analysis of the interview transcripts revealed three design-oriented themes: (1) design analysis, (2) public engagement, and (3) sustainability. By analysing expert reflections, this paper considers potential research directions for visualising social and ecological data as a design material in the built environment. We discuss the implications of such visualisations for the broader community of spatial planning research including urban designers and communication scholars, proposing future research directions that leverage visual data to better design evolving urban landscapes.
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.
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 | 0.038 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.041 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".