Applications of 3D printing in physical geography education and urban visualization
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
<p>Through decreasing hardware costs and novel areas of application, three-dimensional (3D) printing has become exceedingly popular in recent years. Starting with a project focused on public education about urban hydrology, we explored the use of 3D-printed landscape models in a number of regional applications. We also experimented with the use of 3D-printed city models to engage users through urban visualization. Our goal was to examine the role of GIS in processing geospatial data for 3D printing and to explore novel applications in physical and urban geography education and outreach. Following a brief review of related literature, this article outlines data sources for digital elevation models, boundary datasets, and building footprints with height information and the processes used to transform these into 3D-printable data files. We then describe applications focused on urban watersheds and landforms in the area of Toronto, Canada, and illustrate city models for neighbourhoods of Toronto. We found that the 3D models were favourably received by diverse types of users, from hydrology experts to environmental studies students to the general public. The overwhelmingly positive feedback generated by this project suggests that 3D-printed landscape and city models are a worthwhile strategy for improving physical and urban geography education and outreach. </p>
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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