Modeling evacuation in institutional space: Linking three-dimensional data capture, simulation, analysis, and visualization workflows for risk assessment and communication
Bibliographic record
Abstract
This article presents exploratory research to develop new workflows that address the challenges of adequately capturing the geometry and topology of complex institutional spaces, the analysis of prescriptive evacuation plans, and the simulation of human movement and behavior in emergency scenarios. We present a collection of geovisual analytical environments that were developed to permit new ways to view and assess risk, evacuation, and human movement. Part of this research considers how different approaches to the representation of complex institutional space, using three-dimensional capture technologies at multiple resolutions (or derived from conventional formats, such as building plans), have implicit advantages or liabilities in the analysis of risk and human evacuation. We combine three-dimensional data capture methods with geographical information science theory, three-dimensional game engines, three-dimensional evacuation simulations and spatial analyses that address the variability of campus populations, and draw upon three-dimensional modeling and photogrammetry for the assessment of real-world features in digital space. The outcome of this research demonstrates agile workflows that address emergency planning requirements, but could also enable enhanced visual analysis and interactive learning by all campus citizens. Furthermore, this work reveals key considerations and limitations associated with the dynamic nature of evacuation events and the static environments in which they have been simulated.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".