Visualizing the impact of natural disaster disruption events with 511 data : a case study in the province of British Columbia, Canada
Bibliographic record
Abstract
This thesis presents a methodology for identifying highly impacted locations on the provincial highway system of British Columbia (BC), Canada, due to disruption events caused by five groups of natural disasters, and for visualizing the degree of impact of these disruptions. Data is obtained from the province’s DriveBC road condition and incident information system. A data parsing procedure is developed to improve data governance. Based on the data provided by DriveBC, natural disaster disruption events occurring on the BC highway network for the years 2017 through 2021 were identified and categorized into the five groups of natural disasters. The impact caused by each disruption event was represented by a score calculated using a weighted linear sum multi-criteria decision analysis (MCDA) model, which uses four criteria to produce an impact score for each event based on a pairwise comparison between each pair of criteria. The Kernel Density Estimation (KDE) for Lines method enables the visualization of the degree of impact of natural disaster disruption events by estimating the density of events weighted by the impact scores of all events found within an array of 50-km by 50-km raster cells. Higher resulting total impact scores are linked to higher impacted highway locations. Highway locations most impacted by events in the five groups were identified and mapped. A top ten list is provided for each map. Connections between the top highway locations and extreme weather events are made when possible.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".