Measuring evacuation rates from mobility data during the McDougall Creek wildfire in British Columbia, Canada
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
In recent years, the intensity and occurrence of wildfires have risen globally, driven by climate change triggering extreme dry weather conditions. This study focuses on the 2023 McDougall Creek wildfire in British Columbia, highlighting the vulnerability of urban communities to severe wildfires. Using aggregated and de-identified network mobility data from a Canadian telecommunications provider, we quantified neighborhood-level evacuation rates and examined inter-regional travel patterns during the wildfire event. We applied a spatial difference-in-difference (DID) model to understand how neighborhood characteristics influenced evacuation rates. Our findings suggest that formal evacuation orders were positively associated with evacuation rates. We also found that the distance to the wildfire perimeter was a strong and significant predictor of evacuation rates, while socio-demographic variables previously identified as strong predictors of evacuation rates were not significant in this particular context. The analysis of travel patterns before and during the wildfire event reveals distinct directional patterns and variations in inter-regional travel across spatial scales. This research contributes to the understanding of wildfire evacuation dynamics and the application of human mobility data into disaster management, enhancing our knowledge of the human response to natural disasters. • Measured wildfire evacuation rates using network mobility data at the neighborhood level. • Analyzed neighborhood characteristics influencing wildfire evacuation rates through a spatial difference-in-difference model. • Found proximity to the wildfire perimeter to be a strong predictor of evacuation rates. • Revealed distinct directional movement patterns in inter-regional travel during the wildfire event.
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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