Leveraging modern geospatial data science techniques for multi-hazard exposure analysis in British Columbia, Canada
Bibliographic record
Abstract
In 2023 and 2024 and in partnership with Sage on Earth Consulting, BGC Engineering undertook a province-wide hazard exposure assessment for the provincial government of British Columbia, Canada. British Columbia spans approximately 940,000 square kilometers, stretching from the Pacific Ocean in the west to the Rocky Mountains in the east and from the Yukon border in the north to Washington State in the south. This expansive region faces a wide array of natural hazards, including flooding from mountain streams, tsunamis and shoreline erosion in coastal communities; earthquakes; landslides; debris flows; wildfires; drought; extreme heat and, increasingly, cascading hazards intensified by climate change. The goal of this project was to design and implement a geospatial workflow for assessing exposure of valued assets to hazards, delivered to a government agency in a standardized format that facilitated future updates and public governance over data sharing. To accomplish this, BGC designed a data model and analysis pipeline that had sufficient performance for the iterative processing of large multi-hazard and asset datasets and that could be packaged for government agency development of a data portal. Within the data model, hazards were defined as areas which exceed hazard-specific intensity thresholds and/or annual probability of occurrence; these binary hazard data were the primary input to the analysis pipeline for each hazard type. The list of assets to be included in the analysis was determined through a series of consultations with project stakeholders aimed at identifying which assets are most important and what data was available consistently for the entire province. The result of the analysis was a set of exposure metrics representing population counts, monetary values of property exposed to hazards, and the lengths of transportation and utility networks within hazard zones summarized using a uniform 1.5 km x 1.5 km grid. These metrics were delivered along with documentation of the data model, the data, and the codebase for the analysis pipeline.The resulting analysis revealed spatial patterns of hazard exposure and provided actionable insights to support provincial-scale risk management. This work represents a foundational step in risk assessment and mitigation planning. It offers a means of prioritizing local-scale risk assessments within a jurisdiction as vast as British Columbia, enabling focused resource allocation and informed decision-making. Collaboration was central to the project's success. In association with Sage on Earth Consulting , BGC engaged with multiple stakeholders to refine inputs and validate assumptions, and ensure the outputs were accessible and meaningful. The results are designed for government-managed web access to both data inputs and analysis outputs, promoting transparency and usability for diverse audiences.This provincial hazard exposure assessment highlights the importance of integrating data, geospatial analysis, stakeholder collaboration, and practical tools to address the complex challenges posed by natural hazards in British Columbia. The findings not only advance the understanding of hazard exposure but also lay the groundwork for more detailed, localized risk assessments and targeted mitigation efforts.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".