Contextualizing the Geographic Influence on Infantry Manoeuvrability in a Historical Battlefield Using GIS: A Case Study of the Canadian Corps in the Second Battle of Passchendaele, First World War
Bibliographic record
Abstract
The Second Battle of Passchendaele (October 26 - November 10, 1917) remains one of the most enduring symbols of the Canadian experience in the First World World, yet its historical prominence contrasts with a limited comprehension of the geographic realities faced by the soldiers. This gap stems largely from the absence of geographic contextualization in teachings of military history, compounded by a historical under-examination of former battlefields in Flanders. This study addresses this gap via a GIS-based quantitative examination of the influence of geographic conditions on infantry manoeuvrability during the Second Battle of Passchendaele. High-resolution digital elevation models and historical British Army maps were used to reconstruct key battlefield features — slope, flooding, roads and railways, and enemy fields of fire (viewsheds) — as "manoeuvrability indicators". Infantry battlefield traversal was simulated using least-cost path calculations across multiple individual and 3 aggregated indicators (terrain passability, hazard exposure, manoeuvrability [all]) for 18 battlefield subdivisions (corridors). Statistical analyses were then conducted to evaluate (1) the influence of each geographic variable on manoeuvrability using the Mantel test for correlation and Welch's t-test between equally and independently weighted regression models; (2) the spatial variability of these relationships via pairwise Games-Howell post hoc tests per regression model weighting type; and (3) the alignment between predicted manoeuvrability and historical accounts of battlefield conditions using Cohen's weighted kappa coefficient under ordinal and binary classification schemes. The results showed least-cost paths to be strongly influenced by spatial concentrations of high- and low-cost features, but also balancing the avoidance of high-cost features with minimization of total costs. All geographic variables exhibited very significant influence on infantry manoeuvrability, but still exhibited substantial spatial variability. Independently weighted regression models generally improved manoeuvrability predictability over equally weighted models, but the complexity of geographic interactions showed some areas with no significant difference between model types. The Games-Howell tests revealed significant variability across corridors, indicating considerable divergence between models for different geographic contexts. Cohen's weighted kappa showed moderate alignment between predicted manoeuvrability and historical accounts, with near-exclusive significance between classification schemes suggesting a trade-off between classification granularity and indicator complexity. Ordinal classifications better captured localized variations, whereas binary more effectively captured broader terrain patterns. The findings consistently emphasized the complex and spatially variable interplay of geographic variables in influencing infantry manoeuvrability. Rather than challenging established historical interpretations, this study complements them by developing a replicable GIS-based framework to analyze the influence of geographic conditions on infantry manoeuvrability, offering a promising foundation for incorporating geographic context into historical military analysis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".