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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,006 | 0,006 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».