Natural Route Analysis of Possible Bluestone Routes.

Last year I linked to a pre-print of this paper - https://www.sarsen.org/2024/04/the-natural-corridor-for-bluestones.html - this is the revised and accepted paper.

Joseph Lewis, Estimating the scale-dependent influence of natural terrestrial corridors on the positioning of settlements: A multi-scale study of Roman forts in Wales,

Journal of Archaeological Science,Volume 170, 2024, 106055, ISSN 0305-4403,

https://doi.org/10.1016/j.jas.2024.106055.

(https://www.sciencedirect.com/science/article/pii/S0305440324001237)

Abstract: Natural terrestrial corridors have been shown to have influenced the positioning of past settlements. The scale at which this pattern-process relationship operates is often un-estimated and thus remains unclear. This paper proposes the comparison of multiple point process models as an approach for estimating the optimal scale at which this relationship is strongest... 

Fig. 4. Natural terrestrial corridors at process scale of 1,100m for the walking-only scenario

Natural route analysis gives us a powerful, terrain-based lens into how people might have moved across ancient landscapes. By modeling least-cost paths (LCPs) across digital elevation models (DEMs), this approach identifies natural terrestrial corridors, low-gradient paths along river valleys, ridges, and coastal plains, that minimize energy expenditure during travel. We can then hypothesis the most likely overland routes the Stonehenge bluestones could have followed from Preseli to Salisbury Plain. When these models align with known historic routes like Roman roads and drovers’ tracks, it suggests we’re seeing deep-rooted patterns of movement that span millennia. This research helps bridge digital modelling with human experience on the ground, offering a realistic, testable framework for where to look next for archaeological evidence. Importantly, such models gain significant credibility when grounded in real-world experience. Keith Ray’s practical testing of the route—through physically walking and evaluating terrain—offers a preliminary experiential dataset that enhances the digital model with lived landscape insight.