Detecting Associations between Archaeological Site Distributions and Landscape Features: A Monte Carlo Simulation Approach for the R Environment
2020
Richard J. Hewitt | Francis F. Wenban-Smith | Martin R. Bates
Detecting association between archaeological sites and physical landscape elements like geological deposits, vegetation, drainage networks, or areas of modern disturbance like mines or quarries is a key goal of archaeological projects. This goal is complicated by the incomplete nature of the archaeological record, the high degree of uncertainty of typical point distribution patterns, and, in the case of deeply buried archaeological sites, the absence of reliable information about the ancient landscape itself. Standard statistical approaches may not be applicable (e.g., X2 test) or are difficult to apply correctly (regression analysis). Monte Carlo simulation, devised in the late 1940s by mathematical physicists, offers a way to approach this problem. In this paper, we apply a Monte Carlo approach to test for association between Lower and Middle Palaeolithic sites in Hampshire and Sussex, UK, and quarries recorded on historical maps. We code our approach in the popular &lsquo:R&rsquo: software environment, describing our methods step-by-step and providing complete scripts so others can apply our method to their own cases. Association between sites and quarries is clearly shown. We suggest ways to develop the approach further, e.g., for detecting associations between sites or artefacts and remotely-sensed deposits or features, e.g., from aerial photographs or geophysical survey.
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