Computational Approaches to the Study of Movement in Archaeology

Polla, Silvia; Verhagen, Philip

doi:10.1515/9783110288384

Cited by 16 publications

(4 citation statements)

References 11 publications

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“…But it would also inevitably result in more specific, less transferable assumptions and interpretations. An excessive amount of detail in the computation of LCPs might even result in less realism, since this would assume a level of planning perfection that might not have been achievable by prehistoric (or even current) societies (this point is raised by Verhagen et al, 2014). And then there is also the matter of a multitude of social factors, not easily representable in a cost surface, that might have prevented optimal network links from being established in the first place (modern transportation networks certainly are a compromise between what should be done and what can be done, taking into consideration political, judicial and economic constraints), which add to our conviction that overoptimisation of models can actually have a negative impact on their realism.…”

Section: Discussionmentioning

confidence: 99%

“…White and Barber (2012) show that it is possible to locate previously unknown sites at crossing points, given solely a realistic set of LCPs; Fonte et al (2017) identify additional historic sites at points of LCP convergence (they refer to them as points of "divergence", but in an isotropic model, this is synonymous). The study by Verhagen et al (2014) considers junction points as candidates for new network nodes and explores their role as means of further link optimisation (so-called Fig. 7 A completely connected network (upper left) and its maximum distance derivations, with links modelled as least-cost paths (compare with Fig.…”

Section: Least-cost Paths and Network Topologymentioning

confidence: 99%

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Exploratory Network Reconstruction with Sparse Archaeological Data and XTENT

Ducke

Suchowska

2021

J Archaeol Method Theory

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This contribution discusses methods for reconstructing the links of past physical networks, based on archaeological site locations and mathematical models of few parameters. Networks are ubiquitous features of human culture. They structure the geographical patterning of the archaeological record strongly. But while material evidence of networked social interaction is abundant (e.g. similarities in artefact types and technologies), preserved physical remains of the networks (such as roads) are much rarer, making it difficult, if not impossible, to understand the spatial structure of cultural exchange and diffusion. This raises the intractably complex problem of optimal physical network reconstruction, based solely on the known locations of archaeological finds or sites. We address the tractable subproblem of plausible reconstruction of network links. Using a small sample set of Late Bronze Age settlement sites in and around the Mediterranean, we explore model-based network reconstruction from sparse and legacy archaeological data. A hierarchical model is provided by a network-adapted version of Renfrew and Level’s (1979) classic XTENT formula. We show that a GIS-based implementation, that utilises cost surfaces, offers significantly increased realism and analytical advantages. Our approach represents an alternative (with its specific strengths and weaknesses) to more data-intense and computationally complex approaches, such as agent-based models and simulations. The network reconstruction software used in this study (v.net.models) is available under an open source license.

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Section: Discussionmentioning

confidence: 99%

Section: Least-cost Paths and Network Topologymentioning

confidence: 99%

Exploratory Network Reconstruction with Sparse Archaeological Data and XTENT

Ducke

Suchowska

2021

J Archaeol Method Theory

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“…The value of such reconstructions for archaeological research has become more prominent in the Netherlands in recent years, also outside the 'Finding the limits of the limes' project (e.g. Pierik 2017;Van Lanen 2017;De Kleijn et al 2018).…”

Section: Natural Palaeogeographymentioning

confidence: 99%

Palaeogeographic-Analysis Approaches to Transport and Settlement in the Dutch Part of the Roman Limes

Groenhuijzen

2019

Computational Social Sciences

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The aim of the PhD research on which this chapter is based is to reconstruct and analyse the cultural landscape of the Dutch limes area using computational approaches, specifically to model and analyse transport networks, settlement patterns and their relationship with the natural environment, to better understand the interactions between the Roman military population and the local population that lived in this frontier region. The goal of this chapter is to present the general results of this study and showcase the technical, methodological and interpretative aspects that it has contributed to the research field of computational archaeology and to the archaeological understanding of the Dutch part of the Roman limes.

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“…These include a semi-quantitative review of the local research history, a discussion of site distributions with respect to modern land use, the nature and intensity of archaeological field work as well as statistical investigations on the impact of erosion, colluviation and weathering conditions on the preservation of prehistoric sites [ 38 – 40 ]. Furthermore, comparisons of archaeological site distributions to random point distributions as well as (predictive) modelling approaches are applied to the study of former settlement dynamics [ 11 , 37 , 41 – 43 ]. (4) Finally, there was a shift from spatial analyses based on point coordinates to studies based either on Site-Catchment Analysis or Site Exploitation Territories (SET) [ 12 , 44 – 51 ].…”

Section: Introductionmentioning

confidence: 99%

Large-scale investigations of Neolithic settlement dynamics in Central Germany based on machine learning analysis: A case study from the Weiße Elster river catchment

et al. 2022

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The paper investigates potentials and challenges during the interpretation of prehistoric settlement dynamics based on large archaeological datasets. Exemplarily, this is carried out using a database of 1365 Neolithic sites in the Weiße Elster river catchment in Central Germany located between the southernmost part of the Northern German Plain and the Central Uplands. The recorded sites are systematically pre-processed with regard to their chronology, functional interpretation and spatial delineation. The quality of the dataset is reviewed by analyzing site distributions with respect to field surveys and modern land use. The Random Forests machine learning algorithm is used to examine the impact of terrain covariates on the depth of sites and pottery preservation. Neolithic settlement dynamics are studied using Site Exploitation Territories, and site frequencies per century are used to compare the intensity of land use with adjacent landscapes. The results show that the main trends of the Neolithic settlement dynamics can be derived from the dataset. However, Random Forests analyses indicate poor pottery preservation in the Central Uplands and a superimposition of Neolithic sites in the southernmost part of the Northern German Plain. Throughout the Neolithic the margins between soils on loess and the Weiße Elster floodplain were continuously settled, whereas only Early and Late Neolithic land use also extended into the Central Uplands. These settlement patterns are reflected in the results of the Site Exploitation Territories analyses and explained with environmental economic factors. Similar with adjacent landscapes the Middle Neolithic site frequency is lower compared to earlier and later periods.

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Computational Approaches to the Study of Movement in Archaeology

Cited by 16 publications

References 11 publications

Exploratory Network Reconstruction with Sparse Archaeological Data and XTENT

Exploratory Network Reconstruction with Sparse Archaeological Data and XTENT

Palaeogeographic-Analysis Approaches to Transport and Settlement in the Dutch Part of the Roman Limes

Large-scale investigations of Neolithic settlement dynamics in Central Germany based on machine learning analysis: A case study from the Weiße Elster river catchment

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