Introducing the Human Factor in Predictive Modelling:

Modelling Archaeological and Geospatial Information for Burial Site Prediction, Identification and Management

“…Inductive (data driven) models, which have long been the dominant form of predictive modelling [5][6] exploit existing knowledge to forecast spatial patterns [7]. In this type of modelling the criteria used come from observations rather than from theory, namely through statistical comparison between known archaeological sites' locations and other variables, usually representing natural landscape features (environmental parameters) [3,6,8]. Nevertheless, by the early 1990's and onwards, numerous studies questioned inductive models, despite their popularity especially for CHM applications [9][10][11][12][13][14][15][16].…”

“…According to these critiques, inductive models follow "loose" approaches that lack of any non-environmental predictor variables. Thus, the role of social, ideological and political factors that produced, along with the environmental variables, the statistical correlations that were found, receive little attention in the analysis, and, therefore, the model is based on insufficient theoretical documentation [3,6,[8][9][10][11][12][13][14][15][16].…”

“…Theory driven models involve the perspective of human decision making in modelling and thus are clearly more advantageous and therefore preferable especially for academic researchers who are more interested in finding explanations in the reasons of archaeological remains' concentrations in particular parts of the landscape [19]. Nevertheless, the problem is always more complex in practice, as this type of modelling needs a welldefined theoretical framework for settlement location choice, which is not always available [6]. On one hand, the discovery of archaeological remains is coincidental and mainly a result of modern development interventions, which, however, lead to partial or total destruction of archaeological sites.…”

The spatial structure of Galician megalithic landscapes (NW iberia): A case study from the Monte Penide region

“…Premo 2004;Bevan, Conolly 2006;Crema et al 2010;Hinz 2011;Bevan 2012;Bevan et al 2013;Verhagen et al 2013;Nakoinz, Knitter 2016). Previous work on Galician megaliths (Carrero-Pazos 2017, 2018b) developed a predictive model of megalithic site probability and assessed its effectiveness via a control sample, significance tests and a gain statistic (for the latter,Kvamme 1988).…”

The Archaeology of the Postindustrial: Spatial Data Infrastructures for Studying the Past in the Present

“…Some computer-aided archaeological techniques adopted very early on and still in widespread use, such as archaeological predictive modeling (APM) (especially popular within cultural resource management (CRM) archaeology), have persisted in spite of postprocessual critiques, and their practitioners (who tend to be more quantitatively-focused in their research) have gradually worked towards developing less positivist or environmentally-deterministic iterations of their methods rather than fundamentally altering or abandoning them (Lock and Harris, 2000;Verhagen 2007;Verhagen, Nuninger, Tourneux, Bertoncello, and Jeneson, 2013). Other approaches, such as computer-aided archaeological simulation, became deeply unfashionable during the postprocessual era, only to re-emerge later as technological innovations and new theoretical arguments addressed some of the critiques and brought the approach back towards the mainstream (Lake, 2014).…”