RT Journal Article
T1 A predictive model for Palaeolithic sites: a case study of Monforte de Lemos basin, NW Iberian Peninsula
A1 Díaz Rodríguez, Mikel
A1 Fábregas Valcarce, Ramón
A1 Pérez Alberti, Augusto
K1 Predictive model
K1 Palaeolithic
K1 Settlement Patterns
K1 Spatial analysis
K1 Iberian Peninsula
K1 Europe
AB Although a theoretical model for the settlement patterns of Galician Palaeolithic has been proposed in the last decades, it has not been statistically tested. The present paper aims to check whether this previous theoretical model can be verified statistically. For this purpose, a methodology based on the creation of a predictive model has been used in which the main environmental variables were analysed and their suitability for predicting the location of Palaeolithic sites statistically verified. The predictive model shows that the most accurate variables are elevation, slope, cost to potential hydrology, the cost to wetland areas, and visual prominence. The results demonstrated that the theoretical model was fulfilled in some of the variables previously proposed. Thus, we have shown the usefulness of this approach to test hypotheses and the results obtained open new possibilities of analysis in the study of the Palaeolithic sites in NW Iberia
PB Elsevier
YR 2023
FD 2023
LK http://hdl.handle.net/10347/30631
UL http://hdl.handle.net/10347/30631
LA eng
NO Journal of Archaeological Science: Reports 49 (2023) 104012
NO This study is part of the research projects Dinámicas poblacionales y tecnológicas durante el Pleistoceno final-Holoceno de las Sierras orientales del Noroeste ibérico (R&D Projects of the Ministry of Science, PID2019-107480 GB-I00), Ocupaciones humanas durante el Pleistoceno en la cuenca media del Miño (HUM2007-63662/HIST) and Poblamiento durante el Pleistoceno Medio/Holoceno en las comarcas orientales de Galicia (HAR2010-21786/HIST). M. Díaz-Rodríguez is part of the CLIOARCH project which has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement 817564); and the NeanderEDGE project which has received funding from the Independent Research Fund Denmark (case number 9062-00027B). We are very grateful to the two anonymous reviewers and the editor, whose comments have contributed significantly and helped improve the initial version of this paper. The analyses of this work have been carried out in the statistical environment R (R Core Team, 2021)
DS Minerva
RD 24 abr 2026