A Prediction Study on Archaeological Sites Based on Geographical Variables and Logistic Regression—A Case Study of the Neolithic Era and the Bronze Age of Xiangyang

Linzhi Li, Yujie Li (), Xingyu Chen and Deliang Sun
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Linzhi Li: The Key Laboratory of GIS Application Research, School of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China
Yujie Li: School of History and Society, Chongqing Normal University, Chongqing 401331, China
Xingyu Chen: The Key Laboratory of GIS Application Research, School of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China
Deliang Sun: The Key Laboratory of GIS Application Research, School of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China

Sustainability, 2022, vol. 14, issue 23, 1-19

Abstract: Archaeological site predictive modeling is widely adopted in archaeological research and cultural resource management. It is conducive to archaeological excavation and reveals the progress of human social civilization. Xiangyang City is the focus of this paper. We selected eight geographical variables as the influencing variables, which are elevation, slope, aspect, micro-landform, slope position, plan curvature, profile curvature, and distance from water. With them, we randomly obtained 260 non-site points at the ratio of 1:1 between site points and non-site points based on the 260 excavated archaeological sites and constructed a sample set of geospatial data and the archaeological based on logistic regression (LR). Using 10-fold cross-validation, we trained and tested the model to select the best samples. Thus, the quantitative relationship between the archaeological sites and geographical variables was established. As a result, the Area Under the Curve (AUC) of the LR model is 0.797 and its accuracy is 0.897 in the study. A geographical detector unveils that the three influencing variables of Distance from water, elevation and Plan Curvature top the chart. The archaeological under LR is highly stable and accurate. The geographical variables constitute crucial variables in the archaeological.

Keywords: archaeological site prediction; logistic regression; geographical variables; Xiangyang City; Hubei (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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