A spatial pattern analysis of frontier passes in china’s northern silk road region using a scale optimization blr archaeological predictive model

Abstract

In China’s Northern Silk Road (CNSR) region, dozens of frontier passes built and fortified at critical intersections were exploited starting at approximately 114 B.C. to guarantee caravan safety. Understanding the pattern of these pass sites is helpful in understanding the defense and trading system along the Silk Road. In this study, a scale optimization Binary Logistic Regression (BLR) archaeological predictive model was proposed to study the spatial pattern of CNSR frontier passes for understanding the critical placement of ancient defense and trading pass sites. Three hundred and fifty sample locations and 17 natural proxies were input into the model. Four strongly correlated factors were reserved as independent variables to construct the model, which was validated by 150 surveyed data and Kvamme’s Gain statistics. According to the variable selection and model optimization, the best spatial scale varies with the stability of the variables, such as 50 m and 1000 m, respectively, for the terrain and non-terrain variables. Clustering characteristics were identified with division overlapped with a 400 mm precipitation line using the site sensibility map. The high and medium probability areas were assembled along the Great Wall and the CNSR routes, especially in the western part, revealing that the model is also helpful to reconstruct the Silk Road routes.