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April 24, 2026npj Heritage Science3 citationsOpen Access

Distribution prediction and driving mechanism of Neolithic settlements in the Jing River Basin, Northwest China

JZJunhui ZhangHZHuan ZhangJLJingyi Li

Key Points

  • The research aims to explore the migration and influencing factors of Neolithic settlements in the Jing River Basin through predictive modeling.
  • Utilized GIS technology and machine learning to construct an archaeological prediction model (APM).
  • Analyzed settlement location data during the Neolithic period in relation to environmental factors.
  • The XGBoost model accurately predicted settlement locations, showing enhanced stability.
  • Altitude and proximity to large sites were crucial in the middle Neolithic, while NDVI and roughness became significant in the late Neolithic.
  • There was a westward and southward migration of settlements, indicating adaptive strategies to environmental changes.

Abstract

The study of the human–land relationship during the Neolithic period helps to understand the mechanism of human–environment interaction. This study focuses on the Jing River Basin (JRB), the key node of the ancient Silk Road, and uses GIS technology and machine learning to build an archaeological prediction model (APM). The study aims to understand the migration, evolution, and influencing factors of human activities in the Neolithic period in the Guanzhong Basin. The results show that: (1) The XGBoost model demonstrated superior accuracy and stability in predicting settlement locations. (2) Altitude and distance from the large sites were key factors in the middle Neolithic period, while climatic deterioration heightened the importance of NDVI and roughness in the late Neolithic period. (3) A westward and southward migration of settlements from the middle to late Neolithic period, reflecting adaptive strategies to environmental change and potential cultural interactions with the Ganqing and Guanzhong regions.

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Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/69eb084f553a5433e34b357dhttps://doi.org/10.1038/s40494-026-02550-3
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