Integrating human mobility and rural multifunction to identify village types in metropolitan suburbanization

Abstract

Increasing human mobility during metropolitan suburbanization drives rapid functional transitions in rural areas. However, the interactions between human mobility and rural function has been challenging due to the scarcity of real-life mobility data. Leveraging mobile phone data capturing 50.26 million real-world exposures among 1.24 million people in Wuhan, China, we investigated the human mobility network and evaluated the multifunctional development levels across 1559 villages. We constructed a multifunctional index system spanning five dimensions: residential life, agricultural production, non-agricultural production, ecological security, and leisure culture. A Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) algorithm was then proposed to identify village types by integrating mobility patterns and multifunctionality. Our analysis reveals that while Wuhan's rural areas remain predominantly monofunctional, high-frequency mobility drives evident multifunctional transformation. Population density, network centrality, and urban-rural connectivity all show significant positive correlations with residential life, non-agricultural production, and leisure-cultural functions, while exhibiting consistent negative correlations with ecological conservation. The STICC algorithm successfully delineated five distinct village types, enabling targeted optimization pathways. These findings provide a novel framework for deciphering mobility-function interdependence and advancing precision governance in metropolitan suburbanization.