Spatial Statistics

We proposed the Geographical Cross Mapping Cardinality (GCMC) model to reliably quantify causal strengths from spatial cross-sectional data with low false positive rates.

We developed tEDM, an open-source R package that implements and extends empirical dynamic modeling for temporal causal discovery in urban data.

Human mobility during the Spring Festival is a large-scale annual human mobility across China. This paper evaluated the human health risks of short-term exposure to PM2.5 in the Yangtze River Delta through a combination of the daily-level high …

To introduce our published paper in sensing health city using multimodal data and GeoAI.

To introduce our studies in health geography.

To introduce our published paper in sensing health city using multimodal data and GeoAI.

Air pollutants have been known as the most persistent environmental risk factors of elevated blood pressure (BP). However, most of the existing studies measured resting BP only, which is not accurate as ambulatory BP. This study investigated the …

Urbanization, characterized by rapid construction land expansion, has transformed natural landscapes and significantly altered river networks in emerging metropolitan areas. Understanding the historical and current conditions of river networks is …

To introduce our published paper in sensing health city using street view imagery and GeoAI.

To introduce the application of night light remote sensing data in urban studies.