Abstract Machine learning and artificial intelligence (ML/AI), previously considered black box
approaches, are becoming more interpretable, as a result of the recent advances in …

Understanding the associations between the built environment and road traffic CO 2
emissions is crucial for develo** strategies to mitigate carbon emissions. However …

Accurate prediction of crop yield is essential for agricultural trading, market risk management
and food security. Although various statistical models and machine learning models have …

Due to its vast territory, the climatic conditions and socioeconomic development of different
regions in China are closely related to geographical location, and their impact on city-level …

While recent developments have extended geographically weighted regression (GWR) in
many directions, it is usually assumed that the relationships between the dependent and the …

Geospatial artificial intelligence (GeoAI) has emerged as a subfield of GIScience that uses
artificial intelligence approaches and machine learning techniques for geographic …

Landslides are among the most devastating natural disasters worldwide. Landslide
susceptibility map** (LSM) is a scientific approach for assessing landslides-prone areas …

Geographically weighted regression (GWR) and geographically and temporally weighted
regression (GTWR) are classic methods for estimating non-stationary relationships …

Spatial prediction tasks are challenging when observed samples are sparse and prediction
samples are abundant. Gaussian processes (GPs) are commonly used in spatial prediction …