Abstract Machine learning (ML) is an artificial intelligence technology that has been used in
atmospheric pollution research due to their powerful fitting ability. In this review, 105 articles …

Properties of spatially explicit data are often ignored or inadequately handled in machine
learning for spatial domains of application. At the same time, resources that would identify …

Understanding population density at a fine spatial scale is beneficial for urban management
and planning. Existing machine learning methods have been widely used to predict the …

Data are increasingly spatio‐temporal—they are collected some‐where and at some‐time.
The role of proximity in spatial process is well understood, but its value is much more …

For buyers, investors and urban policy, understanding drivers of community-level house
prices across space and across time, are important for urban management and economic …

Conventional methods of machine learning have been widely used to generate spatial
prediction models because such methods can adaptively learn the mapping relationships …

多模态地理大数据时空分析旨在融合地理大数据的多模态信息发现有价值的时空分布规律,
异常表现, 关联模式与变化趋势, 是全空间信息系统的核心研究内容, 并有望成为推进地理学人地 …

Multiscale geographically weighted regression (MGWR) is an important method that is used
across many disciplines for exploring spatial heterogeneity and modeling local spatial …

Models for estimating urban rental house prices in the real estate market continue to pose a
challenging problem due to the insufficiency of algorithms and comprehensive perspectives …

Accurate prediction of mineral resources is imperative to meet the energy demands of
modern society. Nonetheless, this task is often difficult due to estimation bias and limited …