Fairness related to locations (ie," where") is critical for the use of machine learning in a
variety of societal domains involving spatial datasets (eg, agriculture, disaster response …

As the adoption of machine learning continues to thrive, fairness of the algorithms has
become a key factor determining their long-term success and sustainability. Among them …

Geospatial interpolation plays a pivotal role in spatial analysis because it provides high-
quality data support for various spatiotemporal data mining (STDM) tasks. However …

Given multi-category point sets from different place-types, our goal is to develop a spatially-
lucid classifier that can distinguish between two classes based on the arrangements of their …

Fairness related to locations (ie,“where”) is critical for the use of machine learning in a
variety of societal domains involving spatial datasets (eg, agriculture, disaster response …

Spatial data are ubiquitous, massively collected, and widely used to support critical decision-
making in many societal domains, including public health (eg, COVID-19 pandemic control) …

In spatial regression models, spatial heterogeneity may be considered with either
continuous or discrete specifications. The latter is related to delineation of spatially …

This chapter provides an overview of this GeoAI handbook. It begins by highlighting the
interdisciplinary nature of GeoAI studies and reviews the historic roots of GeoAI. It then …

Recent developments of deep learning have demonstrated promising results for challenging
tasks in computer vision, natural language processing and so on. With the rapid revolution of …

Forecasting the spatiotemporal distribution of mobile traffic is crucial for efficient cellular
network management. Despite the superior performance of many deep learning studies …