We are writing this editorial piece at the peak of the current Artificial Intelligence
(AI)'spring'as generative models quickly cross the bridge from the confines of academic and …

Spatial networks are useful for modeling geographic phenomena where spatial interaction
plays an important role. To analyze the spatial networks and their internal structures, graph …

Existing regionalization methods tend to be either spatial attribute-or spatial interaction-
based, while real-world tasks usually involve both considerations to satisfy multiple …

Building characteristics, such as number of storeys and type, play a key role across many
domains: interpreting urban form, simulating urban microclimate or modelling building …

Recognising the increasing complexities posed by climate challenges to urban
environments, it is crucial to develop holistic capabilities for urban areas to effectively …

Geodemographic analysis clusters geographic areas into socio-demographically
homogeneous groups. Existing clustering methods prioritize overall effectiveness, measured …

Geodemographic classification, a process of categorizing neighborhoods into distinct
groups based on their demographic, social, and economic characteristics to create summary …

Determining the optimal number of regions is a challenging issue in regionalization.
Although cluster validity indices developed for non-spatial clustering have been used to …

This study presents a multi-scale demographic analysis of COVID-19 booster vaccination
rates using Graph Neural Networks (GNNs). Data from Nueces County, Texas, was …

Geodemographics involves studying populations based on similarities in their living areas to
understand their characteristics, behaviours, and conditions, thereby facilitating informed …