Functions to calculate measures of spatial association, especially measures of spatial
autocorrelation, have been made available in many software applications. Measures may be …

Graph neural networks (GNNs) provide a powerful and scalable solution for modeling
continuous spatial data. However, they often rely on Euclidean distances to construct the …

Inference procedures for spatial autocorrelation statistics assume that the underlying
configurations of spatial units are fixed. However, sometimes this assumption can be …

From ecology to atmospheric sciences, many academic disciplines deal with data
characterized by intricate spatio-temporal complexities, the modeling of which often requires …

Metropolitan phenomena are often characterised by spatial structuring and arrangement.
The term 'urban', understood as a “fabric in which […] sociocultural and politicaleconomic …

Few laws about geographical information are known, partly because geographical
information is inherently complex. Tobler's first law of Geography and, to a lesser degree …

Accurate detection of urban changes is critical for guiding city planning that leads to smart
and sustainable development. Few existing methods are able to detect urban changes in a …

Geosocial media feeds have become an important part of the geographical and GIScience
research landscapes, and have led to insights in different areas that often could not be …

The lack of a well‐established and unified place theory across disciplines is decelerating its
formalization, evolution, and especially its pragmatic implications and applicability. In this …

The measurement of spatial dependence within a set of observations or the residuals from a
regression is one of the most common operations within spatial analysis. However, there …