Geographically weighted regression (GWR) is a spatial statistical technique that recognizes
that traditional 'global'regression models may be limited when spatial processes vary with …

Global warming is forcing many species to shift their distributions upward, causing
consequent changes in the compositions of species that occur at specific locations. This …

Key features: Unique in its combination of serving as an introduction to spatial statistics and
to modeling agricultural and ecological data using R Provides exercises in each chapter to …

Urban tree canopies are fundamental to mitigating the impacts of climate change within
cities as well as providing a range of other important ecosystem, health, and amenity …

Aim Marine macroalgae provide an excellent opportunity to test hypotheses about latitudinal
diversity gradients because macroalgal richness decreases towards the tropics, contrary to …

In regression models for spatial data, it is often assumed that the marginal effects of
covariates on the response are constant over space. In practice, this assumption might often …

By the end of the twentieth century, a scholarly consensus emerged around the idea that oil
fuels authoritarianism and slow growth. The natural abundance once thought to be a …

As an established spatial analytical tool, Geographically Weighted Regression (GWR) has
been applied across a variety of disciplines. However, its usage can be challenging for large …

Formerly, tree height has been more difficult to measure accurately in the field than tree
diameter at breast height. As a consequence, models to predict height from diameter …

While significant progress has been made towards explaining black-box machine-learning
(ML) models, there is still a distinct lack of diagnostic tools that elucidate the spatial …