Spatial statistics is a growing discipline providing important analytical techniques in a wide
range of disciplines in the natural and social sciences. In the R package GWmodel, we …

In this study, we present a collection of local models, termed geographically weighted (GW)
models, which can be found within the GWmodel R package. A GW model suits situations …

The coastal shallow water zone can be a challenging and costly environment in which to
acquire bathymetry and other oceanographic data using traditional survey methods. Much of …

Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of
social and environmental data. It allows spatial heterogeneities in processes and …

In the context of climate change and rapid urbanization, urban pluvial floods pose an
increasing threat to human wellbeing and security in the cities of China. A valuable aid to …

This study estimates the environmental Kuznets curve (EKC) relationship at the province
level in China. We apply empirical methods to test three industrial pollutants—SO 2 …

Water poverty, defined as insufficient water of adequate quality to cover basic needs, is an
issue that may manifest itself in multiple ways. Extreme seasonal variation in water …

This study demonstrates the use of a geographically weighted principal components
analysis (GWPCA) of remote sensing imagery to improve land cover classification accuracy …

A new method is developed to design a multi-objective and multi-pollutant sensitive air
quality monitoring network (AQMN) for an industrial district. A dispersion model is employed …

The spatial characteristics of geochemical patterns are significant for mineral exploration
and environmental studies. In this contribution, geographically weighted means, coefficients …