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

The areal modeling of the extremes of a natural process such as rainfall or temperature is
important in environmental statistics; for example, understanding extreme areal rainfall is …

We describe the construction of a new version of the Europe‐wide E‐OBS temperature
(daily minimum, mean, and maximum values) and precipitation data set. This version …

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

Modeling spatial and spatio-temporal continuous processes is an important and challenging
problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential …

The Gaussian process is an indispensable tool for spatial data analysts. The onset of the
“big data” era, however, has lead to the traditional Gaussian process being computationally …

China has recently made available hourly air pollution data from over 1500 sites, including
airborne particulate matter (PM), SO2, NO2, and O3. We apply Kriging interpolation to four …

城市空间POI 点的分布模式, 分布密度在基础设施规划, 城市空间分析中具有重要意义,
表达该特征的核密度法(kernel density estimation) 由于顾及了地理学第一定律的区位影响 …

Spatial process models for analyzing geostatistical data entail computations that become
prohibitive as the number of spatial locations become large. This article develops a class of …

Thermal regimes are fundamental determinants of aquatic ecosystems, which makes
description and prediction of temperatures critical during a period of rapid global change …