There has been a growing interest in using local statistics to identify spatial or spatiotemporal association patterns among georeferenced data, in which the null distributions of the statistics play a key role for the confirmatory inferences. In this study we focus on a generic form of local...

This paper presents a case study of recovering the waste heat of the exhaust flue gas before entering a flue gas desulphurizer (FGD) in a 600 MW power plant. This waste heat can be recovered by installing a low pressure economizer (LPE) to heat the condensed water which can save the steam...

This article focuses on the estimation of the parametric component, which is of primary interest, in semi-varying coefficient models with heteroscedastic errors. Specifically, we first present a procedure for estimating the variance function of the error term and the resulting estimator is...

Geographically weighted regression (GWR), as a useful method for exploring spatial nonstationarity of a regression relationship, has been applied to a variety of areas. In this method a spatially varying coefficient model is locally calibrated and the spatial-variation patterns of the locally...

Geographically weighted regression (GWR) is a way of exploring spatial nonstationarity by calibrating a multiple regression model which allows different relationships to exist at different points in space. Nevertheless, formal testing procedures for spatial nonstationarity have not been...

In recent years, there has been a growing interest in the use of local measures such as Anselin's LISAs and Ord and Getis <i>G </i>statistics to identify local patterns of spatial association. The statistical significance test based on local statistics is one of the most important aspects in performing...

A mixed geographically weighted regression (MGWR) model is a kind of regression model in which some coefficients of the explanatory variables are constant, but others vary spatially. It is a useful statistical modelling tool in a number of areas of spatial data analysis. After an MGWR model is...

A mixed, geographically weighted regression (GWR) model is useful in the situation where certain explanatory variables influencing the response are global while others are local. Undoubtedly, how to identify these two types of the explanatory variables is essential for building such a model....

Geographically weighted regression (GWR) is a useful technique for exploring spatial nonstationarity by calibrating, for example, a regression model which allows different relationships to exist at different points in space. In this line of research, many spatial data sets have been successfully...

In the framework of the geographically weighted regression technique, spatial homoscedasticity of the model error term is a common assumption when a spatially varying coefficient model is calibrated to explore spatial nonstationarity of the regression relationship. In many real-world problems,...