Add to ORKGWe investigate the issue of collinearity in data when using Geographically Weighted Regression to explore spatial variation in data sets – and show how the ideas of condition numbers and variance inflation factors may be `localised’ to detect and respond to problems caused by this phenomenon
This paper describes preliminary work analysing the stability of parameter coefficient estimates for...
Survey data are often used to fit models. The values of covariates used in modeling are not controll...
Under the realization that Geographically Weighted Regression (GWR) is a data-borrowing technique, t...
We investigate the issue of collinearity in data when using Geographically Weighted Regression to e...
This paper explores the impact of different distance metrics on collinearity in local regression mod...
Despite the growing ubiquity of sensor deployments and the advances in sensor data analysis technol...
This paper develops a localized approach to elastic net logistic regression, extending previous rese...
In fitting regression models with spatial data, it is often assumed that the relationships between t...
This text is written as a follow-up to a two-day workshop on Geographically Weighted Regression (GWR...
In this study, we present a collection of local models, termed geographically weighted (GW) models,...
In this study, we link and compare the geographically weighted regression (GWR) model with the kri...
abstract: Geographically Weighted Regression (GWR) has been broadly used in various fields to model...
In the field of spatial analysis, the interest of some researchers in modeling relationships between...
Spatial nonstationarity is a condition in which a simple ‘global” model cannot explain the relations...
Geographically weighted regression (Fotheringham et al., 2002) is a method of modelling spatial var...
This paper describes preliminary work analysing the stability of parameter coefficient estimates for...
Survey data are often used to fit models. The values of covariates used in modeling are not controll...
Under the realization that Geographically Weighted Regression (GWR) is a data-borrowing technique, t...
We investigate the issue of collinearity in data when using Geographically Weighted Regression to e...
This paper explores the impact of different distance metrics on collinearity in local regression mod...
Despite the growing ubiquity of sensor deployments and the advances in sensor data analysis technol...
This paper develops a localized approach to elastic net logistic regression, extending previous rese...
In fitting regression models with spatial data, it is often assumed that the relationships between t...
This text is written as a follow-up to a two-day workshop on Geographically Weighted Regression (GWR...
In this study, we present a collection of local models, termed geographically weighted (GW) models,...
In this study, we link and compare the geographically weighted regression (GWR) model with the kri...
abstract: Geographically Weighted Regression (GWR) has been broadly used in various fields to model...
In the field of spatial analysis, the interest of some researchers in modeling relationships between...
Spatial nonstationarity is a condition in which a simple ‘global” model cannot explain the relations...
Geographically weighted regression (Fotheringham et al., 2002) is a method of modelling spatial var...
This paper describes preliminary work analysing the stability of parameter coefficient estimates for...
Survey data are often used to fit models. The values of covariates used in modeling are not controll...
Under the realization that Geographically Weighted Regression (GWR) is a data-borrowing technique, t...