The second mortality estimation method, called the Geographically Weighted Method (GWM), takes the geographical distributions of ethnic groups as measured in the 2001 Census and local area mortality rates to produce a weighted national average for ethnic mortality rates.
DFLE and HLE by age and gender for five-year age groups were computed for 16 ethnic groups by combining the 2001 Census data on ethnicity, self-reported limiting long-term illness and self-rated health using mortality by ethnic group estimated by two methods: the Standardised Illness Ratio (SIR) method and the Geographically Weighted Method (GWM).
A geographically weighted regression model provided useful insights about localization of effects.
Our research utilizes a geographically weighted regression (GWR) model to explore the relationships between crashes and the built environment in the context of the Detroit region in Michigan.
This study demonstrates the use of a geographically weighted principal components analysis (GWPCA) of remote sensing imagery to improve land cover classification accuracy.
In this study, we applied a geographically weighted logistic regression (GWR) on a large area (whole of France) to explore spatial variations of the relations between five environmental variables and walking for leisure and errands purposes among 40,480 French adults.
Here we proposed a geographically weighted temporally correlated logistic regression (GWTCLR) model to identify such dynamic correlation of predictors on binomial outcome data, by incorporating spatial and temporal information for joint inference.
Using a series of Monte Carlo experiments, we demonstrate how extending a geographically weighted likelihood regression (GWLR) model to account for temporal heterogeneity can improve the performance of the model when heterogeneity exists in the spatial and temporal dimensions.
In this research, we have introduced a geographically weighted regression (GWR) model supported by a GIS in order to build location-dependent regression models.
Furthermore, a geographically weighted regression model was used to explore the local spatial heterogeneity in the relationships.
Wang used a susceptible restore transmission model [ 11], Hii used a Poisson regression model [ 7], and Hu used a geographically weighted regression model [ 12].
