Geographically Weighted Discriminant Analysis

Boosting Weighted Linear Discriminant Analysis

We propose a novel approach to boosting weighted linear discriminant analysis (LDA) as a weak classifier. Combining Adaboost with LDA allows to select the most relevant features for classification at each boosting iteration, thus benefiting from feature correlation. The advantages of this approach include the use of a smaller number of weak learners to achieve a low error rate, improved classif...

Fuzzy Geographically Weighted Clustering

Geodemographic analysis has been described as “the analysis of spatially referenced geodemographic and lifestyle data” (See and Openshaw, 2001, p.269) It is widely used in the public and private sectors for the planning and provision of products and services. Geodemographic analysis often uses clustering techniques which are used to classify the geodemographic data into groups, making the data ...

C.5 Geographically Weighted Regression

Geographically weighted regression (GWR) was introduced to the geography literature by Brunsdon et al. (1996) to study the potential for relationships in a regression model to vary in geographical space, or what is termed parametric nonstationarity. GWR is based on the non-parametric technique of locally weighted regression developed in statistics for curve-fitting and smoothing applications, w...

Geographically Weighted Regression White Paper

Geographically Weighted Regression the analysis of spatially varying relationships