<Ol> <Li> Since information is concentrated on the first new factors, it is possible to keep only a few of them while losing only a small amount of information; mapping them produces fewer and more significant maps </Li> <Li> The factors, actually the eigenvectors, are orthogonal by construction, i.e. not correlated . In most cases, the dominant factor (with the largest eigenvalue) is the Social Component, separating rich and poor in the city . Since factors are not - correlated, other smaller processes than social status, which would have remained hidden otherwise, appear on the second, third,...factors . </Li> </Ol> <Li> Since information is concentrated on the first new factors, it is possible to keep only a few of them while losing only a small amount of information; mapping them produces fewer and more significant maps </Li> <Li> The factors, actually the eigenvectors, are orthogonal by construction, i.e. not correlated . In most cases, the dominant factor (with the largest eigenvalue) is the Social Component, separating rich and poor in the city . Since factors are not - correlated, other smaller processes than social status, which would have remained hidden otherwise, appear on the second, third,...factors . </Li> <P> Factor analysis depends on measuring distances between observations: the choice of a significant metric is crucial . The Euclidean metric (Principal Component Analysis), the Chi - Square distance (Correspondence Analysis) or the Generalized Mahalanobis distance (Discriminant Analysis) are among the more widely used . More complicated models, using communalities or rotations have been proposed . </P>

Which of the following variables is not a topic that geographer's study