Opening the London Underground to visually handicapped tourists

Abstract

There are several practical rules for determining categories (class intervals) for maps representing statistical data, like arithmetic, geometric or equal steps etc. In this paper, however a coherent method is proposed to provide statistically separable Classes on a map with minimum redundancy in terms of information content.

The number of class intervals can be directly computed by means of appropriate statistical methods if the widths of classes are determined by t-test, i.e. when their difference is significant at a high level of confidence. A class narrower than this width would represent data in different categories due only to variance, however, the selection of wider classes leads to a certain loss of information.

The class intervals determined this way should be positioned on the statistical data-set so that each category contains approximately equal number of data providing maximum information content of the output map. At the final step the class intervals derived this way should be rounded, if necessary, to provide user-friendly maps.