Thematic Attributes and Classification

This lecture introduces the background notions of statistical thematic mapping, different types of thematic maps, and discretization methods. It covers quantitative and qualitative variables, absolute and relative quantitative data, proportional symbols in maps, and the process of choosing a classification scheme. The instructor explains how to divide data into classes, preserve hierarchy, and consider various factors for quantitative variables. Different discretization methods like Standard Deviation, Equal Interval, Natural Breaks, and Quantiles are discussed, emphasizing the importance of justifiable class choices. The lecture concludes with a summary of key concepts and the use of Gini Coefficients to measure inequality in populations.