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Utilize small multiples for comparative analysis from "summary" of The Visual Display of Quantitative Information by Edward R. Tufte
Small multiples, a concept introduced by Jacques Bertin in the 1960s, involve the creation of a series of small, similar graphics, each showing a different subset of the data. By using small multiples, the viewer can easily compare and contrast various aspects of the data across multiple dimensions. This technique is particularly useful for revealing patterns, trends, and anomalies that may not be immediately apparent in a single, large graphic. One of the key benefits of utilizing small multiples for comparative analysis is the ability to maintain a consistent scale and format across all the individual graphics. This consistency allows the viewer to make accurate comparisons without being misled by differences in axes or visual elements. Additionally, small multiples enable the viewer to quickly scan through a series of graphics, absorbing information at a glance and identifying trends or outliers. In essence, small multiples offer a way to break down complex data sets into more digestible chunks, making it easier for the viewer to extract meaningful insights. By presenting multiple views of the same data in a structured and systematic manner, small multiples help the viewer to identify relationships, patterns, and variations that might otherwise go unnoticed. This approach enhances the viewer's ability to analyze and interpret the data effectively, leading to more informed decision-making and deeper understanding.- The concept of small multiples for comparative analysis is a powerful tool for visually displaying quantitative information in a clear and insightful manner. By leveraging the benefits of repetition, consistency, and systematic organization, small multiples enable the viewer to explore complex data sets with ease and efficiency. Through the use of small multiples, designers and analysts can unlock the full potential of their data, revealing hidden insights and facilitating a deeper understanding of the underlying patterns and trends.
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