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Module 1: Data VisualizationHow to use visualization well?-Data specifics of what data are available-Audience who will be using this?-Messagewhat do you want the audience to see?Types of Visualization1) Exploratory a.Where data analysis begins – get an understanding of the datab.Discoveries arise from data patternsi.Difficult without graphical techniquesii.Visualizations draw out subtle patternsc.“impressive techniques” should not distract from the datad.Audience is me– make sure it makes sense, made quickly and do many at a time2) Explanatory a.Draft … analyze … rewrite b.Clean and clear – tell a story, mind messageand audiencec.Go through many drafts of visualizationModule 1: Mapping and PerceptionVariables1)Nominal (discreate, qualitative, categorial)a. Ex.Variable with specific setof variables such as animals dog, cat, pigb.You can compare(equal, less than, greater than)2)Ordinal a.It has an order to it b.You can compare (equal, less than, greater than)c.You can subtract d.Nodivisione.Ex. Months Jan, Feb, … etc. i.You can write them as numbers, but they are not numbers 3)Numerical (quantitative, continuous, ratio scale) a.Ex. 1234557689 etc. b.Variable can have any number assigned to it c.You can compare, subtract, and dividePre-attentive attributes-Visual cues that are visually apparent very quickly (~200ms) and do not require intentional processing to see
Reasons to use data visualization-Take advantage of human visual processing machinery for insight generation-Can get high information density in an image-Conveys patterns that may be difficult to describe Representation-Discrete mapping identity mapping-Continuous mapping Magnitude mappingColor Subtlety -Valueobrightness, amount of light coming through or reflecting oLuminance (white to black) oSaturation -ColoroHueGestalt Psychology -Our brains are wired for certain types of patterns and this affects howwe see the world
-Human brain finds structure in visual stimuli 1)Law of proximity oThings that are close together, get paired together Vertical Lines and horizontal lines 2)Law of Similarity oShades or shaped 3)Connectedness and Containment can OverruleDescription of Mapping:Channel interference: Weber’s Law-Relative nature of our perception – Human perception works by percentage increase -Perception is not simply on absolute terms, but we perceive differences as a function of the magnitude of the original (i.e. we perceive percentage changes)
Steven’s Power Law-Stimulus = Intensity of actual stimulus ^ n (length) -Perception of stimuli is not linear -Shows relationship between actual and perceived value ** Major differences in the character of datasets can be invisible to summary statistics **Module 2: Hierarchical DataHierarchical Data-Has top, middle, and bottom sections like a “pyramid” where it has subdivisions and main categories -Usually represented in a table it contains the main category, sub category, sub sub category, etc.