Chapter 14 Reduce Items and Attributes Vis/Visual Analytics, Chap 14 Reduce 1 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
The Big Picture • Datasets are large and complex – Showing everything in a view visual clutter – There are five options for handling complexity • Change view over time – most obvious, most popular, and most flexible one • Derive new data (chap 4) • Facet into multiple views (chap. 13) • Reduce items and attributes (chap 14) • Embed: Focus + Context in a single view (chap 15) – Are not mutually exclusive, and various combinations of them are common Vis/Visual Analytics, Chap 14 Reduce 2 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
The Big Picture • Reduce what is shown at once within a view – Filtering – eliminate elements • Items or attributes – Aggregate – combines many together • Items or attributes Vis/Visual Analytics, Chap 14 Reduce 3 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
The Big Picture Vis/Visual Analytics, Chap 14 Reduce 4 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Why Reduce? • Reducing the amount of data shown in a view – An obvious way to reduce its visual complexity – The challenge is to minimize the chance that information important to the task is hidden • Filtering simply eliminate elements • Aggregation – Creates a single new element that stands in for multiple others that it replace Vis/Visual Analytics, Chap 14 Reduce 5 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Why Reduce? • Trade-off between filtering and aggregation – Filtering is very straightforward for users to understand and to compute • People tend to have “out of sight, out of mind” mentally about missing information – Aggregation can be somewhat safer cognitively • The stand-in element is designed to convey information about the entire set of information it replaces • It cannot convey all omitted information. The challenge is how and what to summarize in a way that match well with the dataset and task Vis/Visual Analytics, Chap 14 Reduce 6 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Filter • Applied to both items and attributes – Challenge come in designing a vis system where filtering can be used to effectively explore a dataset • To do filtering, users need to select a “range”, the problem is that users do not know the dataset yet • In an interactive vis context, filtering is often accomplished through dynamic queries – There is a tightly coupled loop between visual encoding and interaction, so that users can immediately see the result of the intervention – Often the filtering is standard GUI widgets » Sliders, buttons, comboboxes, text fields Vis/Visual Analytics, Chap 14 Reduce 7 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Filter Item Filtering • To eliminate items based on their values w.r.t specific attributes – Fewer items are shown, # of attributes does not change • Ex: FilmFinder – Data • A movie database: A table with 9 value attributes – Genre, year made, title, actors, actresses, directors, rating, popularity, length – Encoding • Interactive scatterplot – Items are movies color coded by genre – Axes: year made vs. popularity Vis/Visual Analytics, Chap 14 Reduce 8 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Filter Item Filtering • Ex: FilmFinder – Filtering • Dual slider – select both a minimum and a maximum • Several alpha sliders – tuned for selection with text strings – Display • Multiform overview-detail views Vis/Visual Analytics, Chap 14 Reduce 9 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Filter Item Filtering FilmFinder features tightly coupled interactive filtering, where the result of moving sliders and pressing buttons is immediately reflected in the visual encoding. (a) Exploration begins with an overview of all movies in the dataset. (b) Moving the actor slider to select Sean Connery filters out most of the other movies, leaving enough room to draw labels. (c) Clicking on the mark representing a movie brings up a detail view. Vis/Visual Analytics, Chap 14 Reduce 10 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Filter Attribute Filtering • To eliminate attributes – To show the same # of items, but fewer attributes for each item – Often used in conjunction with attribute ordering • If attributes can be ordered according to a derived attribute that measures the similarity between them, all of the high-scoring attributes or lower-scoring attributes can be filtered out • Item filtering and attribute filtering can be combined Vis/Visual Analytics, Chap 14 Reduce 11 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Filter Attribute Filtering Ex: DOSFA • Attribute filtering with attribute ordering • Dataset – 215 attributes, representing word counts – 298 points representing documents • Encoding – Star plots • Original star plots are so densely packed that little structure can be seen • After attribute ordering and filtering, the star plots show clear pattern Vis/Visual Analytics, Chap 14 Reduce 12 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Filter Attribute Filtering The DOSFA idiom shown on star glyphs with a medical records dataset of 215 dimensions and 298 points. (a) The full dataset is so dense that patterns cannot be seen. (b) After ordering on similarity and filtering on both similarity and importance, the star glyphs show structure. Vis/Visual Analytics, Chap 14 Reduce 13 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Filter Attribute Filtering Vis/Visual Analytics, Chap 14 Reduce 14 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Aggregate • A group of elements is represented by a new derived element that stand in for the entire group – Typically involves the use of a derived attribute • Simple Ex: average, minimum, maximum, count, sum, but rarely an adequate solution! – Are rarely good solutions! – Challenge: avoid eliminating the interesting signal in the process of summarization – A powerful design choice, particularly when used within interactive idiom • Change the level of aggregation on the fly to inspect the dataset at different levels of detail Vis/Visual Analytics, Chap 14 Reduce 15 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Aggregate Different data with same mean, variance, and correlation Dataset 1: ok Dataset 2: nonlinear Dataset 3: a single outlier leads to a misleading regression line! Dataset 4: dramatically mislead! Vis/Visual Analytics, Chap 14 Reduce 16 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Aggregate Item Aggregation • The most straightforward use of item aggregation is within static visual encoding idioms – Its full power and flexibility can be harnessed by interactive idioms where the view dynamically changes • Examples – Histograms – Continuous scatterplots – Boxplot charts – SolarPlot – Hierarchical parallel coordinates Vis/Visual Analytics, Chap 14 Reduce 17 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Aggregate Item Aggregation • Histograms – Shows the distribution of items within an original attribute • EX. Shows the distribution of weights for all the cats in a neighborhood, binned into 5-pound blocks – Compared to bar charts • Histogram can be continuous • Histograms do not show the original table directly – Is an aggregation idiom that shows a derived table that is more concise than the original dataset • The choice of bin size is crucial and tricky – Compute the # of bins based on the dataset characteristics – User-controlled + interactivity; to see how the histogram changes Vis/Visual Analytics, Chap 14 Reduce 18 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Aggregate Item Aggregation The histogram idiom aggregates an arbitrary number of items into a concise representation of their distribution. Vis/Visual Analytics, Chap 14 Reduce 19 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Aggregate Item Aggregation Vis/Visual Analytics, Chap 14 Reduce 20 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Aggregate Item Aggregation • Continuous scatterplots – Solve the occlusion problem by plotting an aggregate value rather than drawing every single item as an individual point » Size coding and text labels exacerbates the problem • Use color coding at each pixel to indicate the density of overplotting, often with transparency – Use an aggregate derived attribute: overplot density • Ex: Dataset: tornado air-flow dataset – Horizontal axis: magnitude of the velocity – Vertical axis: z-direction velocity – The density is shown with a log-scale sequential colormap with monotonically increasing luminance » Starts with dark blue at the low end, continuous with reds, and then yellows and whites at the high end Vis/Visual Analytics, Chap 14 Reduce 21 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Aggregate Item Aggregation The continuous scatterplot idiom uses color to show the density at each location, solving the problem of occlusion from overplotting and allowing scalability to large datasets. Vis/Visual Analytics, Chap 14 Reduce 22 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Aggregate Item Aggregation Vis/Visual Analytics, Chap 14 Reduce 23 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
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