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CIS 4930/6930-902 SCIENTIFIC VISUALIZATION FILTERING & AGGRICATION Paul Rosen Assistant Professor University of South Florida slides credits Miriah Meyer (U of Utah)

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CIS 4930/6930-902SCIENTIFICVISUALIZATION

FILTERING & AGGRICATIONPaul RosenAssistant ProfessorUniversity of South Florida

slides credits Miriah Meyer (U of Utah)

•ADMINISTRATIVE

•project 2 due today•project 3 posted

•project 3 presentations Thursday

•TODAY• filtering & aggregation

•WHY REDUCE?

•FILTER• elements are eliminated

• dynamic queries•coupling between encoding and

interaction so that user can immediately see the results of an action

Willett 2007

•SCENTED WIDGETS

•information scent: user gets sense of data•GOAL: lower the cost of information forging

through better cues

•INTERACTIVE LEGENDS

•controls combining the visual representation of static legends with interaction mechanisms of widgets•define and control visual display together

Riche 2010

Wang 2003

•ATTRIBUTE FILTERING

•AGGREGATE• a group of elements is

represented by a new derived element that stands

in for the entire group

score

num

ber o

f stu

dent

shistogram

•ITEM AGGREGATION

Bachthaler 2008

continuous scatterplot

•ITEM AGGREGATION

hierarchical parallel coordinates

Fua 1999

•ITEM AGGREGATION

box plot

•ITEM AGGREGATION

•SPATIAL AGGREGATION

•MODIFIABLE AREAL UNIT PROBLEM

•in cartography, changing the boundaries of the regions used to analyze data can

yield dramatically different results

•CONGRESSIONAL DISTRICTS

2014 Proposed 2015

•ATTRIBUTE AGGREGATION

•group attributes and compute a similarity score across the set

•dimensionality reduction to preserve meaningful structure

•SIMILARITY SCORES

•correlation• measure of similarity between 2 or more

attributes• many variants—pearson, rank, multi-way, etc.

•regression• fit a model to the data

• measure the quality of fit (i.e. R2)

Ingram 2009

•DIMENSIONALITY REDUCTION

• Linear methods (e.g. PCA)• linear combination of orthogonal basis vectors

• new dimensions created in order of maximum variance

• Nonlinear methods (e.g. MDS)• maximize differences in distances from high dim

space in the low dim space

•END OF FOUNDATIONS!