SIMS 247 Information Visualization and Presentation

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SIMS 247Information Visualization and
Presentation

• Marti Hearst
• March 15, 2002

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Outline

• Why Text is Tough
• Visualizing Concept Spaces
• Clusters
• Category Hierarchies
• Visualizing Query Specifications
• Visualizing Retrieval Results
• Usability Study Meta-Analysis

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Why Visualize Text?

• To help with Information Retrieval
• give an overview of a collection
• show user what aspects of their interests are
  present in a collection
• help user understand why documents retrieved as a
  result of a query
• Text Data Mining
• Mainly clustering nodes-and-links
• Software Engineering
• not really text, but has some similar properties

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Why Text is Tough

• Text is not pre-attentive
• Text consists of abstract concepts
• which are difficult to visualize
• Text represents similar concepts in many
  different ways
• space ship, flying saucer, UFO, figment of
  imagination
• Text has very high dimensionality
• Tens or hundreds of thousands of features
• Many subsets can be combined together

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Why Text is Tough
The Dog.
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Why Text is Tough
The Dog.
The dog cavorts.
The dog cavorted.
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Why Text is Tough
The man.
The man walks.
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Why Text is Tough
The man walks the cavorting dog.
So far, we can sort of show this in pictures.
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Why Text is Tough
As the man walks the cavorting dog,
thoughts arrive unbidden of the previous spring,
so unlike this one, in which walking was marching
and dogs were baleful sentinals outside unjust
halls.
How do we visualize this?
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Why Text is Tough

• Abstract concepts are difficult to visualize
• Combinations of abstract concepts are even more
  difficult to visualize
• time
• shades of meaning
• social and psychological concepts
• causal relationships

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Why Text is Tough

• Language only hints at meaning
• Most meaning of text lies within our minds and
  common understanding
• How much is that doggy in the window?
• how much social system of barter and trade (not
  the size of the dog)
• doggy implies childlike, plaintive, probably
  cannot do the purchasing on their own
• in the window implies behind a store window,
  not really inside a window, requires notion of
  window shopping

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Why Text is Tough

• General categories have no standard ordering
  (nominal data)
• Categorization of documents by single topics
  misses important distinctions
• Consider an article about
• NAFTA
• The effects of NAFTA on truck manufacture
• The effects of NAFTA on productivity of truck
  manufacture in the neighboring cities of El Paso
  and Juarez

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Why Text is Tough

• Other issues about language
• ambiguous (many different meanings for the same
  words and phrases)
• different combinations imply different meanings

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Why Text is Tough

• I saw Pathfinder on Mars with a telescope.
• Pathfinder photographed Mars.
• The Pathfinder photograph mars our perception of
  a lifeless planet.
• The Pathfinder photograph from Ford has arrived.
• The Pathfinder forded the river without marring
  its paint job.

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Why Text is Easy

• Text is highly redundant
• When you have lots of it
• Pretty much any simple technique can pull out
  phrases that seem to characterize a document
• Instant summary
• Extract the most frequent words from a text
• Remove the most common English words

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Guess the Text

• 478 said
• 233 god
• 201 father
• 187 land
• 181 jacob
• 160 son
• 157 joseph
• 134 abraham
• 121 earth
• 119 man
• 118 behold
• 113 years
• 104 wife
• 101 name
• 94 pharaoh

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Text Collection Overviews

• How can we show an overview of the contents of a
  text collection?
• Show info external to the docs
• e.g., date, author, source, number of inlinks
• does not show what they are about
• Show the meanings or topics in the docs
• a list of titles
• results of clustering words or documents
• organize according to categories (next time)

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Clustering for Collection Overviews

• Scatter/Gather
• show main themes as groups of text summaries
• Scatter Plots
• show docs as points closeness indicates nearness
  in cluster space
• show main themes of docs as visual clumps or
  mountains
• Kohonen Feature maps
• show main themes as adjacent polygons
• BEAD
• show main themes as links within a force-directed
  placement network

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Clustering for Collection Overviews

• Two main steps
• cluster the documents according to the words they
  have in common
• map the cluster representation onto a
  (interactive) 2D or 3D representation

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Text Clustering

• Finds overall similarities among groups of
  documents
• Finds overall similarities among groups of tokens
• Picks out some themes, ignores others

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Scatter/Gather
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S/G Example query on star

• Encyclopedia text
• 14 sports
• 8 symbols 47 film, tv
• 68 film, tv (p) 7 music
• 97 astrophysics
• 67 astronomy(p) 12 steller phenomena
• 10 flora/fauna 49 galaxies, stars
• 29 constellations
• 7 miscelleneous
• Clustering and re-clustering is entirely
  automated

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Scatter/Gather

• Cutting, Pedersen, Tukey Karger 92, 93, Hearst
  Pedersen 95
• How it works
• Cluster sets of documents into general themes,
  like a table of contents
• Display the contents of the clusters by showing
  topical terms and typical titles
• User chooses subsets of the clusters and
  re-clusters the documents within
• Resulting new groups have different themes
• Originally used to give collection overview
• Evidence suggests more appropriate for displaying
  retrieval results in context
• Appearing (sort-of) in commercial systems

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Northern Light Web Search Started out with
clustering. Then integrated with categories.
Now does not do web search and uses only
categories.
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Teoma appears to combine categories and clusters
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Scatter Plot of Clusters(Chen et al. 97)
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BEAD (Chalmers 97)
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BEAD (Chalmers 96)
An example layout produced by Bead, seen in
overview, of 831 bibliography entries. The
dimensionality (the number of unique words in
the set) is 6925. A search for cscw or
collaborative shows the pattern of occurrences
coloured dark blue, mostly to the right. The
central rectangle is the visualizers motion
control.
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Example Themescapes(Wise et al. 95)
Themescapes (Wise et al. 95)
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Clustering for Collection Overviews

• Since text has tens of thousands of features
• the mapping to 2D loses a tremendous amount of
  information
• only very coarse themes are detected

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Galaxy of News Rennison 95
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Galaxy of News Rennison 95
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Kohonen Feature Maps(Lin 92, Chen et al. 97)
(594 docs)
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Study of Kohonen Feature Maps

• H. Chen, A. Houston, R. Sewell, and B. Schatz,
  JASIS 49(7)
• Comparison Kohonen Map and Yahoo
• Task
• Window shop for interesting home page
• Repeat with other interface
• Results
• Starting with map could repeat in Yahoo (8/11)
• Starting with Yahoo unable to repeat in map (2/14)

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How Useful is Collection Cluster Visualization
for Search?

• Three studies find negative results

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Study 1

• Kleiboemer, Lazear, and Pedersen. Tailoring a
  retrieval system for naive users. In Proc. of
  the 5th Annual Symposium on Document Analysis and
  Information Retrieval, 1996
• This study compared
• a system with 2D graphical clusters
• a system with 3D graphical clusters
• a system that shows textual clusters
• Novice users
• Only textual clusters were helpful (and they were
  difficult to use well)

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Study 2 Kohonen Feature Maps

• H. Chen, A. Houston, R. Sewell, and B. Schatz,
  JASIS 49(7)
• Comparison Kohonen Map and Yahoo
• Task
• Window shop for interesting home page
• Repeat with other interface
• Results
• Starting with map could repeat in Yahoo (8/11)
• Starting with Yahoo unable to repeat in map (2/14)

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Study 2 (cont.)

• Participants liked
• Correspondence of region size to documents
• Overview (but also wanted zoom)
• Ease of jumping from one topic to another
• Multiple routes to topics
• Use of category and subcategory labels

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Study 2 (cont.)

• Participants wanted
• hierarchical organization
• other ordering of concepts (alphabetical)
• integration of browsing and search
• correspondence of color to meaning
• more meaningful labels
• labels at same level of abstraction
• fit more labels in the given space
• combined keyword and category search
• multiple category assignment (sportsentertain)

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Study 3 NIRVE

• NIRVE Interface by Cugini et al. 96. Each
  rectangle is a cluster. Larger clusters closer
  to the pole. Similar clusters near one
  another. Opening a cluster causes a projection
  that shows the titles.

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Study 3

• Visualization of search results a comparative
  evaluation of text, 2D, and 3D interfaces
  Sebrechts, Cugini, Laskowski, Vasilakis and
  Miller, Proceedings of SIGIR 99, Berkeley, CA,
  1999.
• This study compared
• 3D graphical clusters
• 2D graphical clusters
• textual clusters
• 15 participants, between-subject design
• Tasks
• Locate a particular document
• Locate and mark a particular document
• Locate a previously marked document
• Locate all clusters that discuss some topic
• List more frequently represented topics

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Study 3

• Results (time to locate targets)
• Text clusters fastest
• 2D next
• 3D last
• With practice (6 sessions) 2D neared text
  results 3D still slower
• Computer experts were just as fast with 3D
• Certain tasks equally fast with 2D text
• Find particular cluster
• Find an already-marked document
• But anything involving text (e.g., find title)
  much faster with text.
• Spatial location rotated, so users lost context
• Helpful viz features
• Color coding (helped text too)
• Relative vertical locations

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Visualizing Clusters

• Huge 2D maps may be inappropriate focus for
  information retrieval
• cannot see what the documents are about
• space is difficult to browse for IR purposes
• (tough to visualize abstract concepts)
• Perhaps more suited for pattern discovery and
  gist-like overviews

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Co-Citation Analysis

• Has been around since the 50s. (Small, Garfield,
  White McCain)
• Used to identify core sets of
• authors, journals, articles for particular fields
  
• Not for general search
• Main Idea
• Find pairs of papers that cite third papers
• Look for commonalitieis
• A nice demonstration by Eugene Garfield at
• http//165.123.33.33/eugene_garfield/papers/mapsci
  world.html

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Co-citation analysis (From Garfield 98)
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Co-citation analysis (From Garfield 98)
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Co-citation analysis (From Garfield 98)
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Category Combinations

• Lets show categories instead of clusters

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DynaCat (Pratt, Hearst, Fagan 99)
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DynaCat (Pratt 97)

• Decide on important question types in an advance
• What are the adverse effects of drug D?
• What is the prognosis for treatment T?
• Make use of MeSH categories
• Retain only those types of categories known to be
  useful for this type of query.

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DynaCat Study

• Design
• Three queries
• 24 cancer patients
• Compared three interfaces
• ranked list, clusters, categories
• Results
• Participants strongly preferred categories
• Participants found more answers using categories
• Participants took same amount of time with all
  three interfaces

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HiBrowse
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Category Combinations

• HiBrowse Problem
• Search is not integrated with browsing of
  categories
• Only see the subset of categories selected (and
  the corresponding number of documents)

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MultiTrees (Furnas Zacks 94)
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Cat-a-ConeMultiple Simultaneous Categories

• Key Ideas
• Separate documents from category labels
• Show both simultaneously
• Link the two for iterative feedback
• Distinguish between
• Searching for Documents vs.
• Searching for Categories

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Cat-a-Cone Interface
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Cat-a-Cone

• Catacomb
• (definition 2b, online Websters)
• A complex set of interrelated things
• Makes use of earlier PARC work on 3Danimation

Rooms Henderson and Card 86 IV Cone Tree
Robertson, Card, Mackinlay 93 Web Book Card,
Robertson, York 96
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search
browse
query terms
Category Hierarchy
Collection
Retrieved Documents
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ConeTree for Category Labels

• Browse/explore category hierarchy
• by search on label names
• by growing/shrinking subtrees
• by spinning subtrees
• Affordances
• learn meaning via ancestors, siblings
• disambiguate meanings
• all cats simultaneously viewable

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Virtual Book for Result Sets

• Categories on Page (Retrieved Document) linked to
  Categories in Tree
• Flipping through Book Pages causes some Subtrees
  to Expand and Contract
• Most Subtrees remain unchanged
• Book can be Stored for later Re-Use

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Improvements over Standard Category Interfaces

• Integrate category selection with viewing of
  categories
• Show all categories context
• Show relationship of retrieved documents to the
  category structure
• But do users understand and like the 3D?

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The FLAMENCO Project

• Basic idea similar to Cat-a-Cone
• But use familiar HTML interaction to achieve
  similar goals
• Usability results are very strong for users who
  care about the collection.

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Query Specification
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Command-Based Query Specification

• command attribute value connector
• find pa shneiderman and tw user
• What are the attribute names?
• What are the command names?
• What are allowable values?

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Form-Based Query Specification (Altavista)
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Form-Based Query Specification (Melvyl)
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Form-based Query Specification (Infoseek)
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Direct Manipulation Spec.VQUERY (Jones 98)
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Menu-based Query Specification(Young
Shneiderman 93)
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Context
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Putting Results in Context

• Visualizations of Query Term Distribution
• KWIC, TileBars, SeeSoft
• Visualizing Shared Subsets of Query Terms
• InfoCrystal, VIBE, Lattice Views
• Table of Contents as Context
• Superbook, Cha-Cha, DynaCat
• Organizing Results with Tables
• Envision, SenseMaker
• Using Hyperlinks
• WebCutter

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Putting Results in Context

• Interfaces should
• give hints about the roles terms play in the
  collection
• give hints about what will happen if various
  terms are combined
• show explicitly why documents are retrieved in
  response to the query
• summarize compactly the subset of interest

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KWIC (Keyword in Context)

• An old standard, ignored until recently by
  internet search engines
• used in some intranet engines, e.g., Cha-Cha

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Display of Retrieval Results

• Goal minimize time/effort for deciding which
  documents to examine in detail
• Idea show the roles of the query terms in the
  retrieved documents, making use of document
  structure

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TileBars

• Graphical Representation of Term Distribution and
  Overlap
• Simultaneously Indicate
• relative document length
• query term frequencies
• query term distributions
• query term overlap

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Example
Query terms What roles do they play in
retrieved documents?
DBMS (Database Systems) Reliability
Mainly about both DBMS reliability
Mainly about DBMS, discusses reliability
Mainly about, say, banking, with a subtopic
discussion on DBMS/Reliability
Mainly about high-tech layoffs
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Exploiting Visual Properties

• Variation in gray scale saturation imposes a
  universal, perceptual order (Bertin et al. 83)
• Varying shades of gray show varying quantities
  better than color (Tufte 83)
• Differences in shading should align with the
  values being presented (Kosslyn et al. 83)

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Key Aspect Faceted Queries

• Conjunct of disjuncts
• Each disjunct is a concept
• osteoporosis, bone loss
• prevention, cure
• research, Mayo clinic, study
• User does not have to specify which are main
  topics, which are subtopics
• Ranking algorithm gives higher weight to overlap
  of topics
• This kind of query works better at high-precision
  queries than similarity search (Hearst 95)

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TileBars Summary

• Preliminary User Studies
• users understand them
• find them helpful in some situations, but
  probably slower than just reading titles
• sometimes terms need to be disambiguated

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SeeSoft Showing Text Content using a linear
representation and brushing and linking (Eick
Wills 95)
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Query Term Subsets

• Show which subsets of query terms occur in
  which subsets of documents occurs in which
  subsets of retrieved documents

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Term Occurrences in Results Sets

• Show how often each query term occurs in
  retrieved documents
• VIBE (Korfhage 91)
• InfoCrystal (Spoerri 94)
• Problems
• cant see overlap of terms within docs
• quantities not represented graphically
• more than 4 terms hard to handle
• no help in selecting terms to begin with

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InfoCrystal (Spoerri 94)
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VIBE (Olson et al. 93, Korfhage 93)
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Term Occurrences in Results Sets

• Problems
• cant see overlap of terms within docs
• quantities not represented graphically
• more than 4 terms hard to handle
• no help in selecting terms to begin with

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DLITE (Cousins 97)

• Supporting the Information Seeking Process
• UI to a digital library
• Direct manipulation interface
• Workcenter approach
• experts create workcenters
• lots of tools for one task
• contents persistent

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DLITE (Cousins 97)

• Drag and Drop interface
• Reify queries, sources, retrieval results
• Animation to keep track of activity

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IR Infovis Meta-Analysis (Chen Yu 00)

• Goal
• Find invariant underlying relations suggested
  collectively by empirical findings from many
  different studies
• Procedure
• Examine the literature of empirical infoviz
  studies
• 35 studies between 1991 and 2000
• 27 focused on information retrieval tasks
• But due to wide differences in the conduct of the
  studies and the reporting of statistics, could
  use only 6 studies

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IR Infovis Meta-Analysis (Chen Yu 00)

• Conclusions
• IR Infoviz studies not reported in a standard
  format
• Individual cognitive differences had the largest
  effect
• Especially on accuracy
• Somewhat on efficiency
• Holding cognitive abilities constant, users did
  better with simpler visual-spatial interfaces
• The combined effect of visualization is not
  statistically significant
• Misc
• Tilebars and Scatter/Gather are well-known enough
  to not require citations!!