InfoVis:Wiki - User contributions [en]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 4

2009-01-07T23:43:12Z

UE-InfoVis0809 0425646:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe4.html Beschreibung der Aufgabe 4]

=== Gegebene Daten ===

'''Homer Simpson's Trinkverhalten in Abhängigkeit von seinen Lebensumständen''' 

...Visualisierung von Homer's Lebensabschnitten bzw. Ereignissen mit Einfluss auf sein
Trinkverhalten (zB.: Kindheit, Pubertät, Arbeitslosigkeit, Beziehungen, Hochzeit, Geburt
der Kinder, Liebeskummer, Alltag, etc.) von seiner Geburt bis Jetzt + mögliche
Zukunftsszenarien (mind. 3).
 
*Die Menge folgender Getränke soll für die jeweiligen Lebensumstände ablesbar sein
(ml oder Liter - je nachdem - pro Tag, Monat, Jahr (z.B.: Fokus+Kontext Methoden):
a) Wasser 
b) Milch 
c) Fruchtsaft 
d) Cola 
e) Kaffee (Würfelzucker?) 
f) Bier
(vereinfacht angenommen, Homer trinkt ausschließlich diese Getränke)
 
*Die folgenden Werte sollen abhängig von den konsumierten Getränken ablesbar sein:
1) g oder kg konsumierter Zucker (aus Getränken) + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 50g; enthaltener Zucker: 10g/100 ml Cola; 10g/100 ml Fruchtsaft; 3g/Würfelzucker). 
2) mg konsumiertes Coffein + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 600mg; enthaltenes Coffein: 10 mg/100 ml Cola; 80 mg/100 ml Kaffee). 
3) g konsumierter Alkohol + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 20g; enthaltener Alkohol: 3,6 g/100ml Bier)
 
*Die Daten sollen zur medizinischen/psychologischen Analyse visualisiert werden. 
*Die bisher erlernten Design-Prinzipien sollen umgesetzt werden (z.B.: Optimierung der Data-ink ratio). 
*Die Mockups sollten zumindest 1) Homer's Leben im Überblick 2) und eine Detailansicht wiedergeben. 
*Alle nicht angeführten Daten können frei erfunden werden.

== Analysis ==

=== Dataset - Analysis ===
The data contains Homer Simpsons drinking behavior according to his personal circumstances or events in his life. The set which is based on abstract information representing the amount of sugar, coffein and alcohol absorbed through six different beverages.

The set consists of the following dimensions:
* Personal circumstances
Personal circumstances as nominal data are split up to the following categories:
Birth, Childhood, Puberty, Unemployment, Relationship, Marriage, Birth of Children,
Lovesickness, daily routine, favourite team win, favourite team loss, getting arrested, retirement

* Drink consumption
Drink consumption is split up to six categories of drinks (water, milk,
juice, coke, coffee, beer) containing continuous values of consumed liter per day.

* Ingredients consumption
Ingredients consumption is split up to three categories of ingredients
(sugar, coffein, alcohol) containing continuous values derived from the
drink consumption.

=== Fields of Application ===
With the aid of suitable visualizations of the given dataset it could be possible to study if there is a relation between personal circumstances and his drinking behavior. Therefore it can be studied in which way personal circumstances or events in Homers life affected his drinking behavior, or otherwise. This could be useful in fields of pschology and medicine. Based on the visualization nutrition advice could be given and schedules could be implemented.

=== Target Group - Analysis ===
Our target group contains medical scientists as well as their patients. So the program should contain all information necessary for a doctor to analyze the patients condition during a certain period and make a matching diagnoses, as well as that easy to understand to let the patient see his progress or regress during special situations in his life.

=== Goals of the Visualization ===
A primary goal of the visualization is to get information about the consumed amount of sugar, coffein and alcohol in respect to the recommended maximal allowance. The visualization should provide mechanisms to get an general overview and detailed information as well.
This information can be used for monitoring exceedance of the recommended maximal allowance. It can also be used for comparision of consumption depending on different personal circumstances.

== Concept ==

=== Type of visualization ===
The main type of visualization used is the Stack Graph [Many eyes]. Whereas the first axis is used for the circumstances data and the second for the amount of a special drink or ingredient.
The decision for it was made because with a Stack Graph it is easy to show the most important data with less data ink used. So the patient can easily see in which time period he has passed the recommended maximum allowance. With the help of interactive navigation, it is nevertheless possible for medicine scientists to get the exact data for a better analysis of the patient and for making a detailed diagnoses.

Figure 1 and Figure 2 shows this type of visualization. Figure 1 shows the general drinking different behavior of Homer Simpson dependent of his personal circumstances and Figure 2 the ingredient alchol derived from this drinking behavior.
 
[[Image:drinks.png|thumb|none|right|150px|Figure 1, Visualization of Homer Simpsons drinking behavior dependent on personal circumstances]]
 
[[Image:sugar9.jpg|thumb|none|right|150px|Figure 2, Visualization of alcohol consumed by Homer Simpson dependent on personal circumstances]]

=== Interaction possibilities ===
Our interactive prototype allows the user to dynamically switch between different
ingredients. Each view shows the consumption of the selected ingredient as a stack graph
which has different highs and lows due to occasions in the life of our subject.

The analyst can also browse through the stack graph with the mouse.
At each peak or low the graphic displays a pop-up window providing more
detailed information about the selected event.

Here is an animation of an example of this interactivity: 

[[Image:sugar1-kurz.gif|Figure 3, Animation of the visualization technique]]

=== Visual Mapping ===
Because the drinking behavior and therefore the amount of consumed ingredients depending on personal circumstances is one of the basic information the ingredients consumption dimension is mapped to the size of the stack. E.q. a high amount of consumed sugar results in a high stack size. Thus someone can easily see the important information.

=== User support ===
The prototype gives the user a fast overview of the consumption of a specific
ingredient by the user. The amount of consumption grouped with life-time events
allow an analyst state a reason for a high or low use of an ingredient. The maximum
allowance bar additonally supports the reader of the graphic.

=== Pros of the visualization ===
* Less data ink
* Important data directly seeable for patient and medicine scientists
* Detailed data through interaction

=== Cons of the visualization ===
* Only one visualization type

=== Refinement ===
A better prototype could have more interaction possibilities like a zoom-in or zoom-out function.
It would also be nice for an analyst to be able to compare different people or people
of to the same area with people from different areas.

== References ==

* [Many eyes, 2009] Many eyes. Retrieved at: January 7, 2009. http://manyeyes.alphaworks.ibm.com/manyeyes/

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2008/09|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03|Gruppe 03]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 4

2009-01-07T23:15:59Z

UE-InfoVis0809 0425646:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe4.html Beschreibung der Aufgabe 4]

=== Gegebene Daten ===

'''Homer Simpson's Trinkverhalten in Abhängigkeit von seinen Lebensumständen''' 

...Visualisierung von Homer's Lebensabschnitten bzw. Ereignissen mit Einfluss auf sein
Trinkverhalten (zB.: Kindheit, Pubertät, Arbeitslosigkeit, Beziehungen, Hochzeit, Geburt
der Kinder, Liebeskummer, Alltag, etc.) von seiner Geburt bis Jetzt + mögliche
Zukunftsszenarien (mind. 3).
 
*Die Menge folgender Getränke soll für die jeweiligen Lebensumstände ablesbar sein
(ml oder Liter - je nachdem - pro Tag, Monat, Jahr (z.B.: Fokus+Kontext Methoden):
a) Wasser 
b) Milch 
c) Fruchtsaft 
d) Cola 
e) Kaffee (Würfelzucker?) 
f) Bier
(vereinfacht angenommen, Homer trinkt ausschließlich diese Getränke)
 
*Die folgenden Werte sollen abhängig von den konsumierten Getränken ablesbar sein:
1) g oder kg konsumierter Zucker (aus Getränken) + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 50g; enthaltener Zucker: 10g/100 ml Cola; 10g/100 ml Fruchtsaft; 3g/Würfelzucker). 
2) mg konsumiertes Coffein + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 600mg; enthaltenes Coffein: 10 mg/100 ml Cola; 80 mg/100 ml Kaffee). 
3) g konsumierter Alkohol + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 20g; enthaltener Alkohol: 3,6 g/100ml Bier)
 
*Die Daten sollen zur medizinischen/psychologischen Analyse visualisiert werden. 
*Die bisher erlernten Design-Prinzipien sollen umgesetzt werden (z.B.: Optimierung der Data-ink ratio). 
*Die Mockups sollten zumindest 1) Homer's Leben im Überblick 2) und eine Detailansicht wiedergeben. 
*Alle nicht angeführten Daten können frei erfunden werden.

== Analysis ==

=== Dataset - Analysis ===
The data contains Homer Simpsons drinking behavior according to his personal circumstances or events in his life. The set which is based on abstract information representing the amount of sugar, coffein and alcohol absorbed through six different beverages.

The set consists of the following dimensions:
* Personal circumstances
Personal circumstances as nominal data are split up to the following categories:
Birth, Childhood, Puberty, Unemployment, Relationship, Marriage, Birth of Children,
Lovesickness, daily routine, favourite team win, favourite team loss, getting arrested, retirement

* Drink consumption
Drink consumption is split up to six categories of drinks (water, milk,
juice, coke, coffee, beer) containing continuous values of consumed liter per day.

* Ingredients consumption
Ingredients consumption is split up to three categories of ingredients
(sugar, coffein, alcohol) containing continuous values derived from the
drink consumption.

=== Fields of Application ===
With the aid of suitable visualizations of the given dataset it could be possible to study if there is a relation between personal circumstances and his drinking behavior. Therefore it can be studied in which way personal circumstances or events in Homers life affected his drinking behavior, or otherwise. This could be useful in fields of pschology and medicine. Based on the visualization nutrition advice could be given and schedules could be implemented.

=== Target Group - Analysis ===
Our target group contains medical scientists as well as their patients. So the program should contain all information necessary for a doctor to analyze the patients condition during a certain period and make a matching diagnoses, as well as that easy to understand to let the patient see his progress or regress during special situations in his life.

=== Goals of the Visualization ===
A primary goal of the visualization is to get information about the consumed amount of sugar, coffein and alcohol in respect to the recommended maximal allowance. The visualization should provide mechanisms to get an general overview and detailed information as well.
This information can be used for monitoring exceedance of the recommended maximal allowance. It can also be used for comparision of consumption depending on different personal circumstances.

== Concept ==

=== Type of visualization ===
The main type of visualization used is the Stack Graph. Whereas the first axes is used for time period data and the second for the amount of a special drink or ingredient.
The decision for it was made because with a Stack Graph it is easy to show the most important data with less data ink used. So the patient can easily see in which time period he has passed the recommended maximum allowance. With the help of interactive navigation, it is nevertheless possible for medicine scientists to get the exact data for a better analysis of the patient and for making a detailed diagnoses.

Figure 1 and Figure 2 shows this type of visualization. Figure 1 shows the general drinking different behavior of Homer Simpson dependent of his personal circumstances and Figure 2 the ingredient alchol derived from this drinking behavior.
 
[[Image:drinks.png|thumb|none|right|150px|Figure 1, Visualization of Homer Simpsons drinking behavior dependent on personal circumstances]]
 
[[Image:sugar9.jpg|thumb|none|right|150px|Figure 2, Visualization of alcohol consumed by Homer Simpson dependent on personal circumstances]]

=== Interaction possibilities ===
Our interactive prototype allows the user to dynamically switch between different
ingredients. Each view shows the consumption of the selected ingredient as a stack graph
which has different highs and lows due to occasions in the life of our subject.

The analyst can also browse through the stack graph with the mouse.
At each peak or low the graphic displays a pop-up window providing more
detailed information about the selected event.

Here is an animation of an example of this interactivity: 

[[Image:sugar1-kurz.gif|Figure 3, Animation of the visualization technique]]

=== User support ===
The prototype gives the user a fast overview of the consumption of a specific
ingredient by the user. The amount of consumption grouped with life-time events
allow an analyst state a reason for a high or low use of an ingredient. The maximum
allowance bar additonally supports the reader of the graphic.

=== Pros of the visualization ===
* Less data ink
* Important data directly seeable for patient and medicine scientists
* Detailed data through interaction

=== Cons of the visualization ===
* Only one visualization type

=== Refinement ===
A better prototype could have more interaction possibilities like a zoom-in or zoom-out function.
It would also be nice for an analyst to be able to compare different people or people
of to the same area with people from different areas.

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2008/09|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03|Gruppe 03]]

File:Sugar9.jpg

2009-01-07T22:59:43Z

UE-InfoVis0809 0425646: Visualization of alcohol of Homer Simpsons drinking behavior dependent on personal circumstances. (Fictional data)

== Summary ==
Visualization of alcohol of Homer Simpsons drinking behavior dependent on personal circumstances. (Fictional data)
== Copyright status ==
Created with http://manyeyes.alphaworks.ibm.com/manyeyes/
== Source ==

File:Drinks.png

2009-01-07T22:57:04Z

UE-InfoVis0809 0425646: Homer Simpson drinking behavior dependent on personal circumstances. (Fictional data)

== Summary ==
Homer Simpson drinking behavior dependent on personal circumstances. (Fictional data)
== Copyright status ==
Created with http://manyeyes.alphaworks.ibm.com/manyeyes/
== Source ==

File:Sugar1-kurz.gif

2009-01-07T22:55:22Z

UE-InfoVis0809 0425646: Animation of Visualization Technique visualizing ingredients of Homer Simpson drinking behavior dependent on personal circumstances. (Fictional data)

== Summary ==
Animation of Visualization Technique visualizing ingredients of Homer Simpson drinking behavior dependent on personal circumstances. (Fictional data)
== Copyright status ==
Created with http://manyeyes.alphaworks.ibm.com/manyeyes/
== Source ==

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 2

2008-12-09T19:20:52Z

UE-InfoVis0809 0425646:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe2.html Beschreibung der Aufgabe 2]

=== Zu beurteilende Tabelle ===

[[Image:Simuresults.jpg]]

== Critics & Changes ==

=== White space ===

The used white space doesn't sufficiently group the data objects that belong together. The horizontal white space between the grouped columns beneath the spanner headers is variying and it is too less to visually track across them.

* White space between grouped columns has been adapted and sized evenly to improve the arrangement of them for a better visual perception. Inside grouped columns white space has been reduced to intensify the visual perception as a group (''Gestalt principle of proximity'').

=== Title ===
The title contains dispensible information which is already visible in the different table headers.

* Title has been reduced to sufficiently describe the table.

=== Subheaders & Arrangement ===

The arrangement of the subheaders breaks up the data and disrupts the flow of information and makes it hard to read and recognize the differences.

* Subheaders have been moved to a separate column on the left. By adding this mechanisms the legibility of the table increased. Additional white space between each group proofed also helpful to improve the clarity of the table.

The boldfacing of the mechanisms is an unecassary emphasis and is a not useful method to group data.

* Boldfacing has been removed.

=== Numbers & Alignment ===

Columns contain data with a varying numeric precision. This disrupt the visual structure of the columns in form of creating ragged edges on the right of a column.

* To facilitate comparison of numbers in columns and to preserve the visual structure of columns (''Gestalt principle of enclosure'') numbers have now equal number of decimal digits with same alignment down the columns. A numeric precision level of 5 decimal digits has been set to not exceed information with too excessive level of precision. The column headers also have been aligned with the associated data.

=== Descriptions ===

The abbreviated row headers ''Comp'' and ''Impu'' are not consistent with the title and are not convenient describing the use of ''CRs only'' and the ''proposed method''.

* The use of the abbreviations ''CR'' to describe the use of ''CRs only'' and ''prop'' to describe the ''proposed method'' link to the used methods in a more intuitive manner.

The arrangement of the rows ''Comp'' and ''Impu'' isn't handy to recognize that there is less difference between ''Full'' and ''Impu'' than between ''Full'' and ''Comp''.

* Therefore the rows have been arranged in such order to be unterstood more easily. The row ''prop'' has been placed above the row ''CR''.

== Corrected Table ==
[[image:table5.png|600px]]

== References ==
*[Few, 2004]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Chapter 8 - Table Design.

*[Wallace, 2004]: Rosa Wallace, Designing Tables, NC State University LabWrite Resources, 2004.
[http://www.ncsu.edu/labwrite/res/gh/gh-tables.html http://www.ncsu.edu/labwrite/res/gh/gh-tables.html]

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2008/09|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03|Gruppe 03]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 2

2008-11-21T01:54:10Z

UE-InfoVis0809 0425646:

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 2

2008-11-20T17:09:57Z

UE-InfoVis0809 0425646:

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 2

2008-11-20T17:00:31Z

UE-InfoVis0809 0425646:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe2.html Beschreibung der Aufgabe 2]

=== Zu beurteilende Tabelle ===

[[Image:Simuresults.jpg]]

== Critics ==

== Corrected Table ==
[[image:table5.png|600px]]

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2008/09|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03|Gruppe 03]]

File:Table5.png

2008-11-20T16:53:37Z

UE-InfoVis0809 0425646: Corrected Table

== Summary ==
Corrected Table
== Copyright status ==

== Source ==
[Paik, 2004] Myunghee Cho Paik. Nonignorable missingness in matched case-control data analyses. Biometrics 60(2), pp. 306-314, June 2004.

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 1 - Cone Tree

2008-11-16T16:44:53Z

UE-InfoVis0809 0425646:

== Introduction ==
{{Quotation| ... the Cone Tree, which is used for visualizing hierarchical information structures.|[Robertson et al., 1991]}}
[[Image:Big tree.jpg|thumb|none|right|150px|2D Tree diagram with 365 leaves, 729 nodes [VanWijk, 2008]]]

Cone Trees are a suitable visualization technique for dealing with a great amount of information in hierachichal structure, with the aim to display and navigate through the information in an intuitative manner. On screens a 2D layout of such structures wouldn't be easy to handle with. It would not fit on the screen and "The user would have to either scroll through the layout or use a size-reduced image of the structure."[Robertson et al., 1991]

{{Quotation| The hierarchy is presented in 3D to maximize effective use of available screen space and enable visualization of the whole structure.|[Robertson et al., 1991]}}

Instead of a 2D tree Cone Trees use three dimensional space. Therefore a parent node and its children are placed in that kind, so parent and children form a cone, with the parent at the apex and the children along the circular base of the cone.

== The idea in detail ==

[[Image:robertson_plate2.jpg|thumb|none|right|150px|Cone tree after a selection rotation is completed. [Robertson et al., 1991]]]

A Cone Tree is a three dimensional diagram with visible graphical edge from parents to their children (node-link diagram).

The root node is placed at the apex of the cone tree. All children nodes are placed at equal distances along the base. This process is repeated for every following level. Hence a child node may span further cones. The aspect ratio of the tree is fixed to fit the room, whereas each layer of cones has the same height.

A key idea is the possibility of interaction. For navigation the user can rotate the cone and choose a special node.

{{Quotation| When a node is selected with the mouse, the Cone Tree rotates so that the selected node and each node in the path from the selected node up to the top are brought to the front and highlighted.|[Robertson et al., 1991]}}

These rotations are shown the user with animations.

[[Image:robertson_plate3.jpg|thumb|none|right|150px|An alternative, horizontally oriented layout, called the ''Cam Tree''. [Robertson et al., 1991]]]

{{Quotation| Interactive animation is used to shift some of the user's cognitive load to the human perceptual system. ... animation allows the perceptual system to track the rotations.|[Robertson et al., 1991]}}

There is the possibility of an alternative horizontally layout, called the Cam Tree, which displays text for each node, compensating the problem of the other view, where text is only shown for the selected path.

== Critics ==
A study showed that subjects were signiﬁcantly slower at locating named ﬁles in a hierarchical data structure when using a cone tree interface than when using a 'normal' tree interface. [Cockburn and Mckenzie, 2000]
And Cone Trees "seem to inherit a problem of standard node-link diagrams: they fall short when it comes to large trees. [[Teaching:TUW_-_UE_InfoVis_WS_2008/09_-_Gruppe_06_-_Aufgabe_1_-_Treemap|Treemaps]] [Shneiderman, 1992] were invented by shneiderman to address this particular problem."[VanWijk et al., 2003]

== References ==
* [Robertson et al., 1991] George Robertson, Jock D. Mackinlay, Stuart Card. Cone Trees: Animated 3D Visualizations of Hierarchical Information. In Proceedings of the ACM CHI 91 Human Factors in Computing Systems Conference, pages 189-- 194, April 28 - June 5, 1991, New Orleans, Louisiana, June 1991. Association for Computing Machinery
* [VanWijk, 2008] Jack van Wijk. Information Visualization Visual Analytics. ''I-science for Astronomy, October 13-17, 2008''. Lorentz center, Leiden. Retrieved at: November 7, 2008. http://www.lorentzcenter.nl/lc/web/2008/321/presentations/VanWijk.pdf
* [Cockburn and Mckenzie, 2000] Andy Cockburn, Bruce Mckenzie. An Evaluation of Cone Trees. In ''People and Computers XIV: British Computer Society Conference on Human Computer Interaction 2000'', p425--436. Springer-Verlag.
* [Shneiderman, 1992] Ben Shneiderman. Tree visualization with tree-maps: A 2-D space-filling approach. ACM Transactions on Graphics 11, pages 92--99, 1992. Association for Computing Machinery
* [VanWijk et al., 2003] Jarke J. van Wijk, Frank Van Ham, Huub Van De Wetering. Rendering Hierarchical Data. Communications of the ACM, pages 257--263, 2003. Association for Computing Machinery

File:Faces2.jpg

2008-11-15T14:19:18Z

UE-InfoVis0809 0425646:

== Summary ==
Six facial variations of Chernoff Faces
== Copyright status ==
Joseph G. Spinelli and Yu Zhou
== Source ==
Joseph G. Spinelli and Yu Zhou. "Mapping Quality of Life with Chernoff Faces", 4th Annual ESRI Education User Conference 2004, San Diego, CA, August 2004. http://downloads2.esri.com/campus/uploads/library/pdfs/58415.pdf

File:Lifeinla.GIF

2008-11-15T14:10:43Z

UE-InfoVis0809 0425646: summary added, source updated

== Summary ==
1977 Life in LA, 1970
Map of Los Angeles with Chernoff Faces describing factors of quality of life
== Copyright status ==
Eugene Turner
== Source ==
Eugene Turner
1977 Life in LA, 1970
http://www.csun.edu/%7Ehfgeg005/eturner/gallery/gallery.htm
http://www.csun.edu/%7Ehfgeg005/eturner/gallery/lifeinla.GIF

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 1 - Cone Tree

2008-11-07T22:44:41Z

UE-InfoVis0809 0425646: idea in detail explanations

== Introduction ==
{{Quotation| ... the Cone Tree, which is used for visualizing hierarchical information structures.|[Robertson et al., 1991]}}
[[Image:Big tree.jpg|thumb|none|right|150px|2D Tree diagram with 365 leaves, 729 nodes [VanWijk, 2008]]]

Cone Trees are a suitable visualization technique for dealing with a great amount of information in hierachichal structure, with the aim to display and navigate through the information in an intuitative manner. On screens a 2D layout of such structures wouldn't be easy to handle with. It would not fit on the screen and "The user would have to either scroll through the layout or use a size-reduced image of the structure."

{{Quotation| The hierarchy is presented in 3D to maximize effective use of available screen space and enable visualization of the whole structure.|[Robertson et al., 1991]}}

== The idea in detail ==

[[Image:robertson_plate2.jpg|thumb|none|right|150px|Cone tree after a selection rotation is completed. [Robertson et al., 1991]]]

A Cone Tree is a three dimensional diagram with visible graphical edge from parents to their children (node-link diagram).
The root node is placed at the apex of the cone tree. All children nodes are placed at equal distances along the base. This process is repeated for every following level. The aspect ratio of the tree is fixed to fit the room, whereas each layer of cones has the same height.
A key idea is the possibility of interaction. For navigation the user can rotate the cone and choose a special node, which is shown with an animation.

[[Image:robertson_plate3.jpg|thumb|none|right|150px|An alternative, horizontally oriented layout, called the ''Cam Tree''. [Robertson et al., 1991]]]

{{Quotation| Interactive animation is used to shift some of the user's cognitive load to the human perceptual system. ... animation allows the perceptual system to track the rotations.|[Robertson et al., 1991]}}

== Critics ==
A study showed that subjects were signiﬁcantly slower at locating named ﬁles in a hierarchical data structure when using a cone tree interface than when using a 'normal' tree interface. [Cockburn and Mckenzie, 2000]
And Cone Trees "seem to inherit a problem of standard node-link diagrams: they fall short when it comes to large trees. [[Teaching:TUW_-_UE_InfoVis_WS_2008/09_-_Gruppe_06_-_Aufgabe_1_-_Treemap|Treemaps]] [Shneiderman, 1992] were invented by shneiderman to address this particular problem."[VanWijk et al., 2003]

== References ==
* [Robertson et al., 1991] George Robertson, Jock D. Mackinlay, Stuart Card. Cone Trees: Animated 3D Visualizations of Hierarchical Information. In Proceedings of the ACM CHI 91 Human Factors in Computing Systems Conference, pages 189-- 194, April 28 - June 5, 1991, New Orleans, Louisiana, June 1991. Association for Computing Machinery
* [VanWijk, 2008] Jack van Wijk. Information Visualization Visual Analytics. ''I-science for Astronomy, October 13-17, 2008''. Lorentz center, Leiden. Retrieved at: November 7, 2008. http://www.lorentzcenter.nl/lc/web/2008/321/presentations/VanWijk.pdf
* [Cockburn and Mckenzie, 2000] Andy Cockburn, Bruce Mckenzie. An Evaluation of Cone Trees. In ''People and Computers XIV: British Computer Society Conference on Human Computer Interaction 2000'', p425--436. Springer-Verlag.
* [Shneiderman, 1992] Ben Shneiderman. Tree visualization with tree-maps: A 2-D space-filling approach. ACM Transactions on Graphics 11, pages 92--99, 1992. Association for Computing Machinery
* [VanWijk et al., 2003] Jarke J. van Wijk, Frank Van Ham, Huub Van De Wetering. Rendering Hierarchical Data. Communications of the ACM, pages 257--263, 2003. Association for Computing Machinery

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 1 - Cone Tree

2008-11-07T22:38:48Z

UE-InfoVis0809 0425646: 2D tree image

== Introduction ==
{{Quotation| ... the Cone Tree, which is used for visualizing hierarchical information structures.|[Robertson et al., 1991]}}
[[Image:Big tree.jpg|thumb|none|right|150px|2D Tree diagram with 365 leaves, 729 nodes [VanWijk, 2008]]]

Cone Trees are a suitable visualization technique for dealing with a great amount of information in hierachichal structure, with the aim to display and navigate through the information in an intuitative manner. On screens a 2D layout of such structures wouldn't be easy to handle with. It would not fit on the screen and "The user would have to either scroll through the layout or use a size-reduced image of the structure."

{{Quotation| The hierarchy is presented in 3D to maximize effective use of available screen space and enable visualization of the whole structure.|[Robertson et al., 1991]}}

== The idea in detail ==

[[Image:robertson_plate2.jpg|thumb|none|right|150px|Cone tree after a selection rotation is completed. [Robertson et al., 1991]]]

The root node is placed at the apex of the cone tree. All children nodes are placed at equal distances along the base. This process is repeated for every following level. The aspect ratio of the tree is fixed to fit the room, whereas each layer of cones has the same height.
For navigation the user can rotate the cone and choose a special node, which is shown with an animation.

[[Image:robertson_plate3.jpg|thumb|none|right|150px|An alternative, horizontally oriented layout, called the ''Cam Tree''. [Robertson et al., 1991]]]

{{Quotation| Interactive animation is used to shift some of the user's cognitive load to the human perceptual system. ... animation allows the perceptual system to track the rotations.|[Robertson et al., 1991]}}

== Critics ==
A study showed that subjects were signiﬁcantly slower at locating named ﬁles in a hierarchical data structure when using a cone tree interface than when using a 'normal' tree interface. [Cockburn and Mckenzie, 2000]
And Cone Trees "seem to inherit a problem of standard node-link diagrams: they fall short when it comes to large trees. [[Teaching:TUW_-_UE_InfoVis_WS_2008/09_-_Gruppe_06_-_Aufgabe_1_-_Treemap|Treemaps]] [Shneiderman, 1992] were invented by shneiderman to address this particular problem."[VanWijk et al., 2003]

== References ==
* [Robertson et al., 1991] George Robertson, Jock D. Mackinlay, Stuart Card. Cone Trees: Animated 3D Visualizations of Hierarchical Information. In Proceedings of the ACM CHI 91 Human Factors in Computing Systems Conference, pages 189-- 194, April 28 - June 5, 1991, New Orleans, Louisiana, June 1991. Association for Computing Machinery
* [VanWijk, 2008] Jack van Wijk. Information Visualization Visual Analytics. ''I-science for Astronomy, October 13-17, 2008''. Lorentz center, Leiden. Retrieved at: November 7, 2008. http://www.lorentzcenter.nl/lc/web/2008/321/presentations/VanWijk.pdf
* [Cockburn and Mckenzie, 2000] Andy Cockburn, Bruce Mckenzie. An Evaluation of Cone Trees. In ''People and Computers XIV: British Computer Society Conference on Human Computer Interaction 2000'', p425--436. Springer-Verlag.
* [Shneiderman, 1992] Ben Shneiderman. Tree visualization with tree-maps: A 2-D space-filling approach. ACM Transactions on Graphics 11, pages 92--99, 1992. Association for Computing Machinery
* [VanWijk et al., 2003] Jarke J. van Wijk, Frank Van Ham, Huub Van De Wetering. Rendering Hierarchical Data. Communications of the ACM, pages 257--263, 2003. Association for Computing Machinery

File:Big tree.jpg

2008-11-07T22:34:25Z

UE-InfoVis0809 0425646:

== Summary ==
Tree diagram
365 leaves, 729 nodes
== Copyright status ==
Jack van Wijk
== Source ==
[Wijk, 2008] Jack van Wijk. Information Visualization Visual Analytics. ''I-science for Astronomy, October 13-17, 2008''. Lorentz center, Leiden. Retrieved at: November 7, 2008. http://www.lorentzcenter.nl/lc/web/2008/321/presentations/VanWijk.pdf

File:Big tree.jpg

2008-11-07T22:32:02Z

UE-InfoVis0809 0425646: Tree diagramm 365 leaves, 729 nodes

== Summary ==
Tree diagramm
365 leaves, 729 nodes
== Copyright status ==
Jack van Wijk
== Source ==
[Wijk, 2008] Jack van Wijk. Information Visualization Visual Analytics. ''I-science for Astronomy, October 13-17, 2008''. Lorentz center, Leiden. Retrieved at: November 7, 2008. http://www.lorentzcenter.nl/lc/web/2008/321/presentations/VanWijk.pdf

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 1 - Chernoff Face

2008-11-07T22:08:44Z

UE-InfoVis0809 0425646: [Wikipedia, 2008] reference update

== Introduction ==
According to wikipedia a Chernoff face ''represents multivariate data in form of a face'' [Wikipedia, 2008]. So instead of looking at a bunch of different numbers in a vector a human can look on faces, where each face describes a data set which can contain up to 18 dimensions.

Also the slightest change of the vector results in a different face. Studying faces the whole life-time the human brain is more used to distinguish between faces than vectors. Chernoff faces therefore are often used in statistic presentations like in Dr. Eugen Turners Map “Life in Los Angeles”.[Turner, 1977]

[[Image:lifeinla.GIF|thumb|none|150px|Life in Los Angeles [Turner, 1977]]]

Faces are type of [[Glyph|glyph]], a graphical object whose properties represent data values

The idea of displaying faces to represent multivariate data was first published in 1973 in The Journal of American National Statistic by Herman Chernoff who was born in 1923 in America. Article: “The Use of Faces to Represent Points in K-Dimensional Space Graphically”. [Chernoff, 1973]

== The idea in detail ==
As already said Chernoff faces are simplified faces that can help viewers to detect patterns, groupings, and correlation. Having data sets each dimension is assigned to a facial characteristic. Facial characteristics are for example eybrow slant, eye spacing, head eccentricity, pupil size, and so on. Inside the data set each dimension represents a feature which can be classified. If we say for example that one feature of our face displays the rate of unemployment (rou) we could classify that rate into three classes e.g. rou < 3%, 3%< rou <6% or rou > 6%. So we have three classes for the feature rate of unemployment. As each feature describes a characteristic of a face we could say that the shape of the mouth could stand for the rate of unemployment as figure 2 demonstrates.

[[Image:faces2.jpg|thumb|none|350px|Six facial variations [Spinelli and Zhou, 2004]]]

Hence, every face unambiguously describes one feature vector, which in turn combines several features to describe one condition, like the quality of life as in [Spinelli and Zhou, 2004].

== Critics ==
Chernoff faces may be not that effective it seems. Certain features like the perception of eye size, eyebrow slant and the combination of those both are more influencing for longer viewing times than others. Therefore [Morris et. al, 2000] came to the conclusion that the use of Chernoff faces ''"may not have a significant advantage over other iconic visualization techniques for multidimensional information visualization.”''

== References ==
* [Wikipedia, 2008] Wikipedia contributors. Chernoff_face. ''Wikipedia, the free encyclopedia''. Last modified on October 5, 2008, at 15:41. Retrieved at: November 7, 2008. http://en.wikipedia.org/wiki/Chernoff_face
* [Turner, 1977] Eugene Turner, Life in LA, 1977 Retrieved at: November 7, 2008. http://www.csun.edu/%7Ehfgeg005/eturner/gallery/
* [Chernoff, 1973] Herman Chernoff. The Use of Faces to Represent Points in k-Dimensional Space Graphically, ''Journal of the American Statistical Association'', 68 (#342): 361-368, 1973. http://www.jstor.org/pss/2284077
* [Spinelli and Zhou, 2004] Joseph G. Spinelli and Yu Zhou. "Mapping Quality of Life with Chernoff Faces", ''4th Annual ESRI Education User Conference 2004'', San Diego, CA, August 2004.http://downloads2.esri.com/campus/uploads/library/pdfs/58415.pdf
* [Morris et al., 2000] Christopher J. Morris, David S. Ebert, Penny Rheingans. An Experimental Analysis of the Effectiveness of Features in Chernoff Faces. ''28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making, Proceedings of SPIE'', pages 12- 17, 2000. http://www.csee.umbc.edu/~ebert/papers/Chernoff_990402.PDF

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 1 - Chernoff Face

2008-11-07T21:54:10Z

UE-InfoVis0809 0425646: reference update

== Introduction ==
According to wikipedia a Chernoff face ''represents multivariate data in form of a face'' [Wikipedia, 2008]. So instead of looking at a bunch of different numbers in a vector a human can look on faces, where each face describes a data set which can contain up to 18 dimensions.

Also the slightest change of the vector results in a different face. Studying faces the whole life-time the human brain is more used to distinguish between faces than vectors. Chernoff faces therefore are often used in statistic presentations like in Dr. Eugen Turners Map “Life in Los Angeles”.[Turner, 1977]

[[Image:lifeinla.GIF|thumb|none|150px|Life in Los Angeles [Turner, 1977]]]

Faces are type of [[Glyph|glyph]], a graphical object whose properties represent data values

The idea of displaying faces to represent multivariate data was first published in 1973 in The Journal of American National Statistic by Herman Chernoff who was born in 1923 in America. Article: “The Use of Faces to Represent Points in K-Dimensional Space Graphically”. [Chernoff, 1973]

== The idea in detail ==
As already said Chernoff faces are simplified faces that can help viewers to detect patterns, groupings, and correlation. Having data sets each dimension is assigned to a facial characteristic. Facial characteristics are for example eybrow slant, eye spacing, head eccentricity, pupil size, and so on. Inside the data set each dimension represents a feature which can be classified. If we say for example that one feature of our face displays the rate of unemployment (rou) we could classify that rate into three classes e.g. rou < 3%, 3%< rou <6% or rou > 6%. So we have three classes for the feature rate of unemployment. As each feature describes a characteristic of a face we could say that the shape of the mouth could stand for the rate of unemployment as figure 2 demonstrates.

[[Image:faces2.jpg|thumb|none|350px|Six facial variations [Spinelli and Zhou, 2004]]]

Hence, every face unambiguously describes one feature vector, which in turn combines several features to describe one condition, like the quality of life as in [Spinelli and Zhou, 2004].

== Critics ==
Chernoff faces may be not that effective it seems. Certain features like the perception of eye size, eyebrow slant and the combination of those both are more influencing for longer viewing times than others. Therefore [Morris et. al, 2000] came to the conclusion that the use of Chernoff faces ''"may not have a significant advantage over other iconic visualization techniques for multidimensional information visualization.”''

== References ==
* [Wikipedia, 2008] Wikipedia contributors. Chernoff_face. ''Wikipedia, the free encyclopedia. '' Retrieved at: November 7, 2008. http://en.wikipedia.org/wiki/Chernoff_face
* [Turner, 1977] Eugene Turner, Life in LA, 1977 Retrieved at: November 7, 2008. http://www.csun.edu/%7Ehfgeg005/eturner/gallery/
* [Chernoff, 1973] Herman Chernoff. The Use of Faces to Represent Points in k-Dimensional Space Graphically, ''Journal of the American Statistical Association'', 68 (#342): 361-368, 1973. http://www.jstor.org/pss/2284077
* [Spinelli and Zhou, 2004] Joseph G. Spinelli and Yu Zhou. "Mapping Quality of Life with Chernoff Faces", ''4th Annual ESRI Education User Conference 2004'', San Diego, CA, August 2004.http://downloads2.esri.com/campus/uploads/library/pdfs/58415.pdf
* [Morris et al., 2000] Christopher J. Morris, David S. Ebert, Penny Rheingans. An Experimental Analysis of the Effectiveness of Features in Chernoff Faces. ''28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making, Proceedings of SPIE'', pages 12- 17, 2000. http://www.csee.umbc.edu/~ebert/papers/Chernoff_990402.PDF

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 1 - Chernoff Face

2008-11-07T21:52:24Z

UE-InfoVis0809 0425646: reference update

== Introduction ==
According to wikipedia a Chernoff face ''represents multivariate data in form of a face'' [Wikipedia, 2008]. So instead of looking at a bunch of different numbers in a vector a human can look on faces, where each face describes a data set which can contain up to 18 dimensions.

Also the slightest change of the vector results in a different face. Studying faces the whole life-time the human brain is more used to distinguish between faces than vectors. Chernoff faces therefore are often used in statistic presentations like in Dr. Eugen Turners Map “Life in Los Angeles”.[Turner, 1977]

[[Image:lifeinla.GIF|thumb|none|150px|Life in Los Angeles [Turner, 1977]]]

Faces are type of [[Glyph|glyph]], a graphical object whose properties represent data values

The idea of displaying faces to represent multivariate data was first published in 1973 in The Journal of American National Statistic by Herman Chernoff who was born in 1923 in America. Article: “The Use of Faces to Represent Points in K-Dimensional Space Graphically”. [Chernoff, 1973]

== The idea in detail ==
As already said Chernoff faces are simplified faces that can help viewers to detect patterns, groupings, and correlation. Having data sets each dimension is assigned to a facial characteristic. Facial characteristics are for example eybrow slant, eye spacing, head eccentricity, pupil size, and so on. Inside the data set each dimension represents a feature which can be classified. If we say for example that one feature of our face displays the rate of unemployment (rou) we could classify that rate into three classes e.g. rou < 3%, 3%< rou <6% or rou > 6%. So we have three classes for the feature rate of unemployment. As each feature describes a characteristic of a face we could say that the shape of the mouth could stand for the rate of unemployment as figure 2 demonstrates.

[[Image:faces2.jpg|thumb|none|350px|Six facial variations [Spinelli and Zhou, 2004]]]

Hence, every face unambiguously describes one feature vector, which in turn combines several features to describe one condition, like the quality of life as in [Spinelli and Zhou, 2004].

== Critics ==
Chernoff faces may be not that effective it seems. Certain features like the perception of eye size, eyebrow slant and the combination of those both are more influencing for longer viewing times than others. Therefore [Morris et. al, 2000] came to the conclusion that the use of Chernoff faces ''"may not have a significant advantage over other iconic visualization techniques for multidimensional information visualization.”''

== References ==
* [Wikipedia, 2008] Wikipedia contributors. Chernoff_face. ''Wikipedia, the free encyclopedia. '' Retrieved at: November 7, 2008. http://en.wikipedia.org/wiki/Chernoff_face
* [Chernoff, 1973] Herman Chernoff. The Use of Faces to Represent Points in k-Dimensional Space Graphically, ''Journal of the American Statistical Association'', 68 (#342): 361-368, 1973. http://www.jstor.org/pss/2284077
* [Spinelli and Zhou, 2004] Joseph G. Spinelli and Yu Zhou. "Mapping Quality of Life with Chernoff Faces", ''4th Annual ESRI Education User Conference 2004'', San Diego, CA, August 2004.http://downloads2.esri.com/campus/uploads/library/pdfs/58415.pdf
* [Turner, 1977] Eugene Turner, Life in LA, 1977 Retrieved at: November 7, 2008. http://www.csun.edu/%7Ehfgeg005/eturner/gallery/
* [Morris et al., 2000] Christopher J. Morris, David S. Ebert, Penny Rheingans. An Experimental Analysis of the Effectiveness of Features in Chernoff Faces. ''28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making, Proceedings of SPIE'', pages 12- 17, 2000. http://www.csee.umbc.edu/~ebert/papers/Chernoff_990402.PDF

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 1 - Cone Tree

2008-11-07T21:15:15Z

UE-InfoVis0809 0425646: critics and references update

== Introduction ==
{{Quotation| ... the Cone Tree, which is used for visualizing hierarchical information structures.|[Robertson et al., 1991]}}

Cone Trees are a suitable visualization technique for dealing with a great amount of information in hierachichal struture, with the aim to display and navigate through the information in an intuitative manner. On screens a 2D layout of such structures wouldn't be easy to handle with. It would not fit on the screen and "The user would have to either scroll through the layout or use a size-reduced image of the structure."

{{Quotation| The hierarchy is presented in 3D to maximize effective use of available screen space and enable visualization of the whole structure.|[Robertson et al., 1991]}}

[[Image:robertson_plate2.jpg|thumb|none|150px|Cone tree after a selection rotation is completed. [Robertson et al., 1991]]]

The root node is placed at the apex of the cone tree. All children nodes are placed at equal distances along the base. This process is repeated for every following level.
For navigation the user can rotate the cone and choose a special node, which is shown with an animation.

[[Image:robertson_plate3.jpg|thumb|none|150px|An alternative, horizontally oriented layout, called the ''Cam Tree''. [Robertson et al., 1991]]]

{{Quotation| Interactive animation is used to shift some of the user's cognitive load to the human perceptual system. ... animation allows the perceptual system to track the rotations.|[Robertson et al., 1991]}}

== Critics ==
A study showed that subjects were signiﬁcantly slower at locating named ﬁles in a hierarchical data structure when using a cone tree interface than when using a 'normal' tree interface. [Cockburn and Mckenzie, 2000]
And Cone Trees "seem to inherit a problem of standard node-link diagrams: they fall short when it comes to large trees. Treemaps [Shneiderman, 1992] were invented by shneiderman to address this particular problem."[VanWijk et al., 2003]

== References ==
* [Robertson et al., 1991] George Robertson, Jock D. Mackinlay, Stuart Card. Cone Trees: Animated 3D Visualizations of Hierarchical Information. In Proceedings of the ACM CHI 91 Human Factors in Computing Systems Conference, pages 189-- 194, April 28 - June 5, 1991, New Orleans, Louisiana, June 1991. Association for Computing Machinery
* [Cockburn and Mckenzie, 2000] Andy Cockburn, Bruce Mckenzie. An Evaluation of Cone Trees. In ''People and Computers XIV: British Computer Society Conference on Human Computer Interaction 2000'', p425--436. Springer-Verlag.
* [Shneiderman, 1992] Ben Shneiderman. Tree visualization with tree-maps: A 2-D space-filling approach. ACM Transactions on Graphics 11, pages 92--99, 1992. Association for Computing Machinery
* [VanWijk et al., 2003] Jarke J. van Wijk, Frank Van Ham, Huub Van De Wetering. Rendering Hierarchical Data. Communications of the ACM, pages 257--263, 2003. Association for Computing Machinery

File:Robertson plate3.jpg

2008-11-07T20:33:50Z

UE-InfoVis0809 0425646: Cone tree by Xerox

== Summary ==
Cone tree by Xerox
== Copyright status ==
George Robertson, Jock D. Mackinlay, Stuart Card
== Source ==
[Robertson et al., 1991] George Robertson, Jock D. Mackinlay, Stuart Card. Cone Trees: Animated 3D Visualizations of Hierarchical Information. In Proceedings of the ACM CHI 91 Human Factors in Computing Systems Conference, pages 189-- 194, April 28 - June 5, 1991, New Orleans, Louisiana, June 1991. Association for Computing Machinery

File:Robertson plate2.jpg

2008-11-07T20:32:52Z

UE-InfoVis0809 0425646: Cone tree by Xerox

File:Faces2.jpg

2008-11-07T17:34:29Z

UE-InfoVis0809 0425646:

== Summary ==
Six facial variations
Joseph G. Spinelli and Yu Zhou. "Mapping Quality of Life with Chernoff Faces", 4th Annual ESRI Education User Conference 2004, San Diego, CA, August 2004. http://downloads2.esri.com/campus/uploads/library/pdfs/58415.pdf
== Copyright status ==
Joseph G. Spinelli and Yu Zhou
== Source ==
http://downloads2.esri.com/campus/uploads/library/pdfs/58415.pdf

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 1 - Chernoff Face

2008-11-07T17:32:32Z

UE-InfoVis0809 0425646:

{{Quotation|bla|[ExampleAuthor et al., 2008]}}

== Introduction ==
According to wikipedia a Chernoff face ''represents multivariate data in form of a face'' [Wikipedia, 2008]. So instead of looking at a bunch of different numbers in a vector a human can look on faces, where each face describes a data set which can contain up to 18 dimensions.

Also the slightest change of the vector results in a different face. Studying faces the whole life-time the human brain is more used to distinguish between faces than vectors. Chernoff faces therefore are often used in statistic presentations like in Dr. Eugen Turners Map “Life in Los Angeles”.[Turner, 1977]

[[Image:lifeinla.GIF|thumb|none|150px|Life in Los Angeles [Turner, 1977]]]

Faces are type of [[Glyph|glyph]], a graphical object whose properties represent data values

The idea of displaying faces to represent multivariate data was first published in 1973 in The Journal of American National Statistic by Herman Chernoff who was born in 1923 in America. Article: “The Use of Faces to Represent Points in K-Dimensional Space Graphically”. [Chernoff, 1973]

== The idea in detail ==
As already said Chernoff faces are simplified faces that can help viewers to detect patterns, groupings, and correlation. Having data sets each dimension is assigned to a facial characteristic. Facial characteristics are for example eybrow slant, eye spacing, head eccentricity, pupil size, and so on. Inside the data set each dimension represents a feature which can be classified. If we say for example that one feature of our face displays the rate of unemployment (rou) we could classify that rate into three classes e.g. rou < 3%, 3%< rou <6% or rou > 6%. So we have three classes for the feature rate of unemployment. As each feature describes a characteristic of a face we could say that the shape of the mouth could stand for the rate of unemployment as figure 2 demonstrates.

[[Image:faces2.jpg|thumb|none|350px|Six facial variations [Spinelli and Zhou, 2004]]]

Hence, every face unambiguously describes one feature vector, which in turn combines several features to describe one condition, like the quality of life as in [Spinelli and Zhou, 2004].

== Critics ==
Chernoff faces may be not that effective it seems. Certain features like the perception of eye size, eyebrow slant and the combination of those both are more influencing for longer viewing times than others. Therefore [Morris et. al, 2000] came to the conclusion that the use of Chernoff faces ''"may not have a significant advantage over other iconic visualization techniques for multidimensional information visualization.”''

== References ==
* [ExampleAuthor et al., 2008] Firstname Lastname. Title of article. ''Title of Journal (in italic)'', Volume, number, Date of issue. Help [[Help:Citation_&_Bibilography_Format]]
* [Wikipedia, 2008] '''TODO''' bla
* [Chernoff, 1973] Herman Chernoff. The Use of Faces to Represent Points in k-Dimensional Space Graphically, ''Journal of the American Statistical Association'', 68 (#342): 361-368, 1973. http://www.jstor.org/pss/2284077
* [Spinelli and Zhou, 2004] Joseph G. Spinelli and Yu Zhou. "Mapping Quality of Life with Chernoff Faces", 4th Annual ESRI Education User Conference 2004, San Diego, CA, August 2004.http://downloads2.esri.com/campus/uploads/library/pdfs/58415.pdf
* [Turner, 1977] Eugene Turner, Life in LA, 1977 Retrieved at November 7, 2008. http://www.csun.edu/%7Ehfgeg005/eturner/gallery/
* [Morris et al., 2000] Christopher J. Morris, David S. Ebert, Penny Rheingans. An Experimental Analysis of the Effectiveness of Features in Chernoff Faces. 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making, Proceedings of SPIE, pages 12- 17, 2000. http://www.csee.umbc.edu/~ebert/papers/Chernoff_990402.PDF

File:Faces2.jpg

2008-11-07T17:27:12Z

UE-InfoVis0809 0425646: Joseph G. Spinelli and Yu Zhou. "Mapping Quality of Life with Chernoff Faces", 4th Annual ESRI Education User Conference 2004, San Diego, CA, August 2004. http://downloads2.esri.com/campus/uploads/library/pdfs/58415.pdf

== Summary ==
Joseph G. Spinelli and Yu Zhou. "Mapping Quality of Life with Chernoff Faces", 4th Annual ESRI Education User Conference 2004, San Diego, CA, August 2004. http://downloads2.esri.com/campus/uploads/library/pdfs/58415.pdf
== Copyright status ==
Joseph G. Spinelli and Yu Zhou
== Source ==
http://downloads2.esri.com/campus/uploads/library/pdfs/58415.pdf

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 1 - Chernoff Face

2008-11-07T02:30:52Z

UE-InfoVis0809 0425646:

{{Quotation|bla|[ExampleAuthor et al., 2008]}}

== Introduction ==
According to wikipedia a Chernoff face ''represents multivariate data in form of a face'' [Wikipedia, 2008]. So instead of looking at a bunch of different numbers in a vector a human can look on faces, where each face describes a data set which can contain up to 18 dimensions.

Also the slightest change of the vector results in a different face. Studying faces the whole life-time the human brain is more used to distinguish between faces than vectors. Chernoff faces therefore are often used in statistic presentations like in Dr. Eugen Turners Map “Life in Los Angeles”.[Turner, 1977]

[[Image:lifeinla.GIF|thumb|none|150px|Life in Los Angeles [Turner, 1977]]]

Faces are type of [[Glyph|glyph]], a graphical object whose properties represent data values

The idea of displaying faces to represent multivariate data was first published in 1973 in The Journal of American National Statistic by Herman Chernoff who was born in 1923 in America. Article: “The Use of Faces to Represent Points in K-Dimensional Space Graphically”. [Chernoff, 1973]

== The idea in detail ==
As already said Chernoff faces are simplified faces that can help viewers to detect patterns, groupings, and correlation. Having data sets each dimension is assigned to a facial characteristic. Facial characteristics are for example eybrow slant, eye spacing, head eccentricity, pupil size, and so on. Inside the data set each dimension represents a feature which can be classified. If we say for example that one feature of our face displays the rate of unemployment (rou) we could classify that rate into three classes e.g. rou < 3%, 3%< rou <6% or rou > 6%. So we have three classes for the feature rate of unemployment. As each feature describes a characteristic of a face we could say that the shape of the mouth could stand for the rate of unemployment as figure 2 demonstrates.

[[Image:Chernoff_rou.jpg]]

Hence, every face unambiguously describes one feature vector, which in turn combines several features to describe one condition, like the quality of life as in [Spinelli and Zhou].

== Critics ==
Chernoff faces may be not that effective it seems. Certain features like the perception of eye size, eyebrow slant and the combination of those both are more influencing for longer viewing times than others. Therefore [Morris et. al, 2000] came to the conclusion that the use of Chernoff faces ''"may not have a significant advantage over other iconic visualization techniques for multidimensional information visualization.”''

== References ==
* [ExampleAuthor et al., 2008] Firstname Lastname. Title of article. ''Title of Journal (in italic)'', Volume, number, Date of issue. Help [[Help:Citation_&_Bibilography_Format]]
* [Wikipedia, 2008] '''TODO''' bla
* [Chernoff, 1973] Herman Chernoff. The Use of Faces to Represent Points in k-Dimensional Space Graphically, ''Journal of the American Statistical Association'', 68 (#342): 361-368, 1973. http://www.jstor.org/pss/2284077
* [Spinelli and Zhou] '''TODO''' "Mapping Quality of Life with Chernoff Faces", Joseph G. Spinelli and Yu Zhou, http://gis.esri.com/library/userconf/educ04/papers/pap5000.pdf
* [Turner, 1977] Eugene Turner, Life in LA, 1977 Retrieved at November 7, 2008. http://www.csun.edu/%7Ehfgeg005/eturner/gallery/
* [Morris et al., 2000] Christopher J. Morris, David S. Ebert, Penny Rheingans. An Experimental Analysis of the Effectiveness of Features in Chernoff Faces. 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making, Proceedings of SPIE, pages 12- 17, 2000. http://www.csee.umbc.edu/~ebert/papers/Chernoff_990402.PDF

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 1 - Chernoff Face

2008-11-07T02:10:53Z

UE-InfoVis0809 0425646:

{{Quotation|bla|[ExampleAuthor et al., 2008]}}

== Introduction ==
According to wikipedia a Chernoff face ''represents multivariate data in form of a face'' [Wikipedia, 2008]. So instead of looking at a bunch of different numbers in a vector a human can look on faces, where each face describes a data set which can contain up to 18 dimensions.

Also the slightest change of the vector results in a different face. Studying faces the whole life-time the human brain is more used to distinguish between faces than vectors. Chernoff faces therefore are often used in statistic presentations like in Dr. Eugen Turners Map “Life in Los Angeles”.[Turner, 1977]

[[Image:lifeinla.GIF]]
[Turner, 1977]

Faces are type of [[Glyph|glyph]], a graphical object whose properties represent data values

The idea of displaying faces to represent multivariate data was first published in 1973 in The Journal of American National Statistic by Herman Chernoff who was born in 1923 in America. Article: “The Use of Faces to Represent Points in K-Dimensional Space Graphically”. [Chernoff, 1973]

== The idea in detail ==
As already said Chernoff faces are simplified faces that can help viewers to detect patterns, groupings, and correlation. Having data sets each dimension is assigned to a facial characteristic. Facial characteristics are for example eybrow slant, eye spacing, head eccentricity, pupil size, and so on. Inside the data set each dimension represents a feature which can be classified. If we say for example that one feature of our face displays the rate of unemployment (rou) we could classify that rate into three classes e.g. rou < 3%, 3%< rou <6% or rou > 6%. So we have three classes for the feature rate of unemployment. As each feature describes a characteristic of a face we could say that the shape of the mouth could stand for the rate of unemployment as figure 2 demonstrates.

[[Image:Chernoff_rou.jpg]]

Hence, every face unambiguously describes one feature vector, which in turn combines several features to describe one condition, like the quality of life as in [Spinelli and Zhou].

== Critics ==
Chernoff faces may be not that effective it seems. Certain features like the perception of eye size, eyebrow slant and the combination of those both are more influencing for longer viewing times than others. Therefore [Morris et. al, 2000] came to the conclusion that the use of Chernoff faces ''"may not have a significant advantage over other iconic visualization techniques for multidimensional information visualization.”''

== References ==
* [ExampleAuthor et al., 2008] Firstname Lastname. Title of article. ''Title of Journal (in italic)'', Volume, number, Date of issue. Help [[Help:Citation_&_Bibilography_Format]]
* [Wikipedia, 2008] '''TODO''' bla
* [Chernoff, 1973] Herman Chernoff. The Use of Faces to Represent Points in k-Dimensional Space Graphically, ''Journal of the American Statistical Association'', 68 (#342): 361-368, 1973. http://www.jstor.org/pss/2284077
* [Spinelli and Zhou] '''TODO''' "Mapping Quality of Life with Chernoff Faces", Joseph G. Spinelli and Yu Zhou, http://gis.esri.com/library/userconf/educ04/papers/pap5000.pdf
* [Turner, 1977] Eugene Turner, Life in LA, 1977 Retrieved at November 7, 2008. http://www.csun.edu/%7Ehfgeg005/eturner/gallery/
* [Morris et al., 2000] Christopher J. Morris, David S. Ebert, Penny Rheingans. An Experimental Analysis of the Effectiveness of Features in Chernoff Faces. 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making, Proceedings of SPIE, pages 12- 17, 2000. http://www.csee.umbc.edu/~ebert/papers/Chernoff_990402.PDF

File:Lifeinla.GIF

2008-11-07T02:09:32Z

UE-InfoVis0809 0425646: Eugene Turner 1977 Life in LA, 1970 http://www.csun.edu/%7Ehfgeg005/eturner/gallery/gallery.htm

== Summary ==
Eugene Turner
1977 Life in LA, 1970
http://www.csun.edu/%7Ehfgeg005/eturner/gallery/gallery.htm
== Copyright status ==
Eugene Turner
== Source ==
http://www.csun.edu/%7Ehfgeg005/eturner/gallery/lifeinla.GIF

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 1 - Chernoff Face

2008-11-07T02:04:36Z

UE-InfoVis0809 0425646:

{{Quotation|bla|[ExampleAuthor et al., 2008]}}

== Introduction ==
According to wikipedia a Chernoff face ''represents multivariate data in form of a face'' [Wikipedia, 2008]. So instead of looking at a bunch of different numbers in a vector a human can look on faces, where each face describes a data set which can contain up to 18 dimensions.

Also the slightest change of the vector results in a different face. Studying faces the whole life-time the human brain is more used to distinguish between faces than vectors. Chernoff faces therefore are often used in statistic presentations like in Dr. Eugen Turners Map “Life in Los Angeles”.[Turner, 1977]

[[Image:LifeInLosAngeles.jpg]]
[Turner, 1977]

Faces are type of [[Glyph|glyph]], a graphical object whose properties represent data values

The idea of displaying faces to represent multivariate data was first published in 1973 in The Journal of American National Statistic by Herman Chernoff who was born in 1923 in America. Article: “The Use of Faces to Represent Points in K-Dimensional Space Graphically”. [Chernoff, 1973]

== The idea in detail ==
As already said Chernoff faces are simplified faces that can help viewers to detect patterns, groupings, and correlation. Having data sets each dimension is assigned to a facial characteristic. Facial characteristics are for example eybrow slant, eye spacing, head eccentricity, pupil size, and so on. Inside the data set each dimension represents a feature which can be classified. If we say for example that one feature of our face displays the rate of unemployment (rou) we could classify that rate into three classes e.g. rou < 3%, 3%< rou <6% or rou > 6%. So we have three classes for the feature rate of unemployment. As each feature describes a characteristic of a face we could say that the shape of the mouth could stand for the rate of unemployment as figure 2 demonstrates.

[[Image:Chernoff_rou.jpg]]

Hence, every face unambiguously describes one feature vector, which in turn combines several features to describe one condition, like the quality of life as in [Spinelli and Zhou].

== Critics ==
Chernoff faces may be not that effective it seems. Certain features like the perception of eye size, eyebrow slant and the combination of those both are more influencing for longer viewing times than others. Therefore [Morris et. al, 2000] came to the conclusion that the use of Chernoff faces ''"may not have a significant advantage over other iconic visualization techniques for multidimensional information visualization.”''

== References ==
* [ExampleAuthor et al., 2008] Firstname Lastname. Title of article. ''Title of Journal (in italic)'', Volume, number, Date of issue. Help [[Help:Citation_&_Bibilography_Format]]
* [Wikipedia, 2008] '''TODO''' bla
* [Chernoff, 1973] Herman Chernoff. The Use of Faces to Represent Points in k-Dimensional Space Graphically, ''Journal of the American Statistical Association'', 68 (#342): 361-368, 1973. http://www.jstor.org/pss/2284077
* [Spinelli and Zhou] '''TODO''' "Mapping Quality of Life with Chernoff Faces", Joseph G. Spinelli and Yu Zhou, http://gis.esri.com/library/userconf/educ04/papers/pap5000.pdf
* [Turner, 1977] Eugene Turner, Life in LA, 1977 Retrieved at November 7, 2008. http://www.csun.edu/%7Ehfgeg005/eturner/gallery/
* [Morris et al., 2000] Christopher J. Morris, David S. Ebert, Penny Rheingans. An Experimental Analysis of the Effectiveness of Features in Chernoff Faces. 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making, Proceedings of SPIE, pages 12- 17, 2000. http://www.csee.umbc.edu/~ebert/papers/Chernoff_990402.PDF

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03 - Aufgabe 1 - Chernoff Face

2008-11-07T02:03:32Z

UE-InfoVis0809 0425646: New page: {{Quatation|bla|[ExampleAuthor et al., 2008]}} == Introduction == According to wikipedia a Chernoff face ''represents multivariate data in form of a face'' [Wikipedia, 2008]. So instead o...

{{Quatation|bla|[ExampleAuthor et al., 2008]}}

== Introduction ==
According to wikipedia a Chernoff face ''represents multivariate data in form of a face'' [Wikipedia, 2008]. So instead of looking at a bunch of different numbers in a vector a human can look on faces, where each face describes a data set which can contain up to 18 dimensions.

Also the slightest change of the vector results in a different face. Studying faces the whole life-time the human brain is more used to distinguish between faces than vectors. Chernoff faces therefore are often used in statistic presentations like in Dr. Eugen Turners Map “Life in Los Angeles”.[Turner, 1977]

[[Image:LifeInLosAngeles.jpg]]
[Turner, 1977]

Faces are type of [[Glyph|glyph]], a graphical object whose properties represent data values

The idea of displaying faces to represent multivariate data was first published in 1973 in The Journal of American National Statistic by Herman Chernoff who was born in 1923 in America. Article: “The Use of Faces to Represent Points in K-Dimensional Space Graphically”. [Chernoff, 1973]

== The idea in detail ==
As already said Chernoff faces are simplified faces that can help viewers to detect patterns, groupings, and correlation. Having data sets each dimension is assigned to a facial characteristic. Facial characteristics are for example eybrow slant, eye spacing, head eccentricity, pupil size, and so on. Inside the data set each dimension represents a feature which can be classified. If we say for example that one feature of our face displays the rate of unemployment (rou) we could classify that rate into three classes e.g. rou < 3%, 3%< rou <6% or rou > 6%. So we have three classes for the feature rate of unemployment. As each feature describes a characteristic of a face we could say that the shape of the mouth could stand for the rate of unemployment as figure 2 demonstrates.

[[Image:Chernoff_rou.jpg]]

Hence, every face unambiguously describes one feature vector, which in turn combines several features to describe one condition, like the quality of life as in [Spinelli and Zhou].

== Critics ==
Chernoff faces may be not that effective it seems. Certain features like the perception of eye size, eyebrow slant and the combination of those both are more influencing for longer viewing times than others. Therefore [Morris et. al, 2000] came to the conclusion that the use of Chernoff faces ''"may not have a significant advantage over other iconic visualization techniques for multidimensional information visualization.”''

== References ==
* [ExampleAuthor et al., 2008] Firstname Lastname. Title of article. ''Title of Journal (in italic)'', Volume, number, Date of issue. Help [[Help:Citation_&_Bibilography_Format]]
* [Wikipedia, 2008] '''TODO''' bla
* [Chernoff, 1973] Herman Chernoff. The Use of Faces to Represent Points in k-Dimensional Space Graphically, ''Journal of the American Statistical Association'', 68 (#342): 361-368, 1973. http://www.jstor.org/pss/2284077
* [Spinelli and Zhou] '''TODO''' "Mapping Quality of Life with Chernoff Faces", Joseph G. Spinelli and Yu Zhou, http://gis.esri.com/library/userconf/educ04/papers/pap5000.pdf
* [Turner, 1977] Eugene Turner, Life in LA, 1977 Retrieved at November 7, 2008. http://www.csun.edu/%7Ehfgeg005/eturner/gallery/
* [Morris et al., 2000] Christopher J. Morris, David S. Ebert, Penny Rheingans. An Experimental Analysis of the Effectiveness of Features in Chernoff Faces. 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making, Proceedings of SPIE, pages 12- 17, 2000. http://www.csee.umbc.edu/~ebert/papers/Chernoff_990402.PDF

User:UE-InfoVis0809 0425646

2008-10-27T21:30:24Z

UE-InfoVis0809 0425646:

== DINCER, Engin ==

[[Image:UE-InfoVis0809_0425646.jpg|thumb|200px|right]]

* Matrikelnummer: 0425646
* Studienkennzahl: 066 935
* Mail: engin.dincer[at]student.tuwien.ac.at

== Links ==
* [[Teaching:TUW_-_UE_InfoVis_WS_2008/09|InfoVis:Wiki UE Homepage]]
** [[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 03|Gruppe 03]]
*** [[User:UE-InfoVis0809_0225026|Mayer, Philipp]]
*** [[User:UE-InfoVis0809_0201183|Moser, Bernd]]

User:UE-InfoVis0809 0425646

2008-10-27T21:12:24Z

UE-InfoVis0809 0425646: New page: == DINCER, Engin == right * Matrikelnummer: 0425646 * Studienkennzahl: 066 935 * Mail: engin.dincer[at]student.tuwien.ac.at

== DINCER, Engin ==

[[Image:UE-InfoVis0809_0425646.jpg|thumb|200px|right]]

* Matrikelnummer: 0425646
* Studienkennzahl: 066 935
* Mail: engin.dincer[at]student.tuwien.ac.at

File:UE-InfoVis0809 0425646.jpg

2008-10-27T21:00:19Z

UE-InfoVis0809 0425646: New page: == Summary == == Copyright status == == Source ==