InfoVis:Wiki - User contributions [en]

File:Detailstammbaum1gruppe11.jpg

2010-01-07T21:36:27Z

0500280: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

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File:Filter-gruppe11.png

2010-01-07T21:35:19Z

0500280: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

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Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 11 - Aufgabe 4

2010-01-06T17:54:41Z

0500280:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe4.html Beschreibung der Aufgabe 4]
=== Zu erstellende Visualisierung ===
-------------------------------
* Stammbaum der Nachkommen von Lisa und Bart Simpson*

...Visualisierung der Nachkommen von Lisa Simpson sowie der Nachkommen von Bart Simpson. Dabei sollen zwei Stammbäume entstehen - einer von Bart und einer von Lisa - die dann miteinander verglichen werden können. Zuerst kommen Lisa und Bart, dann deren Kinder, ihre Enkel, etc. (mind 4 Generationen). Da es noch keine Nachkommen gibt, können diese frei erfunden werden.

Die Visualisierung soll folgende Informationen darstellen:

- Verwandtschaftsverhältnisse (zumindest Eltern-Kinder),

- Unterscheidung zwischen Blutsverwandtschaft und angeheirateten Familienmitgliedern,

- Geburts- und Todestag sowie Lebensdauer von allen Familienmitgliedern,

- wichtige Ereignisse im Leben jedes Familienmitglieds (z.B., Anzeigen, Gefängnisaufenthalte, Schulzeit, Studienzeit, Nobelpreise, Arbeitslosigkeit etc.)

- Zufriedenheit jedes Familienmitglieds (Skala: sehr niedrig - niedrig - mittel - hoch - sehr hoch); kann sich im Laufe des Lebens ändern.

Die Visualisierung soll die interaktive Auseinandersetzung mit den Daten ermöglichen.
Verpflichtend:
Möglichkeiten zum besseren Vergleich von einzelnen Abschnitten der Stammbäume bzw. Vergleich von Ausschnitten aus Lisas und Barts Stammbäumen.
+ mind. 2 weitere Interaktionsmöglichkeiten (z.B., Details on Demand, Filteroptionen)

Allgemein:

- Die Daten sollen zur Analyse von Zusammenhängen zwischen Familienverhältnissen, wichtigen Ereignissen und Zufriedenheit visualisiert werden (die Anwendungsgebiets- und Zielgruppenanalyse kann kurz gehalten werden).

- Die bisher erlernten Design-Prinzipien sollen umgesetzt werden z.B.: Optimierung der Data-ink ratio (keine Comics!), visuelle Attribute (Größe, Farbe, Position, etc.) sollen sinnvoll eingesetzt werden (Information darstellen).

- Die Mockups sollten zumindest 1) die beiden Stammbäume im Überblick und 2) eine detaillierte Vergleichsansicht von 2 Teil-Stammbäumen wiedergeben.

- Alle nicht angeführten Daten können frei erfunden werden.

------------------------------

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2009/10|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 11|Gruppe 11]]

== Visualisation ==

------------------------------

=== Information ===

The following graphic shows the familiy tree from Bart and Lisa Simpson. On the top is the beginning with Bart and Lisa. Below you can find their childs, grandchilds, great-grandchilds and the childs of the great-grandchilds. Males are blue and females are pink characterised.

=== Family relationship ===

A thick line show the family relationship between parents and childs.

=== Difference between blood relationship and releated by mariage people ===

A thick line show the blood relationship and two overlapped circles show the related by mariage persons.

=== Date of birth and day of death ===

Both dates are shown in the family tree graphic under the names of the persons.

=== Age, happiness and important events in life ===

If you will know the age, happiness or the important events in life of a person you should double-klick on the specific box of this person.

[[Image:Naeherinfosgruppe11.jpg]]

=== Family tree from Bart and Lisa Simpson ===

[[Image:SimpStbGruppe11.jpg]]

File:Naeherinfosgruppe11.jpg

2010-01-06T17:47:59Z

0500280: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 11 - Aufgabe 4

2010-01-06T15:51:39Z

0500280:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe4.html Beschreibung der Aufgabe 4]
=== Zu erstellende Visualisierung ===
-------------------------------
* Stammbaum der Nachkommen von Lisa und Bart Simpson*

...Visualisierung der Nachkommen von Lisa Simpson sowie der Nachkommen von Bart Simpson. Dabei sollen zwei Stammbäume entstehen - einer von Bart und einer von Lisa - die dann miteinander verglichen werden können. Zuerst kommen Lisa und Bart, dann deren Kinder, ihre Enkel, etc. (mind 4 Generationen). Da es noch keine Nachkommen gibt, können diese frei erfunden werden.

Die Visualisierung soll folgende Informationen darstellen:

- Verwandtschaftsverhältnisse (zumindest Eltern-Kinder),

- Unterscheidung zwischen Blutsverwandtschaft und angeheirateten Familienmitgliedern,

- Geburts- und Todestag sowie Lebensdauer von allen Familienmitgliedern,

- wichtige Ereignisse im Leben jedes Familienmitglieds (z.B., Anzeigen, Gefängnisaufenthalte, Schulzeit, Studienzeit, Nobelpreise, Arbeitslosigkeit etc.)

- Zufriedenheit jedes Familienmitglieds (Skala: sehr niedrig - niedrig - mittel - hoch - sehr hoch); kann sich im Laufe des Lebens ändern.

Die Visualisierung soll die interaktive Auseinandersetzung mit den Daten ermöglichen.
Verpflichtend:
Möglichkeiten zum besseren Vergleich von einzelnen Abschnitten der Stammbäume bzw. Vergleich von Ausschnitten aus Lisas und Barts Stammbäumen.
+ mind. 2 weitere Interaktionsmöglichkeiten (z.B., Details on Demand, Filteroptionen)

Allgemein:

- Die Daten sollen zur Analyse von Zusammenhängen zwischen Familienverhältnissen, wichtigen Ereignissen und Zufriedenheit visualisiert werden (die Anwendungsgebiets- und Zielgruppenanalyse kann kurz gehalten werden).

- Die bisher erlernten Design-Prinzipien sollen umgesetzt werden z.B.: Optimierung der Data-ink ratio (keine Comics!), visuelle Attribute (Größe, Farbe, Position, etc.) sollen sinnvoll eingesetzt werden (Information darstellen).

- Die Mockups sollten zumindest 1) die beiden Stammbäume im Überblick und 2) eine detaillierte Vergleichsansicht von 2 Teil-Stammbäumen wiedergeben.

- Alle nicht angeführten Daten können frei erfunden werden.

------------------------------

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2009/10|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 11|Gruppe 11]]

== Visualisation ==

------------------------------

=== Information ===

The following graphic shows the familiy tree from Bart and Lisa Simpson. On the top is the beginning with Bart and Lisa. Below you can find their childs, grandchilds, great-grandchilds and the childs of the great-grandchilds. Males are blue and females are pink characterised.

=== Family relationship ===

A thick line show the family relationship between parents and childs.

=== Difference between blood relationship and releated by mariage people ===

A thick line show the blood relationship and two overlapped circles show the related by mariage persons.

=== Date of birth and day of death ===

Both dates are shown in the family tree graphic under the names of the persons.

=== Family tree from Bart and Lisa Simpson ===

[[Image:SimpStbGruppe11.jpg]]

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 11 - Aufgabe 4

2010-01-06T15:50:11Z

0500280:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe4.html Beschreibung der Aufgabe 4]
=== Zu erstellende Visualisierung ===
-------------------------------
* Stammbaum der Nachkommen von Lisa und Bart Simpson*

...Visualisierung der Nachkommen von Lisa Simpson sowie der Nachkommen von Bart Simpson. Dabei sollen zwei Stammbäume entstehen - einer von Bart und einer von Lisa - die dann miteinander verglichen werden können. Zuerst kommen Lisa und Bart, dann deren Kinder, ihre Enkel, etc. (mind 4 Generationen). Da es noch keine Nachkommen gibt, können diese frei erfunden werden.

Die Visualisierung soll folgende Informationen darstellen:

- Verwandtschaftsverhältnisse (zumindest Eltern-Kinder),

- Unterscheidung zwischen Blutsverwandtschaft und angeheirateten Familienmitgliedern,

- Geburts- und Todestag sowie Lebensdauer von allen Familienmitgliedern,

- wichtige Ereignisse im Leben jedes Familienmitglieds (z.B., Anzeigen, Gefängnisaufenthalte, Schulzeit, Studienzeit, Nobelpreise, Arbeitslosigkeit etc.)

- Zufriedenheit jedes Familienmitglieds (Skala: sehr niedrig - niedrig - mittel - hoch - sehr hoch); kann sich im Laufe des Lebens ändern.

Die Visualisierung soll die interaktive Auseinandersetzung mit den Daten ermöglichen.
Verpflichtend:
Möglichkeiten zum besseren Vergleich von einzelnen Abschnitten der Stammbäume bzw. Vergleich von Ausschnitten aus Lisas und Barts Stammbäumen.
+ mind. 2 weitere Interaktionsmöglichkeiten (z.B., Details on Demand, Filteroptionen)

Allgemein:

- Die Daten sollen zur Analyse von Zusammenhängen zwischen Familienverhältnissen, wichtigen Ereignissen und Zufriedenheit visualisiert werden (die Anwendungsgebiets- und Zielgruppenanalyse kann kurz gehalten werden).

- Die bisher erlernten Design-Prinzipien sollen umgesetzt werden z.B.: Optimierung der Data-ink ratio (keine Comics!), visuelle Attribute (Größe, Farbe, Position, etc.) sollen sinnvoll eingesetzt werden (Information darstellen).

- Die Mockups sollten zumindest 1) die beiden Stammbäume im Überblick und 2) eine detaillierte Vergleichsansicht von 2 Teil-Stammbäumen wiedergeben.

- Alle nicht angeführten Daten können frei erfunden werden.

------------------------------

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2009/10|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 11|Gruppe 11]]

== Visualisation ==

------------------------------

== Information ==

The following graphic shows the familiy tree from Bart and Lisa Simpson. On the top is the beginning with Bart and Lisa. Below you can find their childs, grandchilds, great-grandchilds and the childs of the great-grandchilds. Males are blue and females are pink characterised.

== Family relationship ==

A thick line show the family relationship between parents and childs.

== Difference between blood relationship and releated by mariage people ==

A thick line show the blood relationship and two overlapped circles show the related by mariage persons.

== Date of birth and day of death ==

Both dates are shown in the family tree graphic under the names of the persons.

=== Family tree from Bart and Lisa Simpson ===

[[Image:SimpStbGruppe11.jpg]]

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 11 - Aufgabe 4

2010-01-06T15:12:12Z

0500280:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe4.html Beschreibung der Aufgabe 4]
=== Zu erstellende Visualisierung ===
-------------------------------
* Stammbaum der Nachkommen von Lisa und Bart Simpson*

...Visualisierung der Nachkommen von Lisa Simpson sowie der Nachkommen von Bart Simpson. Dabei sollen zwei Stammbäume entstehen - einer von Bart und einer von Lisa - die dann miteinander verglichen werden können. Zuerst kommen Lisa und Bart, dann deren Kinder, ihre Enkel, etc. (mind 4 Generationen). Da es noch keine Nachkommen gibt, können diese frei erfunden werden.

Die Visualisierung soll folgende Informationen darstellen:

- Verwandtschaftsverhältnisse (zumindest Eltern-Kinder),

- Unterscheidung zwischen Blutsverwandtschaft und angeheirateten Familienmitgliedern,

- Geburts- und Todestag sowie Lebensdauer von allen Familienmitgliedern,

- wichtige Ereignisse im Leben jedes Familienmitglieds (z.B., Anzeigen, Gefängnisaufenthalte, Schulzeit, Studienzeit, Nobelpreise, Arbeitslosigkeit etc.)

- Zufriedenheit jedes Familienmitglieds (Skala: sehr niedrig - niedrig - mittel - hoch - sehr hoch); kann sich im Laufe des Lebens ändern.

Die Visualisierung soll die interaktive Auseinandersetzung mit den Daten ermöglichen.
Verpflichtend:
Möglichkeiten zum besseren Vergleich von einzelnen Abschnitten der Stammbäume bzw. Vergleich von Ausschnitten aus Lisas und Barts Stammbäumen.
+ mind. 2 weitere Interaktionsmöglichkeiten (z.B., Details on Demand, Filteroptionen)

Allgemein:

- Die Daten sollen zur Analyse von Zusammenhängen zwischen Familienverhältnissen, wichtigen Ereignissen und Zufriedenheit visualisiert werden (die Anwendungsgebiets- und Zielgruppenanalyse kann kurz gehalten werden).

- Die bisher erlernten Design-Prinzipien sollen umgesetzt werden z.B.: Optimierung der Data-ink ratio (keine Comics!), visuelle Attribute (Größe, Farbe, Position, etc.) sollen sinnvoll eingesetzt werden (Information darstellen).

- Die Mockups sollten zumindest 1) die beiden Stammbäume im Überblick und 2) eine detaillierte Vergleichsansicht von 2 Teil-Stammbäumen wiedergeben.

- Alle nicht angeführten Daten können frei erfunden werden.

------------------------------

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2009/10|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 11|Gruppe 11]]

=== Familienstammbaum von Bart und Lisa Simpson ===

[[Image:SimpStbGruppe11.jpg]]

File:SimpStbGruppe11.jpg

2010-01-06T15:08:35Z

0500280: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 11 - Aufgabe 3

2009-12-29T23:39:58Z

0500280:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe3.html Beschreibung der Aufgabe 3]
=== Zu verbessernde Grafik ===
------------------------------- 
[[Image:guardian.png]]

=== Positives ===

* colours show the regions

=== Critique on the existing graphic ===

* it is very difficult to interpret
* there is a growth in carbon emissions of 28 % but this is not shown in the graphic
* most of the datas in the circles are not assignable to specific countries
* you can't click on the circles below
* the big dotted circle is expressionless
* you can see the growth in carbon emissions in per cent only at three of the five bigest CO2 producers
* the dimension of the circles is expressionless (sometimes bigger circles for low numbers and smaller circles for high numbers)
* the sum of the numbers from the circles is not the same as the given number of the million tonnes of CO2

=== Verbesserte Grafik ===
------------------------------- 
[[Image:Ueberarbeitetegrafikgruppe11.jpg]]
 
[[Image:Gruppe11Aufgabe3Tabelle.jpg]]

=== Changes ===

* sum up the several circles to big regions -> table is more clearly arranged
* remove the given number of the million tonnes of CO2 (-> irrelevant, does not arise from the graphic), the growth in carbon emissions (-> is not shown in the graphic) und "Click of the circles below" (-> misleading)
* bar chart instead of the circles -> table is more clearly arranged
* table with the five top CO2 producers -> you can compare the countries better
* no different colours -> not essential

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 11 - Aufgabe 3

2009-12-29T23:20:54Z

0500280:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe3.html Beschreibung der Aufgabe 3]
=== Zu verbessernde Grafik ===
------------------------------- 
[[Image:guardian.png]]

=== Positives ===

* colours show the regions

=== Critique on the existing graphic ===

* it is very difficult to interpret
* there is a growth in carbon emissions of 28 % but this is not shown in the graphic
* most of the datas in the circles are not assignable to specific countries
* you can't click on the circles below
* the big dotted circle is expressionless
* you can see the growth in carbon emissions in per cent only at three of the five bigest CO2 producers
* the dimension of the circles is expressionless (sometimes bigger circles for low numbers and smaller circles for high numbers)
* the sum of the numbers from the circles is not the same as the given number of the million tonnes of CO2

=== Verbesserte Grafik ===
------------------------------- 
[[Image:Ueberarbeitetegrafikgruppe11.jpg]]
 
[[Image:Gruppe11Aufgabe3Tabelle.jpg]]

=== Changes ===

* sum up the several circles to big regions
* remove the given number of the million tonnes of CO2, the growth in carbon emissions und "Click of the circles below"
* bar chart instead of the circles
* table with the five top CO2 producers
* no different colours

File:Ueberarbeitetegrafikgruppe11.jpg

2009-12-29T23:19:36Z

0500280: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

Teaching talk:TUW - UE InfoVis WS 2009/10 - Gruppe 11 - Aufgabe 3

2009-12-08T14:17:40Z

0500280: New page: === Positives === * colours show the regions === Kritik an der vorliegenden Grafik === * it is very difficult to interpret * there is a growth in carbon emissions of 28 % but this is n...

=== Positives ===

* colours show the regions

=== Kritik an der vorliegenden Grafik ===

* it is very difficult to interpret
* there is a growth in carbon emissions of 28 % but this is not shown in the graphic
* most of the datas in the circles are not assignable to specific countries
* you can't click on the circles below
* the big dotted circle is expressionless
* you can see the growth in carbon emissions in per cent only at three of the five bigest CO2 producers
* the dimension of the circles is expressionless (sometimes bigger circles for low numbers and smaller circles for high numbers)
* the sum of the numbers from the circles is not the same as the given number of the million tonnes of CO2

=== Änderungen ===

* sum up the several circles to big regions
* remove the given number of the million tonnes of CO2, the growth in carbon emissions und "Click of the circles below"
* bar chart instead of the circles
* table with the five top CO2 producers
* no different colours

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 11 - Aufgabe 3

2009-12-08T13:37:14Z

0500280:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe3.html Beschreibung der Aufgabe 3]
=== Zu verbessernde Grafik ===
------------------------------- 
[[Image:guardian.png]]

=== Verbesserte Grafik ===
------------------------------- 
[[Image:Gruppe11Aufgabe3Diagramm.jpg]]
 
[[Image:Gruppe11Aufgabe3Tabelle.jpg]]

File:Gruppe11Aufgabe3Tabelle.jpg

2009-12-08T13:36:15Z

0500280: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 11 - Aufgabe 3

2009-12-08T13:35:40Z

0500280:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe3.html Beschreibung der Aufgabe 3]
=== Zu verbessernde Grafik ===
------------------------------- 
[[Image:guardian.png]]

=== Verbesserte Grafik ===
------------------------------- 
[[Image:Gruppe11Aufgabe3Diagramm.jpg]]

File:Gruppe11Aufgabe3Diagramm.jpg

2009-12-08T13:34:28Z

0500280: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G3 - Aufgabe 1 - Preattentive Processing

2009-11-23T19:04:40Z

0500280:

= Preattentive Processing =
{{Quotation|Visualization is so effective and useful because it utilizes one of the channels to our brain that have the highest bandwidths: our eyes. But even this channel can be used more or less efficiently. One special property of our visual system is preattentive processing. |[Ware, 2000; Treisman, 1985]}}

[Healey et al., 1996] mention in their paper that only a limited set of visual properties exists. Tasks, which can be accomplished within 200 milliseconds, are considered preattentive because eye movements need this time to initiate. Everything which can be perceived in this short time includes only absconding information. Attention can not be prefocused on a particular position if elements are set random.

== Preattentive Features ==

[[Image:Features.jpg|thumb|none|400px|
Figure 1: A list of two-dimensional features that “pop out” during visual search, and a list of authors who describe preattentive tasks performed using the given feature. [Healey et al., 1996]]]

== Examples ==

{|
|-
| [[Image:Preattantive_1.jpg|thumb|none|300px|Figure 2: Detecting the Red Object preattentively. [Chipman, 1996] One visual variable is very easy to find it. ]]
| [[Image:Preattantive_2.jpg|thumb|none|300px|Figure 3: Detecting the Circle preattentively.[Chipman, 1996] It is more difficult but still preantentiv.]]
|-
| [[Image:Preattentive_4.JPG|thumb|none|450px|Figure 4: Examples of two target detection tasks: (a) target can be detected preattentively because it possess the feature “filled”; (b) target cannot be detected preattentively because it has no visual feature that is unique from its distractors. [Healey et al., 1996]]]
| [[Image:Preattentive_3.JPG|thumb|none|450px|Figure 5: Examples of two target detection tasks: (a) target can be detected preattentively because it possess the feature “filled”; (b) target cannot be detected preattentively because it has no visual feature that is unique from its distractors. [Healey et al., 1996]]]
|}

==Conclusion==
Any visual processing of that item prior to the act of selection can be called “preattentive”.[Wolfe, Treisma, 2003]

Preattentive processing can help to rapidly draw the focus of attention to a target with a unique visual feature (i.e., little or no searching is required in the preattentive case). [Healey, 2005]

==Ressources==

*[Healey et al., 1996] Healey, C. G., Booth, K. S., and Enns, J. T.. High-Speed Visual Estimation Using Preattentive Processing. ''ACM Transactions on Human Computer Interaction'' 3(2), pages 107-135, Created at: 1996. http://www.csc.ncsu.edu/faculty/healey/download/tochi.96.pdf .

*[Wolfe, Treisma, 2003] Jeremy M Wolfe, Anne Treisma. What shall we do with the preattentive processing stage: Use it or lose it?, ''Todd S Horowitz poster presented at the Third Annual Meeting of the Vision Sciences Society''. Sarasota. Created at: May, 2003. http://search.bwh.harvard.edu/links/talks/VSS03-JMW.pdf .

*[Healey, 2005] Christopher G. Healey. Perception in Visualization. Department of Computer Science, North Carolina State University. Created at: May, 2005. http://www.csc.ncsu.edu/faculty/healey/PP/index.html#Tri_Cog_Psych:80 .

*[Chipman, 1996] Gene Chipman. Review of High Speed Visual Estimation Using Preattantive Processing (Healy, Booth and Enns). Created at: 1996, Accessed at: November 05, 2009. http://www.cs.umd.edu/class/spring2002/cmsc838f/preattentive.ppt#267 .

*[Treisman, 1985] A. Treisman. Preattentive processing in vision. Computer Vision, Graphics, and Image Processing, 31:156–177, 1985.

*[Ware, 2000] C. Ware. Information Visualization: Perception for Design. Morgan Kaufmann Publishers, 2000.

Teaching talk:TUW - UE InfoVis WS 2005/06 - Gruppe G3 - Aufgabe 1 - Preattentive Processing

2009-11-23T11:24:46Z

0500280:

== Bearbeitung 05.11.2009 ==
* Tabelle durch ein Bild ersetzt
* Quelle zu Preattentive_1.jpg richtig gestellt
* 1.Zitat auf richtige Autoren ausgebessert und Quellen hinzugefügt

== Bearbeitung 10.11.2009 ==
* Preattentive.jpg durch richtige Tabelle ersetzt
* Quelle dazu richtig gestellt
* Quelle entfernt
* Examples neu formatiert
* Überschriften neu formatiert

== Bearbeitung 23.11.2009 ==
*langes Zitat umformuliert

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G3 - Aufgabe 1 - Preattentive Processing

2009-11-23T11:23:39Z

0500280:

= Preattentive Processing =
{{Quotation|Visualization is so effective and useful because it utilizes one of the channels to our brain that have the highest bandwidths: our eyes. But even this channel can be used more or less efficiently. One special property of our visual system is preattentive processing. |[Ware, 2000; Treisman, 1985]}}

[Healey et al., 1996] shows that a discovery of the past 20 years was that there is a limited set of visual properties. Tasks, which can be accomplished within 200 milliseconds, are considered preattentive because eye movements need this time to initiate. Everything which can be perceived in this short time includes only absconding information. Attention can not be prefocused on a particular position if elements are set random.

== Preattentive Features ==

[[Image:Features.jpg|thumb|none|400px|
Figure 1: A list of two-dimensional features that “pop out” during visual search, and a list of authors who describe preattentive tasks performed using the given feature. [Healey et al., 1996]]]

== Examples ==

{|
|-
| [[Image:Preattantive_1.jpg|thumb|none|300px|Figure 2: Detecting the Red Object preattentively. [Chipman, 1996] One visual variable is very easy to find it. ]]
| [[Image:Preattantive_2.jpg|thumb|none|300px|Figure 3: Detecting the Circle preattentively.[Chipman, 1996] It is more difficult but still preantentiv.]]
|-
| [[Image:Preattentive_4.JPG|thumb|none|450px|Figure 4: Examples of two target detection tasks: (a) target can be detected preattentively because it possess the feature “filled”; (b) target cannot be detected preattentively because it has no visual feature that is unique from its distractors. [Healey et al., 1996]]]
| [[Image:Preattentive_3.JPG|thumb|none|450px|Figure 5: Examples of two target detection tasks: (a) target can be detected preattentively because it possess the feature “filled”; (b) target cannot be detected preattentively because it has no visual feature that is unique from its distractors. [Healey et al., 1996]]]
|}

==Conclusion==
Any visual processing of that item prior to the act of selection can be called “preattentive”.[Wolfe, Treisma, 2003]

Preattentive processing can help to rapidly draw the focus of attention to a target with a unique visual feature (i.e., little or no searching is required in the preattentive case). [Healey, 2005]

==Ressources==

*[Healey et al., 1996] Healey, C. G., Booth, K. S., and Enns, J. T.. High-Speed Visual Estimation Using Preattentive Processing. ''ACM Transactions on Human Computer Interaction'' 3(2), pages 107-135, Created at: 1996. http://www.csc.ncsu.edu/faculty/healey/download/tochi.96.pdf .

*[Wolfe, Treisma, 2003] Jeremy M Wolfe, Anne Treisma. What shall we do with the preattentive processing stage: Use it or lose it?, ''Todd S Horowitz poster presented at the Third Annual Meeting of the Vision Sciences Society''. Sarasota. Created at: May, 2003. http://search.bwh.harvard.edu/links/talks/VSS03-JMW.pdf .

*[Healey, 2005] Christopher G. Healey. Perception in Visualization. Department of Computer Science, North Carolina State University. Created at: May, 2005. http://www.csc.ncsu.edu/faculty/healey/PP/index.html#Tri_Cog_Psych:80 .

*[Chipman, 1996] Gene Chipman. Review of High Speed Visual Estimation Using Preattantive Processing (Healy, Booth and Enns). Created at: 1996, Accessed at: November 05, 2009. http://www.cs.umd.edu/class/spring2002/cmsc838f/preattentive.ppt#267 .

*[Treisman, 1985] A. Treisman. Preattentive processing in vision. Computer Vision, Graphics, and Image Processing, 31:156–177, 1985.

*[Ware, 2000] C. Ware. Information Visualization: Perception for Design. Morgan Kaufmann Publishers, 2000.

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

2009-11-22T21:13:21Z

0500280:

==Bearbeitung: 04.11.2009==

Das erste Zitat wurde geändert, da die Wikipedia Seite dazu geändert wurde. Hierzu wurde auch der Quellenverweis aktualisiert.

Weiters wurde das zweite Zitat geändert, da es nicht mit dem auf der Originalseite übereinstimmte.

Referenzen wurden ausgebessert und mit aktuellen Datum versehen.

==Bearbeitung: 05.11.2009==

Alle Referenzen durchgesichtet und laut den Richtlinien berichtigt.

Artikel durchgelesen und etliche Stellen (1. Absatz und 3. Absatz) umgeschrieben, da diese etwas verwirrend waren.

==Bearbeitung: 22.11.2009==

Ausbesserung des ersten Satzes bei den Kritiken

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

2009-11-22T21:10:43Z

0500280:

== Introduction ==
{{Quotation| Chernoff faces display multivariate data in the shape of a human face. |[Wikipedia, 2009]}}

Instead of using a vector representation for describing sets of data, a vector is represented by a face which can hold up to 18 dimensions. Changes of vectors result in different faces, which the human brain can treat better than vectors. Chernoff faces are often used in statistic presentations as Figure 1 shows.

[[Image:lifeinla.GIF|thumb|none|right|150px|Fig. 1: Life in Los Angeles [Turner, 1977]]]

{{Quotation| These faces are a type of [[Glyph|glyph]], a graphical object whose properties represent data values. |[Kosara, 2007]}}

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.

== The idea in detail ==
Chernoff faces are simplified faces that can help viewers to detect patterns, groupings, and correlation. Each dimension is assigned to a facial characteristic, for example eyebrow slant, eye spacing, head eccentricity, pupil size, et cetera. 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 (rate) we could classify that rate into three classes e.g. rate < 3%, 3% < rate < 6% or rate > 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|right|150px|Fig. 2: 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 are not always the best solution for visualizing of multidimensional information. 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, 2009] Wikipedia contributors. Chernoff_face. Wikipedia, the free encyclopedia. Last modified on: October 8, 2009, at 17:34. Retrieved at: November 4, 2009. http://en.wikipedia.org/wiki/Chernoff_face
* [Turner, 1977] Eugene Turner. Life in LA. Last modified on: November 1, 1998. Retrieved at: November 5, 2009. http://www.csun.edu/~hfgeg005/eturner/gallery/lifeinla.GIF
* [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://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.948
* [Kosara, 2007] Robert Kosara. A Critique of Chernoff Faces. Created at: February 25, 2007. Retrieved at: November 4, 2009. http://eagereyes.org/VisCrit/ChernoffFaces.html

Teaching talk:TUW - UE InfoVis WS 2009/10 - Gruppe 11 - Aufgabe 2

2009-11-20T19:16:08Z

0500280:

=== Kritik an der vorliegenden Tabelle ===

* The unit in the column 'Bandwidth' should be the same, not GB and MB only GB or only MB
* A line above the row 'Total' should be inserted to divide the footer from the other values
* The Cell 'Month' and the row 'Total' should not be highlighted with a color because it's not used as a bar in the chart
* It would be also good to draw a line after the 1st row to divide the header from the data
* Maybe the Months from Sep2007 to Dec2007 could be deleted because they refer obviously to data in the future
* Numbers should be aligned to the right
* There should be a thousand separator

=== Erklärung der vorgenommenen Verbesserungen ===

* The Months from Sep2007 to Dec2007 have been deleted from the Table and the Chart
* The values of the column 'Bandwidth' have been changed to GB
* Strokes after the 1st row and before 'Total' have been inserted
* Highlighted Cell 'Month' and Row 'Total' have been changed to with background
* Numbers aligned to the right
* Aug 2007 from bold to normal
* thousand separator has been set

Teaching talk:TUW - UE InfoVis WS 2009/10 - Gruppe 11 - Aufgabe 2

2009-11-20T19:08:14Z

0500280:

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

2009-11-05T21:51:20Z

0500280:

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

2009-11-05T21:50:45Z

0500280:

==Bearbeitung: 04.11.2009==

Das erste Zitat wurde geändert, da die Wikipedia Seite dazu geändert wurde. Hierzu wurde auch der Quellenverweis aktualisiert.

Weiters wurde das zweite Zitat geändert, da es nicht mit dem auf der Originalseite übereinstimmte.

Referenzen wurden ausgebessert und mit aktuellen Datum versehen.

==Bearbeitung: 05.11.2009==

Alle Referenzen durchgesichtet und laut den Richtlinien berichtigt.
Artikel durchgelesen und etliche Stellen (1. Absatz und 3. Absatz) umgeschrieben, da diese etwas verwirrend waren.

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

2009-11-05T21:46:49Z

0500280:

== Introduction ==
{{Quotation| Chernoff faces display multivariate data in the shape of a human face. |[Wikipedia, 2009]}}

Instead of using a vector representation for describing sets of data, a vector is represented by a face which can hold up to 18 dimensions. Changes of vectors result in different faces, which the human brain can treat better than vectors. Chernoff faces are often used in statistic presentations as Figure 1 shows.

[[Image:lifeinla.GIF|thumb|none|right|150px|Fig. 1: Life in Los Angeles [Turner, 1977]]]

{{Quotation| These faces are a type of [[Glyph|glyph]], a graphical object whose properties represent data values. |[Kosara, 2007]}}

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.

== The idea in detail ==
Chernoff faces are simplified faces that can help viewers to detect patterns, groupings, and correlation. Each dimension is assigned to a facial characteristic, for example eyebrow slant, eye spacing, head eccentricity, pupil size, et cetera. 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 (rate) we could classify that rate into three classes e.g. rate < 3%, 3% < rate < 6% or rate > 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|right|150px|Fig. 2: 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 are not that effective as 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, 2009] Wikipedia contributors. Chernoff_face. Wikipedia, the free encyclopedia. Last modified on: October 8, 2009, at 17:34. Retrieved at: November 4, 2009. http://en.wikipedia.org/wiki/Chernoff_face
* [Turner, 1977] Eugene Turner. Life in LA. Last modified on: November 1, 1998. Retrieved at: November 5, 2009. http://www.csun.edu/~hfgeg005/eturner/gallery/lifeinla.GIF
* [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://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.948
* [Kosara, 2007] Robert Kosara. A Critique of Chernoff Faces. Created at: February 25, 2007. Retrieved at: November 4, 2009. http://eagereyes.org/VisCrit/ChernoffFaces.html

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

2009-11-05T21:46:03Z

0500280:

== Introduction ==
{{Quotation| Chernoff faces display multivariate data in the shape of a human face. |[Wikipedia, 2009]}}

Instead of using a vector representation for describing sets of data, a vector is represented by a face which can hold up to 18 dimensions. Changes of vectors result in different faces, which the human brain can treat better than vectors. Chernoff faces are often used in statistic presentations as Figure 1 shows. 

[[Image:lifeinla.GIF|thumb|none|right|150px|Fig. 1: Life in Los Angeles [Turner, 1977]]]

{{Quotation| These faces are a type of [[Glyph|glyph]], a graphical object whose properties represent data values. |[Kosara, 2007]}}

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.

== The idea in detail ==
Chernoff faces are simplified faces that can help viewers to detect patterns, groupings, and correlation. Each dimension is assigned to a facial characteristic, for example eyebrow slant, eye spacing, head eccentricity, pupil size, et cetera. 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 (rate) we could classify that rate into three classes e.g. rate < 3%, 3% < rate < 6% or rate > 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|right|150px|Fig. 2: 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 are not that effective as 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, 2009] Wikipedia contributors. Chernoff_face. Wikipedia, the free encyclopedia. Last modified on: October 8, 2009, at 17:34. Retrieved at: November 4, 2009. http://en.wikipedia.org/wiki/Chernoff_face
* [Turner, 1977] Eugene Turner. Life in LA. Last modified on: November 1, 1998. Retrieved at: November 5, 2009. http://www.csun.edu/~hfgeg005/eturner/gallery/lifeinla.GIF
* [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://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.948
* [Kosara, 2007] Robert Kosara. A Critique of Chernoff Faces. Created at: February 25, 2007. Retrieved at: November 4, 2009. http://eagereyes.org/VisCrit/ChernoffFaces.html

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

2009-11-05T21:42:14Z

0500280:

== Introduction ==
{{Quotation| Chernoff faces display multivariate data in the shape of a human face. |[Wikipedia, 2009]}}

Instead of using a vector representation for describing sets of data, a vector is represented by a face which can hold up to 18 dimensions. Changes of vectors result in different faces, which the human brain can treat better than vectors. Chernoff faces are often used in statistic presentations as Figure 1 shows.

[[Image:lifeinla.GIF|thumb|none|right|150px|Fig. 1: Life in Los Angeles [Turner, 1977]]]

{{Quotation| These faces are a type of [[Glyph|glyph]], a graphical object whose properties represent data values. |[Kosara, 2007]}}

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.

== The idea in detail ==
Chernoff faces are simplified faces that can help viewers to detect patterns, groupings, and correlation. Each dimension is assigned to a facial characteristic, for example eyebrow slant, eye spacing, head eccentricity, pupil size, et cetera. 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 (rate) we could classify that rate into three classes e.g. rate < 3%, 3% < rate < 6% or rate > 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|right|150px|Fig. 2: 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 are not that effective as 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, 2009] Wikipedia contributors. Chernoff_face. Wikipedia, the free encyclopedia. Last modified on: October 8, 2009, at 17:34. Retrieved at: November 4, 2009. http://en.wikipedia.org/wiki/Chernoff_face
* [Turner, 1977] Eugene Turner. Life in LA. Last modified on: November 1, 1998. Retrieved at: November 5, 2009. http://www.csun.edu/~hfgeg005/eturner/gallery/lifeinla.GIF
* [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://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.948
* [Kosara, 2007] Robert Kosara. A Critique of Chernoff Faces. Created at: February 25, 2007. Retrieved at: November 4, 2009. http://eagereyes.org/VisCrit/ChernoffFaces.html

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

2009-11-05T21:40:45Z

0500280:

== Introduction ==
{{Quotation| Chernoff faces display multivariate data in the shape of a human face. |[Wikipedia, 2009]}}

Instead of using a vector representation for describing sets of data, a vector is represented by a faced which can hold up to 18 dimensions. Changes of vectors result in different faces, which the human brain can treat better than vectors. Chernoff faces are often used in statistic presentations as Figure 1 shows.

[[Image:lifeinla.GIF|thumb|none|right|150px|Fig. 1: Life in Los Angeles [Turner, 1977]]]

{{Quotation| These faces are a type of [[Glyph|glyph]], a graphical object whose properties represent data values. |[Kosara, 2007]}}

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.

== The idea in detail ==
Chernoff faces are simplified faces that can help viewers to detect patterns, groupings, and correlation. Each dimension is assigned to a facial characteristic, for example eyebrow slant, eye spacing, head eccentricity, pupil size, et cetera. 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 (rate) we could classify that rate into three classes e.g. rate < 3%, 3% < rate < 6% or rate > 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|right|150px|Fig. 2: 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 are not that effective as 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, 2009] Wikipedia contributors. Chernoff_face. Wikipedia, the free encyclopedia. Last modified on: October 8, 2009, at 17:34. Retrieved at: November 4, 2009. http://en.wikipedia.org/wiki/Chernoff_face
* [Turner, 1977] Eugene Turner. Life in LA. Last modified on: November 1, 1998. Retrieved at: November 5, 2009. http://www.csun.edu/~hfgeg005/eturner/gallery/lifeinla.GIF
* [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://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.948
* [Kosara, 2007] Robert Kosara. A Critique of Chernoff Faces. Created at: February 25, 2007. Retrieved at: November 4, 2009. http://eagereyes.org/VisCrit/ChernoffFaces.html

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

2009-11-05T20:32:56Z

0500280:

User:UE-InfoVis0910 0500280

2009-11-05T20:26:13Z

0500280:

'''Name:''' Gastecker, Bettina

[[Image:Avatar.jpg|thumb|left]]

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

2009-11-04T14:50:57Z

0500280: New page: Das erste Zitat wurde geändert, da die Wikipedia Seite dazu geändert wurde. Hierzu wurde auch der Quellenverweis aktualisiert. Weiters wurde das zweite Zitat geändert, da es nicht mit ...

Das erste Zitat wurde geändert, da die Wikipedia Seite dazu geändert wurde. Hierzu wurde auch der Quellenverweis aktualisiert.

Weiters wurde das zweite Zitat geändert, da es nicht mit dem auf der Originalseite übereinstimmte.

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

2009-11-04T14:46:32Z

0500280:

== Introduction ==
{{Quotation| Chernoff faces display multivariate data in the shape of a human face. |[Wikipedia, 2009]}}
This means that instead of using a vector representation for describing sets of data, a vector is represented by a face that can hold up to 18 dimensions. Changes of vectors result in different faces, which the human brain can treat better than vectors. Chernoff faces therefore are often used in statistic presentations as Figure 1 shows.

[[Image:lifeinla.GIF|thumb|none|right|150px|Fig. 1: Life in Los Angeles [Turner, 1977]]]

{{Quotation| These faces are a type of [[Glyph|glyph]], a graphical object whose properties represent data values. |[Kosara, 2007]}}

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.

== 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 eyebrow 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 (rate) we could classify that rate into three classes e.g. rate < 3%, 3% < rate < 6% or rate > 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|right|150px|Fig. 2: 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, 2009] Wikipedia contributors. Chernoff_face. ''Wikipedia, the free encyclopedia''. Last modified on October 8, 2009, at 17:34. Retrieved at: November 4, 2009. 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
* [Kosara, 2007] Robert Kosara. A Critique of Chernoff Faces. Created at: February 25, 2007. Retrieved at: November 4, 2009. http://eagereyes.org/VisCrit/ChernoffFaces.html

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

2009-11-04T11:32:11Z

0500280:

== Introduction ==
{{Quotation| Chernoff faces display multivariate data in the shape of a human face. |[Wikipedia, 2009]}}
This means that instead of using a vector representation for describing sets of data, a vector is represented by a face that can hold up to 18 dimensions. Changes of vectors result in different faces, which the human brain can treat better than vectors. Chernoff faces therefore are often used in statistic presentations as Figure 1 shows.

[[Image:lifeinla.GIF|thumb|none|right|150px|Fig. 1: Life in Los Angeles [Turner, 1977]]]

{{Quotation| Faces are type of [[Glyph|glyph]], a graphical object whose properties represent data values. |[Kosara, 2007]}}

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.

== 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 eyebrow 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 (rate) we could classify that rate into three classes e.g. rate < 3%, 3% < rate < 6% or rate > 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|right|150px|Fig. 2: 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, 2009] Wikipedia contributors. Chernoff_face. ''Wikipedia, the free encyclopedia''. Last modified on October 8, 2009, at 17:34. Retrieved at: November 4, 2009. 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
* [Kosara, 2007] Robert Kosara. A Critique of Chernoff Faces. Created at: February 25, 2007. Retrieved at: October 28, 2008. http://eagereyes.org/VisCrit/ChernoffFaces.html

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

2009-11-03T23:03:58Z

0500280:

== Introduction ==
{{Quotation| a Chernoff face represents multivariate data in form of a face. |[Wikipedia, 2008]}}
This means that instead of using a vector representation for describing sets of data, a vector is represented by a face that can hold up to 18 dimensions. Changes of vectors result in different faces, which the human brain can treat better than vectors. Chernoff faces therefore are often used in statistic presentations as Figure 1 shows.

[[Image:lifeinla.GIF|thumb|none|right|150px|Fig. 1: Life in Los Angeles [Turner, 1977]]]

{{Quotation| Faces are type of [[Glyph|glyph]], a graphical object whose properties represent data values. |[Kosara, 2007]}}

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.

== 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 eyebrow 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 (rate) we could classify that rate into three classes e.g. rate < 3%, 3% < rate < 6% or rate > 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|right|150px|Fig. 2: 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
* [Kosara, 2007] Robert Kosara. A Critique of Chernoff Faces. Created at: February 25, 2007. Retrieved at: October 28, 2008. http://eagereyes.org/VisCrit/ChernoffFaces.html

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2009-10-28T09:43:52Z

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