InfoVis:Wiki - User contributions [en]

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 4

2010-01-06T14:37:17Z

UE-InfoVis0910 0625847:

== 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.

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

== 1. Description of application domain, the data, users, tasks and goals ==

=== 1.1. Description of the application domain and the dataset ===

==== 1.1.1. Application domain analysis ====

Task 4 is about to create a pedigree of the descendants of Lisa and Bart Simpson (which means that two separate trees should be created - one for Bart and one for Lisa). Furthermore comparisons of individual sections of the trees should be possible.
 
'''The visualization should represent the following information:'''
 
• family relationships (at least parent-children)

• distinction between blood relatives and family members by marriage

• birthday, day of death and lifetime of all family members

• important events in the life of every family member (e.g. advertisements, prison visits, study time, nobel prize, unemployment, etc.)

• satisfaction of each family member (scale: very low - low - medium - high - very high), may change during the course of life.
 
The visualization should enable the interactive analysis of the data.
 
'''Mandatory:'''

• Opportunities for a better comparison of individual sections of the trees or the comparison of excerpts from Lisa's and Bart's family trees.

• Furthermore at least 2 other possibilities for interaction (e.g. details on demand, filter options)
 
'''General:'''

• the data should be visualized for analysing background stories between family relationships, important events and satisfaction.

==== 1.1.2. Dataset analysis ====

First of all we consider the underlying date as a set of multidimensional multivariate data. Mulitdimensional because of the fact that there are a few attributes in view of every person. Multivariate because we have got multiple person variables. To make the parent-child relationships visible we are going to organize the data hierarchically (like a tree). The datatypes of the attributes are:
 
• Nominal – have no ordering

• Quantitative – exact values
 
'''For each person the following attributes are available:'''
 
• birthday - exact value

• age - exact value

• date of Death - exact value

• important events (e.g. advertisements, prison visits, study time, …) - some text

• satisfaction of each family member - temporal
 
=== 1.2. Audience analysis ===

The visualization is intended for members of the Simpson family who wants to find out more about their family history. Or in other words, for people who wants to operate in the field of genealogy (in view of the Simpson family).

Specificities of the target group are those that some family data and personal data is needed. Gernally all kind of data is useful in our case, which has to do with family. Well, if we are going to create a pedigree for doctors, much more information about the health history per person is needed as the task definition mentioned.

One of the most common visualization techniques used for creating a pedigree are hierarchical ones. So we prefer to use such a technique for our purpose.

=== 1.3. The purpose of the visualization ===

What should be achieved with the visualization or which tasks should be solved?
 
• detection of family relationships

• check wheter a family member belongs to the blood relatives or not

• to find out more about important events in the life of a certain family member

• to make comparisions between some family members in view of some data

• comparison of individual sections of the trees

== 2. Concept ==

=== 2.1. Types of visualization ===
 
'''Main visualization'''

To visualize the pedigree we are going to use a hierachical tree structure where the nodes contain a photo of the corresponding person. First of all only the first person (in our case even Bart or Lisa) is to be visualized. If the user wants to go deeper into the family hierarchy then he or she selects one of the childnodes so that an expansion takes places (so that the children of the selected node are going to be visible on the screen). Well the principle works after the following one:

http://prefuse.org/gallery/treeview/

To close such an expansion the user has only to click at the corresponding node. If the screenspace is too small for further expansions the visualization will go over into a Perspektive Wall which means that the focus lies only on a certain region. Because of the fact that two pedigrees should be developed (one for Bart and one for Lisa) wie have to split the screen into two separate regions where each tree will be displayed. Furthermore a third screen region will be provided. This region works as a working panel at which the user can drop some nodes of the trees into it to make some comparisions. The explanation for this follows in a later section.

[[Image:Unbenannt3.png]]

=== 2.2. Visual Mapping ===

Next, we explain how the different data dimensions are translated into visual attributes:
 
'''Family relationships'''
 
These relationships are visualized by the edges between the nodes
 
'''Blood relatives or family members by marriage'''
 
To make a distinction between blood relatives and family members by marriage we use a different border colour of the photo. For example: if someone is a blood relative then the border of his treenode (which shows a photo from e corresponding person) looks like this:

[[Image:Unbenannt4.png]]

In addition the colour of the outgoing edges could also be in the colour of the photo border.
 
'''Dead'''
 
Next if someone is dead, then were are going to visualise the corresponding tree node like the following one so that the user will see immeadiately if someone is still alive or not.
 
[[Image:Dead.JPG]]
 
'''Relation between a couple'''
 
To see, if a couple is married, divorced or if there is only a relationship between them, serveral symbols could be used to express this. These symbols will be displayed between the corresponding persons (man and woman in general). Well, the first symbol stands for marriage and the second one for divorce. If they are leading a relationship then no icon will be used (displayed between the two corresponding tree nodes).
 
[[Image:Married.JPG]]
 
[[Image:Geschieden.JPG]]
 
=== 2.3. Description of the used techniques/applied principles ===

=== 2.4. Opportunities for interaction ===
 
'''Selection/Highlighting'''
 
To visualize the children of a certain node, the user has to click at the node. Next the childnodes are going to appear on the screen. Furthermore closing the node relationships works similar. If the place on the screen is too small after some expansions, the visualization will go over in a perspektive wall:
 
• Everytime when the user moves forward, the focus lies on this region. If the screenspace becomes too small so all the regions which are not interesting for the user at this time move into the Out-of-Focus region.

• If the user switches back without closing the front nodes, these nodes are going to move into the Out-of-Focus region.
 
'''Explore'''
 
Another possibility to get an overview of the whole tree is zooming. If the user moves the mouse wheel backwards, it will zoom out. Zoom in works in the other direction. For example, at the last zooming out level, the user sees the whole pedigree (which will look like a connected scatterplott). This overview is also useful if the user wants to mark subsets of data the purpose of comparision.

[[Image:Unbenannt2.png]]

By zooming in, the focus will move to a certain region, the will become bigger and the photos of the nodes will be visible again.

[[Image:Unbenannt.png]]

'''Abstract/Elaborate'''
 
To display detailed information about a specific person on demand, the user has to double click at a node. Afterwards a pop-up window, which we call Infobox, appears which contains some informations about the person. These informations are arranged in a table.

==== 2.4.1. Comparison between parts of the pedigree ====

The user should be able to make some comparison in view of the underlying tree data. For this purpose it is necessary to offer the user some interaction mechanism which allows him or her to make such comparisons. Furthermore comparisons among various tree nodes should also be possible. To do so some nodes have to be selected and moved into the working panel. To make various comparisons a botton list should be available for the user (e.g.: bottons for each/some person properties). If the user klicks at such a botton, a corresponding visualisation will be created.
 
'''Temporal plot of the satisfaction (one person)'''
 
In this visualisation the satisfication of some family member (because it may change during the course of life) will be plotted. For this purpose a time-series plot will be used, where the scale of the x-axis contains the time and the y-axes the scale of satisfaction (very low - low - medium - high - very high). So the user has insight about which person has changed its corresponding satisfaction over time.
 
[[Image:infobox.jpg]]
 
Well, this plot is visible in the detailed infobox of a person which means that the user has to open the normal infobox. Furthermore if the user wants to look at the plot, he or she must click at a button, so that an expansion of the infobox take place. As a result the detailed infobox with the corresponding plot appears. Furthermore a comparision between various persons is also possible. For this purpose the user will select some tree node and drop them into the working panel. As a result the infobox for each selected node will be visible in this panel.
 
'''Temporal plot of the satisfaction (more than one person)'''
 
Its like the plot in the detailed infobox, except that points for more than one persons are going to be drawn. To make a distinction between which persons belong to Lisas tree and which family members belong to Barts tree, two different colors for the points will be used. Well in this case it would be clearer if the points are going to be connected by lines so that the user can better distinguish the predecessor and successor of a point.
 
'''Comparison of major events'''
 
A comparison of the major events in view of the selected persons should be made. For this purpose a simple barplot will be drawn. Each bar stand for an appearing event (X-axes) in view of the tree node selection. The incidences of the events will be mapped on the Y-axes. If the selection contains different nodes (from Bart and from Lisas tree) then for each event two bars (with different colors) will be plotted (the one bar says, that this event has occurred in Lisas family and the other one says, the event occurred in Barts family). So the user has an overview of the major events and sees if one even occurred also in the other family. The following figure gives an example for such a barplot (which will occur in the working panel of the main window)
 
[[Image:Gegenüberstellung.JPG]]
 

=== 2.5. Mockup(s)/Fake Screenshots ===

Here is a screenshot of the pedigree visualization program. At the top there are the two pedigrees of bart and lisa simpson. On the left the zoom factor is less big than on the right side. On the bottom you can see the comparison pane and the persons who are compared in detail. To get the person out of the pane it is just necessary to drag it and then drop it outside the pane. All tables and information panes are scrollable.
[[Image:Mockup.jpg]]
[[Image:Far.jpg]]

=== 2.6. User support in their tasks ===

The User can decide how detailed the view of the pedigree is and what type of information he wants to view. It is possible to view two pedigrees, e.g. of lisa and bart, at the same time. The handling is intuitive and only need clicking and scrolling. The number of generations can easily be seen by a lower number of zoom factor. The comparison pane supports the user in detailed comparison of many persons. This is possible by simply drag and drop of the person into the pane.

=== 2.7. Specificities ===

In this pedigree visualization it is possible to view the portrait of the persons. It is also possible to visualize the feeling of a single person in a chart.

=== 2.8. Pros and cons of the used technology ===

The used technologies enables the user to simply navigate throw the pedigree. The navigation techniques are mostly intuitive and easy to learn.

=== 2.9. Possible expansions and improvements ===

Well for our first version, we have only listend 3 possibilities in view of a comparision between the two pedigrees (see 2.4.1. Comparison between parts of the pedigree). A possible improvement would be to think about more such mechanism for comparisons (e.g.: lifetime-plot which shows in a plot birthday, lifetime and eventually the year of death for the selected persons. So the user would gain a temporal overview in view of the family members).

== 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 09|Gruppe 09]]

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 4

2010-01-06T14:14:07Z

UE-InfoVis0910 0625847:

== 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.

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

== 1. Description of application domain, the data, users, tasks and goals ==

=== 1.1. Description of the application domain and the dataset ===

==== 1.1.1. Application domain analysis ====

Task 4 is about to create a pedigree of the descendants of Lisa and Bart Simpson (which means that two separate trees should be created - one for Bart and one for Lisa). Furthermore comparisons of individual sections of the trees should be possible.
 
'''The visualization should represent the following information:'''
 
• family relationships (at least parent-children)

• distinction between blood relatives and family members by marriage

• birthday, day of death and lifetime of all family members

• important events in the life of every family member (e.g. advertisements, prison visits, study time, nobel prize, unemployment, etc.)

• satisfaction of each family member (scale: very low - low - medium - high - very high), may change during the course of life.
 
The visualization should enable the interactive analysis of the data.
 
'''Mandatory:'''

• Opportunities for a better comparison of individual sections of the trees or the comparison of excerpts from Lisa's and Bart's family trees.

• Furthermore at least 2 other possibilities for interaction (e.g. details on demand, filter options)
 
'''General:'''

• the data should be visualized for analysing background stories between family relationships, important events and satisfaction.

==== 1.1.2. Dataset analysis ====

First of all we consider the underlying date as a set of multidimensional multivariate data. Mulitdimensional because of the fact that there are a few attributes in view of every person. Multivariate because we have got multiple person variables. To make the parent-child relationships visible we are going to organize the data hierarchically (like a tree). The datatypes of the attributes are:
 
• Nominal – have no ordering

• Quantitative – exact values
 
'''For each person the following attributes are available:'''
 
• birthday - exact value

• age - exact value

• date of Death - exact value

• important events (e.g. advertisements, prison visits, study time, …) - some text

• satisfaction of each family member - temporal
 
=== 1.2. Audience analysis ===

The visualization is intended for members of the Simpson family who wants to find out more about their family history. Or in other words, for people who wants to operate in the field of genealogy (in view of the Simpson family).

Specificities of the target group are those that some family data and personal data is needed. Gernally all kind of data is useful in our case, which has to do with family. Well, if we are going to create a pedigree for doctors, much more information about the health history per person is needed as the task definition mentioned.

One of the most common visualization techniques used for creating a pedigree are hierarchical ones. So we prefer to use such a technique for our purpose.

=== 1.3. The purpose of the visualization ===

What should be achieved with the visualization or which tasks should be solved?
 
• detection of family relationships

• check wheter a family member belongs to the blood relatives or not

• to find out more about important events in the life of a certain family member

• to make comparisions between some family members in view of some data

• comparison of individual sections of the trees

== 2. Concept ==

=== 2.1. Types of visualization ===
 
'''Main visualization'''

To visualize the pedigree we are going to use a hierachical tree structure where the nodes contain a photo of the corresponding person. First of all only the first person (in our case even Bart or Lisa) is to be visualized. If the user wants to go deeper into the family hierarchy then he or she selects one of the childnodes so that an expansion takes places (so that the children of the selected node are going to be visible on the screen). Well the principle works after the following one:

http://prefuse.org/gallery/treeview/

To close such an expansion the user has only to click at the corresponding node. If the screenspace is too small for further expansions the visualization will go over into a Perspektive Wall which means that the focus lies only on a certain region. Because of the fact that two pedigrees should be developed (one for Bart and one for Lisa) wie have to split the screen into two separate regions where each tree will be displayed. Furthermore a third screen region will be provided. This region works as a working panel at which the user can drop some nodes of the trees into it to make some comparisions. The explanation for this follows in a later section.

[[Image:Unbenannt3.png]]

=== 2.2. Visual Mapping ===

Next, we explain how the different data dimensions are translated into visual attributes:
 
'''Family relationships'''
 
These relationships are visualized by the edges between the nodes
 
'''Blood relatives or family members by marriage'''
 
To make a distinction between blood relatives and family members by marriage we use a different border colour of the photo. For example: if someone is a blood relative then the border of his treenode (which shows a photo from e corresponding person) looks like this:

[[Image:Unbenannt4.png]]

In addition the colour of the outgoing edges could also be in the colour of the photo border.
 
'''Dead'''
 
Next if someone is dead, then were are going to visualise the corresponding tree node like the following one so that the user will see immeadiately if someone is still alive or not.
 
[[Image:Dead.JPG]]
 
'''Relation between a couple'''
 
To see, if a couple is married, divorced or if there is only a relationship between them, serveral symbols could be used to express this. These symbols will be displayed between the corresponding persons (man and woman in general). Well, the first symbol stands for marriage and the second one for divorce. If they are leading a relationship then no icon will be used (displayed between the two corresponding tree nodes).
 
[[Image:Married.JPG]]
 
[[Image:Geschieden.JPG]]
 
=== 2.3. Description of the used techniques/applied principles ===

=== 2.4. Opportunities for interaction ===
 
'''Selection/Highlighting'''
 
To visualize the children of a certain node, the user has to click at the node. Next the childnodes are going to appear on the screen. Furthermore closing the node relationships works similar. If the place on the screen is too small after some expansions, the visualization will go over in a perspektive wall:
 
• Everytime when the user moves forward, the focus lies on this region. If the screenspace becomes too small so all the regions which are not interesting for the user at this time move into the Out-of-Focus region.

• If the user switches back without closing the front nodes, these nodes are going to move into the Out-of-Focus region.
 
'''Explore'''
 
Another possibility to get an overview of the whole tree is zooming. If the user moves the mouse wheel backwards, it will zoom out. Zoom in works in the other direction. For example, at the last zooming out level, the user sees the whole pedigree (which will look like a connected scatterplott). This overview is also useful if the user wants to mark subsets of data the purpose of comparision.

[[Image:Unbenannt2.png]]

By zooming in, the focus will move to a certain region, the will become bigger and the photos of the nodes will be visible again.

[[Image:Unbenannt.png]]

'''Abstract/Elaborate'''
 
To display detailed information about a specific person on demand, the user has to double click at a node. Afterwards a pop-up window, which we call Infobox, appears which contains some informations about the person. These informations are arranged in a table.

==== 2.4.1. Comparison between parts of the pedigree ====

The user should be able to make some comparison in view of the underlying tree data. For this purpose it is necessary to offer the user some interaction mechanism which allows him or her to make such comparisons. Furthermore comparisons among various tree nodes should also be possible. To do so some nodes have to be selected and moved into the working panel. To make some comparisons a botton list should be available for the user (e.g.: a botton for each person property). If the user klicks at such a botton, a corresponding visualisation will be created.
 
'''Temporal plot of the satisfaction (one person)'''
 
In this visualisation the satisfication of some family member (because it may change during the course of life) will be plotted. For this purpose a time-series plot will be used, where the scale of the x-axis contains the time and the y-axes the scale of satisfaction (very low - low - medium - high - very high). So the user has insight about which person has changed its corresponding satisfaction over time.
 
[[Image:infobox.jpg]]
 
Well, this plot is visible in the detailed infobox of a person which means that the user has to open the normal infobox. Furthermore if the user wants to look at the plot, he or she must click at a button, so that an expansion of the infobox take place. As a result the detailed infobox with the corresponding plot appears. Furthermore a comparision between various persons is also possible. For this purpose the user will select some tree node and drop them into the working panel. As a result the infobox for each selected node will be visible in this panel.
 
'''Temporal plot of the satisfaction (more than one person)'''
 
Its like the plot in the detailed infobox, except that points for more than one persons are going to be drawn. To make a distinction between persons, who belongs to Lisas tree, and family members, who belongs to Barts tree, two different colors for the lines will be used.
 
'''Comparison of major events'''
 
A comparison of the major events in view of the selected persons should be made. For this purpose a simple barplot will be drawn. Each bar stand for an appearing event (X-axes) in view of the tree node selection. The incidences of the events will be mapped on the Y-axes. If the selection contains different nodes (from Bart and from Lisas tree) then for each event two bars (with different colors) will be plotted (the one bar says, that this event has occurred in Lisas family and the other one says, the event occurred in Barts family). So the user has an overview of the major events and sees if one even occurred also in the other family. The following figure gives an example for such a barplot (which will occur in the working panel of the main window)
 
[[Image:Gegenüberstellung.JPG]]
 

=== 2.5. Mockup(s)/Fake Screenshots ===

Here is a screenshot of the pedigree visualization program. At the top there are the two pedigrees of bart and lisa simpson. On the left the zoom factor is less big than on the right side. On the bottom you can see the comparison pane and the persons who are compared in detail. To get the person out of the pane it is just necessary to drag it and then drop it outside the pane. All tables and information panes are scrollable.
[[Image:Mockup.jpg]]
[[Image:Far.jpg]]

=== 2.6. User support in their tasks ===

The User can decide how detailed the view of the pedigree is and what type of information he wants to view. It is possible to view two pedigrees, e.g. of lisa and bart, at the same time. The handling is intuitive and only need clicking and scrolling. The number of generations can easily be seen by a lower number of zoom factor. The comparison pane supports the user in detailed comparison of many persons. This is possible by simply drag and drop of the person into the pane.

=== 2.7. Specificities ===

In this pedigree visualization it is possible to view the portrait of the persons. It is also possible to visualize the feeling of a single person in a chart.

=== 2.8. Pros and cons of the used technology ===

The used technologies enables the user to simply navigate throw the pedigree. The navigation techniques are mostly intuitive and easy to learn.

=== 2.9. Possible expansions and improvements ===

== 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 09|Gruppe 09]]

File:Gegenüberstellung.JPG

2010-01-06T14:08:19Z

UE-InfoVis0910 0625847: New page: == Beschreibung == == Copyright status == == Source ==

== Beschreibung ==

== Copyright status ==

== Source ==

File:Married.JPG

2010-01-06T13:46:25Z

UE-InfoVis0910 0625847: New page: == Beschreibung == == Copyright status == == Source ==

== Beschreibung ==

== Copyright status ==

== Source ==

File:Geschieden.JPG

2010-01-06T13:46:01Z

UE-InfoVis0910 0625847: New page: == Beschreibung == == Copyright status == == Source ==

== Beschreibung ==

== Copyright status ==

== Source ==

File:Dead.JPG

2010-01-06T13:45:38Z

UE-InfoVis0910 0625847: New page: == Beschreibung == == Copyright status == == Source ==

== Beschreibung ==

== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 4

2010-01-06T13:44:38Z

UE-InfoVis0910 0625847:

== 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.

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

== 1. Description of application domain, the data, users, tasks and goals ==

=== 1.1. Description of the application domain and the dataset ===

==== 1.1.1. Application domain analysis ====

Task 4 is about to create a pedigree of the descendants of Lisa and Bart Simpson (which means that two separate trees should be created - one for Bart and one for Lisa). Furthermore comparisons of individual sections of the trees should be possible.
 
'''The visualization should represent the following information:'''
 
• family relationships (at least parent-children)

• distinction between blood relatives and family members by marriage

• birthday, day of death and lifetime of all family members

• important events in the life of every family member (e.g. advertisements, prison visits, study time, nobel prize, unemployment, etc.)

• satisfaction of each family member (scale: very low - low - medium - high - very high), may change during the course of life.
 
The visualization should enable the interactive analysis of the data.
 
'''Mandatory:'''

• Opportunities for a better comparison of individual sections of the trees or the comparison of excerpts from Lisa's and Bart's family trees.

• Furthermore at least 2 other possibilities for interaction (e.g. details on demand, filter options)
 
'''General:'''

• the data should be visualized for analysing background stories between family relationships, important events and satisfaction.

==== 1.1.2. Dataset analysis ====

First of all we consider the underlying date as a set of multidimensional multivariate data. Mulitdimensional because of the fact that there are a few attributes in view of every person. Multivariate because we have got multiple person variables. To make the parent-child relationships visible we are going to organize the data hierarchically (like a tree). The datatypes of the attributes are:
 
• Nominal – have no ordering

• Quantitative – exact values
 
'''For each person the following attributes are available:'''
 
• birthday - exact value

• age - exact value

• date of Death - exact value

• important events (e.g. advertisements, prison visits, study time, …) - some text

• satisfaction of each family member - temporal
 
=== 1.2. Audience analysis ===

The visualization is intended for members of the Simpson family who wants to find out more about their family history. Or in other words, for people who wants to operate in the field of genealogy (in view of the Simpson family).

Specificities of the target group are those that some family data and personal data is needed. Gernally all kind of data is useful in our case, which has to do with family. Well, if we are going to create a pedigree for doctors, much more information about the health history per person is needed as the task definition mentioned.

One of the most common visualization techniques used for creating a pedigree are hierarchical ones. So we prefer to use such a technique for our purpose.

=== 1.3. The purpose of the visualization ===

What should be achieved with the visualization or which tasks should be solved?
 
• detection of family relationships

• check wheter a family member belongs to the blood relatives or not

• to find out more about important events in the life of a certain family member

• to make comparisions between some family members in view of some data

• comparison of individual sections of the trees

== 2. Concept ==

=== 2.1. Types of visualization ===
 
'''Main visualization'''

To visualize the pedigree we are going to use a hierachical tree structure where the nodes contain a photo of the corresponding person. First of all only the first person (in our case even Bart or Lisa) is to be visualized. If the user wants to go deeper into the family hierarchy then he or she selects one of the childnodes so that an expansion takes places (so that the children of the selected node are going to be visible on the screen). Well the principle works after the following one:

http://prefuse.org/gallery/treeview/

To close such an expansion the user has only to click at the corresponding node. If the screenspace is too small for further expansions the visualization will go over into a Perspektive Wall which means that the focus lies only on a certain region. Because of the fact that two pedigrees should be developed (one for Bart and one for Lisa) wie have to split the screen into two separate regions where each tree will be displayed. Furthermore a third screen region will be provided. This region works as a working panel at which the user can drop some nodes of the trees into it to make some comparisions. The explanation for this follows in a later section.

[[Image:Unbenannt3.png]]

=== 2.2. Visual Mapping ===

Next, we explain how the different data dimensions are translated into visual attributes:
 
'''Family relationships'''
 
These relationships are visualized by the edges between the nodes
 
'''Blood relatives or family members by marriage'''
 
To make a distinction between blood relatives and family members by marriage we use a different border colour of the photo. For example: if someone is a blood relative then the border of his treenode (which shows a photo from e corresponding person) looks like this:

[[Image:Unbenannt4.png]]

In addition the colour of the outgoing edges could also be in the colour of the photo border.
 
'''Dead'''
 
Next if someone is dead, then were are going to visualise the corresponding tree node like the following one so that the user will see immeadiately if someone is still alive or not.
 
'''Relation between a couple'''
 
To see, if a couple is married, divorced or if there is only a relationship between them, serveral symbols could be used to express this. These symbols will be displayed between the corresponding persons (man and woman in general). Well, the first symbol stands for marriage and the second one for divorce. If they are leading a relationship then no icon will be used (displayed between the two corresponding tree nodes).

=== 2.3. Description of the used techniques/applied principles ===

=== 2.4. Opportunities for interaction ===
 
'''Selection/Highlighting'''
 
To visualize the children of a certain node, the user has to click at the node. Next the childnodes are going to appear on the screen. Furthermore closing the node relationships works similar. If the place on the screen is too small after some expansions, the visualization will go over in a perspektive wall:
 
• Everytime when the user moves forward, the focus lies on this region. If the screenspace becomes too small so all the regions which are not interesting for the user at this time move into the Out-of-Focus region.

• If the user switches back without closing the front nodes, these nodes are going to move into the Out-of-Focus region.
 
'''Explore'''
 
Another possibility to get an overview of the whole tree is zooming. If the user moves the mouse wheel backwards, it will zoom out. Zoom in works in the other direction. For example, at the last zooming out level, the user sees the whole pedigree (which will look like a connected scatterplott). This overview is also useful if the user wants to mark subsets of data the purpose of comparision.

[[Image:Unbenannt2.png]]

By zooming in, the focus will move to a certain region, the will become bigger and the photos of the nodes will be visible again.

[[Image:Unbenannt.png]]

'''Abstract/Elaborate'''
 
To display detailed information about a specific person on demand, the user has to double click at a node. Afterwards a pop-up window, which we call Infobox, appears which contains some informations about the person. These informations are arranged in a table.

==== 2.4.1. Comparison between parts of the pedigree ====

The user should be able to make some comparison in view of the underlying tree data. For this purpose it is necessary to offer the user some interaction mechanism which allows him or her to make such comparisions. Furthermore comparisons among various tree nodes should also be possible. To do so some nodes have to be selected and moved into the working panel. To make some comparisons a botton list should be available for the user (e.g.: a botton for each person property). If the user klicks at such a botton, a corresponding visualisation will be created.
 
'''Temporal plot of the satisfaction (one person)'''
 
In this visualisation the satisfication of some family member (because it may change during the course of life) will be plotted. For this purpose a time-series plot will be used, where the scale of the x-axis contains the time and the y-axes the scale of satisfaction (very low - low - medium - high - very high). So the user has insight about which person has changed its corresponding satisfaction over time.
 
Well, this plot is visible in the detailed infobox of a person which means that the user has to open the normal infobox. Furthermore if the user wants to look at the plot, he or she must click at a button, so that an expansion of the infobox take place. As a result the detailed infobox with the corresponding plot appears. Furthermore a comparision between various persons is also possible. For this purpose the user will select some tree node and drop them into the working panel. As a result the infobox for each selected node will be visible in this panel.
 
'''Temporal plot of the satisfaction (more than one person)'''
 
Its like the plot in the detailed infobox, except that points for more than one persons are going to be drawn. To make a distinction between persons, who belongs to Lisas tree, and family members, who belongs to Barts tree, two different colors for the lines will be used.
 
'''Comparison of major events'''
A comparison of the major events in view of the selected persons should be made. For this purpose a simple barplot will be drawn. Each bar stand for an appearing event (X-axes). The incidences of the events will be mapped on the Y-axes. If there are different nodes (from Bart and from Lisas tree) then for each event two bars (with different colors) will be plotted (the one bar says, that this event has occurred in Lisas family and the other one says, the event occurred in Barts family). So the user has a overview of the major events and sees if one even occurred also in the other family.
 

=== 2.5. Mockup(s)/Fake Screenshots ===

Here is a screenshot of the pedigree visualization program. At the top there are the two pedigrees of bart and lisa simpson. On the left the zoom factor is less big than on the right side. On the bottom you can see the comparison pane and the persons who are compared in detail. To get the person out of the pane it is just necessary to drag it and then drop it outside the pane. All tables and information panes are scrollable.
[[Image:Mockup.jpg]]
[[Image:Far.jpg]]

Detailed infobox
[[Image:infobox.jpg]]

=== 2.6. User support in their tasks ===

The User can decide how detailed the view of the pedigree is and what type of information he wants to view. It is possible to view two pedigrees, e.g. of lisa and bart, at the same time. The handling is intuitive and only need clicking and scrolling. The number of generations can easily be seen by a lower number of zoom factor. The comparison pane supports the user in detailed comparison of many persons. This is possible by simply drag and drop of the person into the pane.

=== 2.7. Specificities ===

In this pedigree visualization it is possible to view the portrait of the persons. It is also possible to visualize the feeling of a single person in a chart.

=== 2.8. Pros and cons of the used technology ===

The used technologies enables the user to simply navigate throw the pedigree. The navigation techniques are mostly intuitive and easy to learn.

=== 2.9. Possible expansions and improvements ===

== 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 09|Gruppe 09]]

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 3

2009-12-09T12:46:20Z

UE-InfoVis0910 0625847:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe3.html Beschreibung der Aufgabe 3]
=== Zu verbessernde Grafik ===
------------------------------- 
[[Image:geo5_2.jpg]]

=== Review of the Existing Graphic ===
 
* Non-data-ink should be reduced to no more than what is necessary to make the data ink understandable [Few, 2004]. Examples of non-data-ink, which should be removed from the graphic:
** The yellow/brown border around the real graphic
** border lines
** The light gray fonts in the background of the graphic
* Next unnecessary data ink should be subtracted. Because the main topic of this graphic is to recognize the connection between the Co2-Emission (total and per capita) of some countries and their gross domestic product. And so I think it is unnecessary to make a distinction in view of region because it’s clear that Brazil belongs to South America and Italy to Europe. Furthermore the distinction of regions carries no additional information in view of the overall topic. The aim is to give the readers of the graphic what they need, and all that they need, but nothing more [Few, 2004]. In addition information which is peripheral to the interests and purposes of the readers should be removed [Few, 2004].
* The next point of criticism relates to the fact that every designer must be carefully avoid saying too little by trying to say too much [Few, 2004]. As a result of this statement only countries which have a certain amount of Co2-Emission should be displayed in the graphic.
 
=== Improvements ===
 
* Now the graphic visualizes the most important countries in view of the CO2-Emission
* Furthermore the user is now able to differ between the various countries in a better way because there aren't so much
entries which overlap other ones.
* The information in view of the region is separated in another barplot. So the user has an overview of the regional CO2-Emissions

[[Image:Sheet_1.jpg]]

[[Image:CO2Region.JPG]]

== Referenzen ==
[Few, 2004] Stephen Few. Show Me the Numbers: Designing Tables and Graphs to Enlighten. Analytics Press, 2004, Chapter 7 – General Design For Communication

CO2 Emissions data set:
http://manyeyes.alphaworks.ibm.com/manyeyes/datasets/carbon-dioxide-emissions-co2-thousan-2/versions/1

http://manyeyes.alphaworks.ibm.com/manyeyes/datasets/carbon-dioxide-emissions-co2-kg-co/versions/1

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 3

2009-12-09T11:45:12Z

UE-InfoVis0910 0625847:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe3.html Beschreibung der Aufgabe 3]
=== Zu verbessernde Grafik ===
------------------------------- 
[[Image:geo5_2.jpg]]

=== Review of the Existing Graphic ===
 
* Non-data-ink should be reduced to no more than what is necessary to make the data ink understandable [Few, 2004]. Examples of non-data-ink, which should be removed from the graphic:
** The yellow/brown border around the real graphic
** border lines
** The light gray fonts in the background of the graphic
* Next unnecessary data ink should be subtracted. Because the main topic of this graphic is to recognize the connection between the Co2-Emission (total and per capita) of some countries and their gross domestic product. And so I think it is unnecessary to make a distinction in view of region because it’s clear that Brazil belongs to South America and Italy to Europe. Furthermore the distinction of regions carries no additional information in view of the overall topic. The aim is to give the readers of the graphic what they need, and all that they need, but nothing more [Few, 2004]. In addition information which is peripheral to the interests and purposes of the readers should be removed [Few, 2004].
* The next point of criticism relates to the fact that every designer must be carefully avoid saying too little by trying to say too much [Few, 2004]. As a result of this statement all countries which population size is less than 2.5 Million should be subtracted.
* The next point refers to emphasizing the most important data ink. The most important data in our case is the total Co2-Emission. So I would prefer to use a preattentive visual attribute because they are especially useful for emphasizing data ink [Few, 2004]. One way is the useage of size: the bigger the name of the country is, the more Co2 is to be emitted.
* For the encoding of the population size, it would be a good idea to use various color intensities for the country names (e.g.: China is written in black because it has a big population size, Peru is written in some light gray because it has a very small population)

[[Image:Sheet_1.jpg]]

[[Image:Sheet_2.jpg]]

[[Image:CO2Region.JPG]]

== Referenzen ==
[Few, 2004] Stephen Few. Show Me the Numbers: Designing Tables and Graphs to Enlighten. Analytics Press, 2004, Chapter 7 – General Design For Communication

CO2 Emissions data set:
http://manyeyes.alphaworks.ibm.com/manyeyes/datasets/carbon-dioxide-emissions-co2-thousan-2/versions/1

http://manyeyes.alphaworks.ibm.com/manyeyes/datasets/carbon-dioxide-emissions-co2-kg-co/versions/1

File:CO2Region.JPG

2009-12-09T11:38:32Z

UE-InfoVis0910 0625847: New page: == Beschreibung == == Copyright status == == Source ==

== Beschreibung ==

== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 3

2009-12-04T13:59:59Z

UE-InfoVis0910 0625847:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe3.html Beschreibung der Aufgabe 3]
=== Zu verbessernde Grafik ===
------------------------------- 
[[Image:geo5_2.jpg]]

=== Review of the Existing Graphic ===
 
* Non-data-ink should be reduced to no more than what is necessary to make the data ink understandable [Few, 2004]. Examples of non-data-ink, which should be removed from the graphic:
** The yellow/brown border around the real graphic
** border lines
** The light gray fonts in the background of the graphic
* Next unnecessary data ink should be subtracted. Because the main topic of this graphic is to recognize the connection between the Co2-Emission (total and per capita) of some countries and their gross domestic product. And so I think it is unnecessary to make a distinction in view of region because it’s clear that Brazil belongs to South America and Italy to Europe. Furthermore the distinction of regions carries no additional information in view of the overall topic. The aim is to give the readers of the graphic what they need, and all that they need, but nothing more [Few, 2004]. In addition information which is peripheral to the interests and purposes of the readers should be removed [Few, 2004].
* The next point of criticism relates to the fact that every designer must be carefully avoid saying too little by trying to say too much [Few, 2004]. As a result of this statement all countries which population size is less than 2.5 Million should be subtracted.
* The next point refers to emphasizing the most important data ink. The most important data in our case is the total Co2-Emission. So I would prefer to use a preattentive visual attribute because they are especially useful for emphasizing data ink [Few, 2004]. One way is the useage of size: the bigger the name of the country is, the more Co2 is to be emitted.
* For the encoding of the population size, it would be a good idea to use various color intensities for the country names (e.g.: China is written in black because it has a big population size, Peru is written in some light gray because it has a very small population)

== Referenzen ==
[Few, 2004] Stephen Few. Show Me the Numbers: Designing Tables and Graphs to Enlighten. Analytics Press, 2004, Chapter 7 – General Design For Communication

CO2 Emissions data set:
http://manyeyes.alphaworks.ibm.com/manyeyes/datasets/carbon-dioxide-emissions-co2-thousan-2/versions/1

http://manyeyes.alphaworks.ibm.com/manyeyes/datasets/carbon-dioxide-emissions-co2-kg-co/versions/1

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 07 - Aufgabe 1 - Pie Chart

2009-11-23T21:04:59Z

UE-InfoVis0910 0625847:

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 1 | Zurueck zu Aufgabe 1]]

== Pie Charts==

=== Definition ===
{{Quotation | A '''pie chart''' (or a '''circle graph''') is a circular chart divided into sectors, illustrating relative magnitudes or frequencies. In a pie chart, the arc length of each sector (and consequently its central angle and area), is proportional to the quantity it represents. Together, the sectors create a full disk. It is named for its resemblance to a pie which has been sliced.| [Wikipedia, 2008]}}

----

===How to use it===
Pie charts are used to show percentages. The circle provides a visual concept of the whole (100%). Despite its popularity, pie charts should be used sparingly for two reasons [Statistics Canada, 2008]:
 
* Use less than six components - otherwise, the resulting picture will be too complex to understand.
* Not useful when the values of each component are similar because it is difficult to see the differences between slice sizes.
 
To present certain values in a pie chart it is necessary to determine how many degrees represent this share of data in comparison to the whole circle. This calculation is done by developing the equation:

percent ÷ 100 x 360 degrees = the number of degrees

This ratio works because the total percent of the pie chart represents 100% and there are 360 degrees in a circle [Statistics Canada, 2008].
 
To construct a pie chart the following steps are necessary [Concordia University, 1999]:
* '''Determine the proportions:''' find the total value for the entire category being studied and calculate the percentage for each segment or part.
* '''Calculate degrees:''' convert the percentage values for each segment into degrees relative to the 360 degrees in the circle.
* '''Construct the chart:''' draw a circle and divide it into appropriately sized segments.
* '''Add labels and a title:''' label each segment or add a legend to identify the segments. Then clearly title the chart.

----

=== Variants of the Pie Chart ===

There are different variants of pie charts [Wikipedia, 2008]:
* '''Exploded Pie Chart:''' One ore more sectors are separated from the rest, to highlight it.
* '''Perspective (3D) pie chart:''' Mainly for aesthetic reasons the pie chart is displayed in 3D. But the third dimension does not improve the reading of the data. On the contrary, these plots are difficult to interpret because of the distorted effect of perspective associated with the third dimension. In general use of superfluous dimensions not used to display the data of interest is discouraged for charts in general, not only for pie charts.
* '''Polar area diagram:''' The polar area diagram is similar to a usual pie chart, except that the sectors are each of an equal angle and differ rather in how far each sector extends from the centre of the circle, enabling multiple comparisons on one diagram.
* '''Multi-Level Pie Chart:''' Such charts are used for representing hierarchical data. The hierarchical structure of data is depicted by means of concentric circles.

----

=== Criticism on the Pie Chart ===

[[image:PiechartsAndBars.png| thumb | Comparison of Pie Chart vs. Bar Chart]]
While pie charts are common in business and journalism, they are uncommon in scientific literature. {{Quotation |Pie charts can be an effective way of displaying information in some cases, in particular if the intent is to compare the size of a slice with the whole pie, rather than comparing the slices among them.| [Wikipedia, 2008]}} It has been also shown that comparison by angle was less accurate than comparison by length. Also a comparison by angle (as in pie charts) was shown to be perceived less accurate than comparison by length. The Usage of bars instead of slices improves the capability to compare the different segments of data [Wikipedia, 2008].
----

=== Other Forms of Charts ===
Among pie charts, used to display percentages, other forms of charts can be used for different purposes [French, 2008]:

* '''Column Charts''' – are used to show comparisons between items of data. A column in the chart represents the value of one item of data.
* '''Bar Charts''' - are very similar to column charts, except they run horizontally on the page instead of vertically like column charts.
* '''Line Charts''' – are used to show trends over time. Each line in the graph shows the changes in the value of one item of data.

----

=== References ===
*[Concordia University, 1999] Unknown, Pie Chart. Last Modified at: 1999. Retrieved at: November 4, 2008 http://web2.concordia.ca/Quality/tools/21piechart.pdf
*[French, 2008] Ted French. Chart. http://spreadsheets.about.com/od/c/g/chart_def.htm
*[Wikipedia, 2008] Wikipedia. Pie chart. Created at: June 15, 2004. Retrieved at: November 2008. http://en.wikipedia.org/wiki/Pie_chart
*[Statistics Canada, 2008] Statistics Canada. Pie charts. Retrieved at: November 19, 2008. http://www.statcan.ca/english/edu/power/ch9/piecharts/pie.htm

Teaching:TUW - UE InfoVis WS 2006/07 - Gruppe 03 - Aufgabe 1 - Data Density

2009-11-21T10:50:23Z

UE-InfoVis0910 0625847:

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 1 | Zurueck zu Aufgabe 1]]

== Data Density ==

=== Definition ===
{{Quotation|Number of Data Points means the number of discrete data values represented on screen at an instant. |[Barth, 1997]}}

=== Overview ===
The cognitive complexity of an image can be measured by data density (which can be measured by the number of points in a graph) [Dal Sasso Freitas et al., 2002].
 
So data density provides information about how many information elements can be displayed on a defined panel. Therefore you need numerical values which relate the number of maximal at the same time presentable information elements to a display panel [Edlinger, 2006].

=== Charts and Graphics ===
In general, Tufte assumes that the greater amount of data represented per square centimeter of print, the more effective the resulting representation [Barth, 1997].
Hence he discussed in his work "The Visual Display of Quantitative Information", which is a milestone in the theory of graph design, principles of graph design. One of them is: Try to maximize the data density and the size of the data matrix, within reason. Furthermore as the volume of data increases, data measures must shrink and the graphics can be shrunk way down [Tufte, 1999].
 
So graphics are at their best when they represents very dense and rich datasets because the human eye/brain can select, filter, edit, group, structure, highlight, focus, blend, outline, cluster, itemize, winnow, sort, abstract, smooth, isolate, idealize, summarize, etc. Give people the data so they can exercise their full powers -- don't limit them.
Note that low data densities on computer displays force us to view information sequentially, rather than spatially, which is bad for comprehension [Smith, 2005].
 

Example for data densities include [Smith, 2005]:
* 110,000 numbers/sq-inch for an astronomical graph. This is the maximum known density for a graph to compare. For most scientific journals we get about 50-200 numbers/sq-inch.
* 150 Mbits = human eye
:8 Mbits = typical computer screen
:25 Mbits = color slide
:150 Mbits = large foldout map
:28,000 Characters = Reference book
:18,000 Characters = phone book
:15,000 Characters = non-fiction

=== Example ===
To better understand this term, we show two graphs and compute their data density. Consider the result of a survey in which the gender, height and weight were recorded for 92 students. The charts are 5.6cm by 7.4cm, an area of 41.4cm2 [Hunt and Tyrrell, 1995].

[[Image:Barplot1.GIF]]
[[Image:NewScatterplot.GIF]]

Above you see a bar graph showing the breakdown into males and females.
According to the definition you have 2 (female and male) areas with 2 kind of information (gender and number of students), what makes 4 data points. By dividing this over the size of its area, you get the data density of this graph [Barth, 1997].

data density = 4 / 41.4 = 0.1 (to 1 dp)

The computation of the second example works similar to the first one. The second graph shows a labeled XY-chart, which additionally shows the relationship between height and weight of the students. As a result you have 3 kind of information on 92 areas, what results 276 data points [Barth, 1997].

data density = 276 / 41.4 = 6.7 (to 1 dp)

'''Another Example:''' 50 information elements on a 200x200 display panel delivers
50/(200*200) = 0,00125 information elements/pixel
 
----

== References ==

[Barth, 1997] R. Barth. Metrics for effective information visualization. In ''Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)'', pages 0–108, DC, USA, October 1997. IEEE Computer Society Washington

[Dal Sasso Freitas et al., 2002] Carla M. Dal Sasso Freitas, Paulo R. G. Luzzardi, Ricardo A. Cava, Marco A. A. Winckler, Marcelo S. Pimenta, Luciana P. Nedel. Evaluating Usability of Information Visualization Techniques. In ''Proceedings of 5th Symposium on Human Factors in Computer Systems (IHC)'', pages 10-11, Fortaleza, CE, 2002. Fortaleza:SBC

[Edlinger, 2006] Karl Edlinger, Informationsvisualisierung im Wissensmanagement – Eine Analyse unterschiedlicher Visualisierungstechniken auf ihre Eignung für das Wissensmanagement, Master's thesis, Fachhochschul-Studiengang Informationsberufe, Eisenstadt, 2006

[Hunt and Tyrrell, 1995] Neville Hunt and Sidney Tyrrell, Discovering Important Statistical Concepts Using SpreadSheets. Retrieved at: April 07, 2008. http://www.coventry.ac.uk/ec/research/discus/discus_home.html

[Smith, 2005] Waynes Smith. Graphics and Web Design Based on Edward Tufte's Principles. Created at: January 17, 2005. Retrieved at: October 29, 2006. http://www.washington.edu/computing/training/560/zz-tufte.html

[Tufte, 1999] Edward R. Tufte. The Visual Display of Quantitative Information. Created at: January 26, 1999. Retrieved at: October 29, 2006. http://ldt.stanford.edu/ldt1999/Students/mizuno/Portfolio/Work/reports/tufte/ed229c-tufte-outline.html

File:NewScatterplot.GIF

2009-11-21T10:47:04Z

UE-InfoVis0910 0625847:

== Summary ==
Number of data entries = 92 x 3 = 276

Data density = 276 / 41.4 = 6.7 (to 1 dp)

== Copyright status ==

== Source ==
[Hayden, 2008] Robert W. Hayden. Multiple Regression. http://courses.statistics.com/software/Excel/pulseinf.htm

File:NewScatterplot.GIF

2009-11-21T10:43:32Z

UE-InfoVis0910 0625847: New page: == Beschreibung == == Copyright status == == Source ==

== Beschreibung ==

== Copyright status ==

== Source ==

File:Scatterplot.GIF

2009-11-21T10:36:45Z

UE-InfoVis0910 0625847:

== Summary ==
Number of data entries = 92 x 3 = 276

Data density = 276 / 41.4 = 6.7 (to 1 dp)

== Copyright status ==

== Source ==
[Hayden, 2008] Robert W. Hayden. Running for Excel: Multiple Regression with Qualitative Independent Variables I. http://courses.statistics.com/software/Excel/pulinf2.htm

File:Scatterplot.GIF

2009-11-21T10:26:26Z

UE-InfoVis0910 0625847: New page: == Beschreibung == == Copyright status == == Source ==

== Beschreibung ==

== Copyright status ==

== Source ==

File:Barplot1.GIF

2009-11-21T10:25:19Z

UE-InfoVis0910 0625847:

== Summary ==
Number of data entries = 4 Data density = 4 / 41.4 = 0.1 (to 1 dp)

== Copyright status ==

== Source ==
Created by: [[User:UE-InfoVis0910_0625847|Richard Kloibhofer]]

File:Barplot1.GIF

2009-11-21T10:22:48Z

UE-InfoVis0910 0625847: New page: == Beschreibung == == Copyright status == == Source ==

== Beschreibung ==

== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 2

2009-11-18T18:01:51Z

UE-InfoVis0910 0625847: eyretoed

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe2.html Beschreibung der Aufgabe 2]
=== Zu beurteilende Tabelle ===
[[Image:table3.gif]]

=== Review of the Existing Table ===
 
Well, we have reviewed the table above based on Stephen Few's work "Show Me the Numbers: Designing Tables and Graphs to Enlighten" which contains some important rules and advices in view of designing tables.
 
* A grid is used to delineate columns and rows of the table, which should be avoided as grids break up the data.
* Rules are used to form a boundary around the entire table, which should be avoided if white space permits.
* Numbers in scientific notation are hard to read and not easily comparable to each other.
* Numbers are not aligned properly (numbers that represent quantitative values should always be aligned to the right)
* The headers should be aligned with the associated data. This is international.
* To Place a comma to the left of every three whole-number digits would improve the readability.
* The readability would also be improved if for each value the same number of decimal digits is going to be used, even when they are zeroes.

=== Enhanced Table ===
[[Image:uebung_2.gif]]

=== Changes to the Table ===
 
* The table has been split into two separate tables as the numbers of the two blocks are not comparable.
* The grid has been removed, because it is useless in view of delineate columns and rows [Few, 2004]. Only the header areas are separated from the body of the tables using rules.
* Columns and rows are delineated using white space only, which enhances readability [Few, 2004].
* The U-235 enrichment column is delineated from the rest of the body as it is a calculated value based on the other columns.
* A percent sign was added to each value in the U-235 enrichment column because percentages are used less often than other units of measure, so it’s easy when reading down columns of numbers to forget that you’re looking at percentages [Few, 2004].
* Numbers are aligned to the right.
* The Unit-column has been removed, because they should be part of the headers [Wallace, 2005]. In our case the units are now part of the spanner headers.
* In generel text should always be aligned to the left! Furthermore one exception to the practice of left alignment works well for columns of text: when the entries each consist of the same number of characters and the column header consists of several more characters than the text entries [Few, 2004]. So I think it also works very well in view of numbers.

== Referenzen ==
 
[Few, 2004] Stephen Few. Show Me the Numbers: Designing Tables and Graphs to Enlighten. Analytics Press, 2004, Chapter 8 – Table Design
 
[Wallace, 2005] Rosa Wallace. Designing Tables.
http://www.ncsu.edu/labwrite/res/gh/gh-tables.html

== 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 09|Gruppe 09]]

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 2

2009-11-18T17:36:03Z

UE-InfoVis0910 0625847:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe2.html Beschreibung der Aufgabe 2]
=== Zu beurteilende Tabelle ===
[[Image:table3.gif]]

=== Review of the Existing Table ===
* A grid is used to delineate columns and rows of the table, which should be avoided as grids break up the data.
* Rules are used to form a boundary around the entire table, which should be avoided if white space permits.
* Numbers in scientific notation are hard to read and not easily comparable to each other.
* Numbers are not aligned properly (numbers that represent quantitative values should always be aligned to the right)
* The Headers should be aligned with the associated data. This is international
* To Place a comma to the left of every three whole-number digits would improve the readability
* The readability would also be improved if for each value the same number of decimal digits is going to be used, even when they are zeroes.

=== Enhanced Table ===
[[Image:uebung_2.gif]]

=== Changes to the Table ===
* The table has been split into two separate tables as the numbers of the two blocks are not comparable.
* The grid has been removed, only the header areas are separated from the body of the tables using rules.
* Columns and rows are delineated using white space only, which enhances readability.
* The U-235 enrichment column is delineated from the rest of the body as it is a calculated value based on the other columns.
* A percent sign was added to each value in the U-235 enrichment column.
* Numbers are aligned to the right.
* The Unit-column has been removed. The Units are now part of the spanner headers
* Well, numbers that represent quantitative values should always be aligned to the right! However, I find that one exception to the practice of right alignment works well for columns of numbers: when the entries each consist of the same quantity of numbers and the column header consists of several more characters than the number entries.

== 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 09|Gruppe 09]]

Teaching talk:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 2

2009-11-15T14:33:13Z

UE-InfoVis0910 0625847: New page: --UE-InfoVis0910 0625847 15:20, 15 November 2009 (CET) '''Assumption''' I have been looking on http://sti.srs.gov/fulltext/tr2002506/tr2...

--[[User:UE-InfoVis0910 0625847|UE-InfoVis0910 0625847]] 15:20, 15 November 2009 (CET)
 
'''Assumption'''
 
I have been looking on http://sti.srs.gov/fulltext/tr2002506/tr2002506.html to learn more about the topic of this table. Decontaminated liquid waste has to be disposed! Well, samples of solids were removed from Tank 50H during October of 2002 and sent to the Savannah River Technology Center for analysis [Wilmarth et al., 2002]. Three core samples were sent to SRTC (labeled as TK50-HTF-E-195, TK50-HTF-E-196, and TK50-HTF-E-197). So our table contains the results from radiochemical analysis of three samples from Tank 50H using Aqua Regia Digestion. And the samples were analyzed in duplicate.
 
'''Inprovement propositions'''
 
'''White Space'''
 
White space can be intentionally manipulated to direct your readers’ eyes to either scan predominantly across the columns or down the rows [Few, 2004]. So I would prefer to direct the readers to scan predominantly across the rows so that comparision between the three samples in view of a certain element could be done easier. Hence the the vertical white space between the rows should be bigger. But be careful: This balance between white space and overall data
density is upset when the vertical white space between the rows exceeds the vertical space used by the rows of data themselves [Few, 2004].
 
'''Rules and Grids'''
 
Grids are useless in view of delineating columns and rows [Few, 2004]. Als rules have only a limited usefulness. The problem with rules and grids is that they break up the data (the lines distract the eye, promoting a strong perception of individual cells through the Gestalt principle of enclosure rather than a seamless flow of information). So I would delete the whole grids. Furthermore I would only use rules to separate the header and footer from the body.
 
'''Columns and rows'''
 
You should almost always arrange the categorical subdivisions bidirectionally when you can fit one or more sets of categorical subdivisions across the columns [Few, 2004]. So I wouldn’t do any changes.
 
'''Data Sequence Alignment'''
 
Numbers that represent quantitative values, as opposed to those that are merely identifiers (e.g., customer numbers), should always be aligned to the right [Few, 2004] So I would do so instead of aligning it to the centre. Furthermore I would align both the decimal point and the final digit to the right. This can be accomplished by expressing each value using the same number of decimal digits, even when they are zeroes [Few., 2004]. Now lets have a look at the text. Text that expresses neither numbers nor dates works best when aligned to the left because of the historical conventions of printing [Few, 2004]. So changing into this would be adviseable.
 
Furthermore the headers should be aligned like their associated data, because this is international [Few, 2004]. E.g.: ff the column’s data are left aligned, its header is left aligned as well, and so on.
 
'''Number Precision'''
 
The level of precision should not exceed the level needed to serve your
communication objectives and the needs of your readers [Few, 2004].
 
'''Measuring Units'''
 
Columns should be titled with the name of the variables followed by the units of measure in parentheses[Wallace, 2005]. I woud do so and delete the column called unit.
 
'''Spanner Headers'''
 
We could use a spanner header per sample e.g.: TK50-HTK-E-195 for the first sample and 1 and 2 as columnheaders for the two columns below the spanner header which stands for the duplicate analysis of one sample.
 
'''Ambiguities'''
 
* I don’t know but are the units dgm/g and microg/g compatible or comparative? If not, then I would break the sets of data into smaller groups in view of their unit
* No americium-241 was detected in the solid samples [Wilmarth et al., 2002] . So if there is no americium-241 I think it should be announced in the table
*Furthermore the table contains the results from radiochemical analysis using gamma spectroscopy, alpha spectroscopy and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) Few., 2004. Does this mean that for every sample another method (e.g.: gamma analysis) has been used? If so then I think we should note this fact in the table.
 
'''Refereces'''
 
[Few, 2004] Stephen Few. Show Me the Numbers: Designing Tables and Graphs to Enlighten. Analytics Press, 2004, Chapter 8 – Table Design
 
[Wallace, 2005] Rosa Wallace. Designing Tables.
http://www.ncsu.edu/labwrite/res/gh/gh-tables.html
 
[Wilmarth et al., 2002] W. R. Wilmarth, C. J. Coleman, F. F. Fondeur, V. H. Dukes, M. P. Bussey, M. S. Blume, and A. V. Bowman. Results of Sample Analysis from Solids Removed from Tank 50H. http://sti.srs.gov/fulltext/tr2002506/tr2002506.html

File:Data1.11.gif

2009-11-06T14:37:03Z

UE-InfoVis0910 0625847:

== Summary ==
Number of data entries = 92 x 3 = 276

Data density = 276 / 41.4 = 6.7 (to 1 dp)
== Copyright status ==

== Source ==
[Hunt and Tyrrell, 1995] Neville Hunt and Sidney Tyrrell, Discovering Important Statistical Concepts Using SpreadSheets. Retrieved at: April 07, 2008. http://www.coventry.ac.uk/ec/research/discus/discus_home.html

File:Data1.10.gif

2009-11-06T14:35:33Z

UE-InfoVis0910 0625847:

== Summary ==
Number of data entries = 4
Data density = 4 / 41.4 = 0.1 (to 1 dp)
== Copyright status ==

== Source ==
[Hunt and Tyrrell, 1995] Neville Hunt and Sidney Tyrrell, Discovering Important Statistical Concepts Using SpreadSheets. Retrieved at: April 07, 2008. http://www.coventry.ac.uk/ec/research/discus/discus_home.html

Teaching:TUW - UE InfoVis WS 2006/07 - Gruppe 03 - Aufgabe 1 - Data Density

2009-11-06T14:08:43Z

UE-InfoVis0910 0625847:

== Data Density ==

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 1 | Zurueck zu Aufgabe 1]]

{{Quotation|Number of Data Points means the number of discrete data values represented on screen at an instant. |[Barth, 1997]}}

=== Charts and Graphs ===
The cognitive complexity of an image can be measured by data density (which can be measured by the number of points in a graph) [Dal Sasso Freitas et al., 2002].
 
So data density provides information about how many information elements can be displayed on a defined panel. Therefore you need numerical values which relate the number of maximal at the same time presentable information elements to a display panel [Edlinger, 2006].
 
'''Example:''' 50 information elements on a 200x200 display panel delivers
50/(200*200) = 0,00125 information elements/pixel
 
In general, Tufte assumes that the greater amount of data represented per square centimeter of print, the more effective the resulting representation [Barth, 1997].
Hence he discussed in his work "The Visual Display of Quantitative Information", which is a milestone in the theory of graph design, principles of graph design. One of them is: Try to maximize the data density and the size of the data matrix, within reason. Furthermore as the volume of data increases, data measures must shrink and the graphics can be shrunk way down [Tufte, 1999].
 
So graphics are at their best when they represents very dense and rich datasets because the human eye/brain can select, filter, edit, group, structure, highlight, focus, blend, outline, cluster, itemize, winnow, sort, abstract, smooth, isolate, idealize, summarize, etc. Give people the data so they can exercise their full powers -- don't limit them.
Note that low data densities on computer displays force us to view information sequentially, rather than spatially, which is bad for comprehension [Smith, 2005].
 

Example for data densities include [Smith, 2005]:
* 110,000 numbers/sq-inch for an astronomical graph. This is the maximum known density for a graph to compare. For most scientific journals we get about 50-200 numbers/sq-inch.
* 150 Mbits = human eye
:8 Mbits = typical computer screen
:25 Mbits = color slide
:150 Mbits = large foldout map
:28,000 Characters = Reference book
:18,000 Characters = phone book
:15,000 Characters = non-fiction

=== Example ===
To better understand this term, we show two graphs and compute their data density. Consider the result of a survey in which the gender, height and weight were recorded for 92 students. The charts are 5.6cm by 7.4cm, an area of 41.4cm2 [Hunt and Tyrrell, 1995].

[[Image:Data1.10.gif]]
[[Image:Data1.11.gif]]

Above you see a bar graph showing the breakdown into males and females.
According to the definition you have 2 (female and male) areas with 2 kind of information (gender and number of students), what makes 4 data points. By dividing this over the size of its area, you get the data density of this graph [Barth, 1997].

data density = 4 / 41.4 = 0.1 (to 1 dp)

The computation of the second example works similar to the first one. The second graph shows a labeled XY-chart, which additionally shows the relationship between height and weight of the students. As a result you have 3 kind of information on 92 areas, what results 276 data points [Barth, 1997].

data density = 276 / 41.4 = 6.7 (to 1 dp)

----

== References ==

[Barth, 1997] R. Barth. Metrics for effective information visualization. In ''Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)'', pages 0–108, DC, USA, October 1997. IEEE Computer Society Washington

[Dal Sasso Freitas et al., 2002] Carla M. Dal Sasso Freitas, Paulo R. G. Luzzardi, Ricardo A. Cava, Marco A. A. Winckler, Marcelo S. Pimenta, Luciana P. Nedel. Evaluating Usability of Information Visualization Techniques. In ''Proceedings of 5th Symposium on Human Factors in Computer Systems (IHC)'', pages 10-11, Fortaleza, CE, 2002. Fortaleza:SBC

[Edlinger, 2006] Karl Edlinger, Informationsvisualisierung im Wissensmanagement – Eine Analyse unterschiedlicher Visualisierungstechniken auf ihre Eignung für das Wissensmanagement, Master's thesis, Fachhochschul-Studiengang Informationsberufe, Eisenstadt, 2006

[Hunt and Tyrrell, 1995] Neville Hunt and Sidney Tyrrell, Discovering Important Statistical Concepts Using SpreadSheets. Retrieved at: April 07, 2008. http://www.coventry.ac.uk/ec/research/discus/discus_home.html

[Smith, 2005] Waynes Smith. Graphics and Web Design Based on Edward Tufte's Principles. Created at: January 17, 2005. Retrieved at: October 29, 2006. http://www.washington.edu/computing/training/560/zz-tufte.html

[Tufte, 1999] Edward R. Tufte. The Visual Display of Quantitative Information. Created at: January 26, 1999. Retrieved at: October 29, 2006. http://ldt.stanford.edu/ldt1999/Students/mizuno/Portfolio/Work/reports/tufte/ed229c-tufte-outline.html

Teaching talk:TUW - UE InfoVis WS 2006/07 - Gruppe 03 - Aufgabe 1 - Data Density

2009-11-06T07:05:47Z

UE-InfoVis0910 0625847:

--[[User:UE-InfoVis0910 0625847|UE-InfoVis0910 0625847]] 08:04, 6 November 2009 (CET)

ok, der Link auf die Seite von Neville Hunt funkt jetzt zwar, nur stimmt jetzt das Datum in der Referenz mit dem auf der
Seite nicht überein bzw. der 2. Autor! in der referenz steht Neville Hunt and Housh Mashhoudy und 2002 ... auf der Webseite
steht Neville Hunt and Sidney Tyrrell und 1995 ... müssen wir noch gegebenfalls ändern!
 
Laut der website ist das DISCUS ja ein Set von interaktiven Spreadsheets in Excel! Da die beiden grafiken nicht explizit
in dieser Spreadsheets vorkommen bzw. auch das Beispiel mit den Studenten nicht, stellt sich die frage, wie wir auf das beispiel referenzieren??
 
--[[User:UE-InfoVis0910 0625100|UE-InfoVis0910 0625100]] 19:02, 5 November 2009 (CET)

Bei "Example for data densities include" sind es eigentlich nur 2 Punkte, wie in der Referenz.
Die Datapoints sind bereits im Text beschrieben und daher habe ich sie weggelassen und die Formel herausgehoben
 
--[[User:UE-InfoVis0910 0625100|UE-InfoVis0910 0625100]] 18:48, 5 November 2009 (CET)

Ich habe ganz oben im Zitat die Referenz von Brath auf Barth geändert. Der Link auf die Seite von Neville Hunt war fehlerhaft. Der passt jetzt auch.

Teaching talk:TUW - UE InfoVis WS 2006/07 - Gruppe 03 - Aufgabe 1 - Data Density

2009-11-06T07:04:43Z

UE-InfoVis0910 0625847:

--UE-InfoVis0910 0625847 07:40, 6 November 2009 (CET)

ok, der Link auf die Seite von Neville Hunt funkt jetzt zwar, nur stimmt jetzt das Datum in der Referenz mit dem auf der
Seite nicht überein bzw. der 2. Autor! in der referenz steht Neville Hunt and Housh Mashhoudy und 2002 ... auf der Webseite
steht Neville Hunt and Sidney Tyrrell und 1995 ... müssen wir noch gegebenfalls ändern!
 
Laut der website ist das DISCUS ja ein Set von interaktiven Spreadsheets in Excel! Da die beiden grafiken nicht explizit
in dieser Spreadsheets vorkommen bzw. auch das Beispiel mit den Studenten nicht, stellt sich die frage, wie wir auf das beispiel referenzieren??
 
--[[User:UE-InfoVis0910 0625100|UE-InfoVis0910 0625100]] 19:02, 5 November 2009 (CET)

Bei "Example for data densities include" sind es eigentlich nur 2 Punkte, wie in der Referenz.
Die Datapoints sind bereits im Text beschrieben und daher habe ich sie weggelassen und die Formel herausgehoben
 
--[[User:UE-InfoVis0910 0625100|UE-InfoVis0910 0625100]] 18:48, 5 November 2009 (CET)

Ich habe ganz oben im Zitat die Referenz von Brath auf Barth geändert. Der Link auf die Seite von Neville Hunt war fehlerhaft. Der passt jetzt auch.

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 07 - Aufgabe 1 - Pie Chart

2009-11-06T06:23:41Z

UE-InfoVis0910 0625847:

== Pie Charts==

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 1 | Zurueck zu Aufgabe 1]]

=== Definition ===
{{Quotation | A '''pie chart''' (or a '''circle graph''') is a circular chart divided into sectors, illustrating relative magnitudes or frequencies. In a pie chart, the arc length of each sector (and consequently its central angle and area), is proportional to the quantity it represents. Together, the sectors create a full disk. It is named for its resemblance to a pie which has been sliced.| [Wikipedia, 2008]}}

----

===How to use it===
Pie Charts are used to show percentages. The circle provides a visual concept of the whole (100%). Despite its popularity, pie charts should be used sparingly for two reasons [Statistics Canada, 2008]:
 
* Use less than six components - otherwise, the resulting picture will be too complex to understand.
* Not useful when the values of each component are similar because it is difficult to see the differences between slice sizes.
 
To present certain values in a pie chart it is necessary to determine how many degrees represent this share of data in comparison to the whole circle. This calculation is done by developing the equation:

percent ÷ 100 x 360 degrees = the number of degrees

This ratio works because the total percent of the pie chart represents 100% and there are 360 degrees in a circle [Statistics Canada, 2008].
 
To construct a pie chart the following steps are necessary [Concordia University, 1999]:
* '''Determine the proportions:''' find the total value for the entire category being studied and calculate the percentage for each segment or part.
* '''Calculate degrees:''' convert the percentage values for each segment into degrees relative to the 360 degrees in the circle.
* '''Construct the chart:''' draw a circle and divide it into appropriately sized segments.
* '''Add labels and a title:''' label each segment or add a legend to identify the segments. Then clearly title the chart.

----

=== Variants of the Pie Chart ===

There are different variants of Pie Charts [Wikipedia, 2008]:
* '''Exploded Pie Chart:''' One ore more sectores are seperated from the rest, to highlight it.
* '''Perspective (3D) pie chart:''' Mainly for aesthetic reasons the pie chart is displayed in 3D. But the third dimension does not improve the reading of the data. On the contrary, these plots are difficult to interpret because of the distorted effect of perspective associated with the third dimension. In generell use of superfluous dimensions not used to display the data of interest is discouraged for charts in general, not only for pie charts.
* '''Polar area diagram:''' The polar area diagram is similar to a usual pie chart, except that the sectors are each of an equal angle and differ rather in how far each sector extends from the centre of the circle, enabling multiple comparisons on one diagram.
* '''Multi-Level Pie Chart:''' Such charts are used for representing hierarchical data. The hierarchical structure of data is depicted by means of concentric circles.

----

=== Criticism on the Pie Chart ===

[[image:PiechartsAndBars.png| thumb | Comparison of Pie Chart vs. Bar Chart]]
While pie charts are common in business and journalism, they are uncommon in scientific literature. Pie charts can be an effective way of displaying information in some cases, in particular if the intent is to compare the size of a slice with the whole pie, rather than comparing the slices among them. It has been also shown that comparison by angle was less accurate than comparison by length. Also a comparison by angle (as in pie charts) was shown to be percieved less accurate than comparison by length. The Usage of bars instead of slices improves the capability to compare the different segments of data [Wikipedia, 2008].
----

=== Other Forms of Charts ===
Among Pie Charts, used to display percentages, other forms of charts can be used for different purposes [French, 2008]:

* '''Column Charts''' – are used to show comparisons between items of data. A column in the chart represents the value of one item of data.
* '''Bar Charts''' - are very similar to column charts, except they run horizontally on the page instead of vertically like column charts.
* '''Line Charts''' – are used to show trends over time. Each line in the graph shows the changes in the value of one item of data.

----

=== References ===
*[Concordia University, 1999] Unknown, Pie Chart. Last Modified at: 1999. Retrieved at: November 4, 2008 http://web2.concordia.ca/Quality/tools/21piechart.pdf
*[French, 2008] Ted French. Chart. http://spreadsheets.about.com/od/c/g/chart_def.htm
*[Wikipedia, 2008] Wikipedia. Pie chart. Created at: June 15, 2004. Retrieved at: November 2008. http://en.wikipedia.org/wiki/Pie_chart
*[Statistics Canada, 2008] Statistics Canada. Pie charts. Retrieved at: November 19, 2008. http://www.statcan.ca/english/edu/power/ch9/piecharts/pie.htm

Teaching talk:TUW - UE InfoVis WS 2008/09 - Gruppe 07 - Aufgabe 1 - Pie Chart

2009-11-06T06:22:33Z

UE-InfoVis0910 0625847:

--[[User:UE-InfoVis0910 0625847|UE-InfoVis0910 0625847]] 07:20, 6 November 2009 (CET) 

Folgenden Link habe ich hinausgekickt, da dieser auf keine Seite verweist und auch in unserer Begriffserklärung
kein einziges mal vorkommt!

[Hull, 1998] Stephen Hull, BusinessObjects Glossary. Last Modified at: November 5, 1998. Retreived at: November 4, 2008 http://planning.ucsc.edu/IRPS/dwh/BOBGLOSS.HTM
 
--[[User:UE-InfoVis0910 0625847|UE-InfoVis0910 0625847]] 07:10, 6 November 2009 (CET) 

Ok, das Beispiel ist definitiv falsch! Wenn man die Prozente zusammenzählt, dann ergeben sie mehr
als 100 %, siehe selbst:

* Sleeping 40%
* Partying 15%
* Eating 10%
* Slacking 20%
* Working 30%
* Studying 20%

Hab das Beispiel jetzt mal rausgekick!

--[[User:UE-InfoVis0910 0625100|UE-InfoVis0910 0625100]] 20:04, 5 November 2009 (CET) 
zu lange sätze mit lückenfüllern habe ich gekürzt

der erste link in der referenzangabe ist wieder ein toter Link (würde ich daher wegkicken) 
eine defintion von pie charts ist genügend (finde ich) 
das beispiel ... weiß nicht ... könnte man weglassen (obsolent) .. referenzangabe fehlt auch

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 07 - Aufgabe 1 - Pie Chart

2009-11-06T06:13:16Z

UE-InfoVis0910 0625847:

== Pie Charts==

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 1 | Zurueck zu Aufgabe 1]]

=== Definition ===
{{Quotation | A '''pie chart''' (or a '''circle graph''') is a circular chart divided into sectors, illustrating relative magnitudes or frequencies. In a pie chart, the arc length of each sector (and consequently its central angle and area), is proportional to the quantity it represents. Together, the sectors create a full disk. It is named for its resemblance to a pie which has been sliced.| [Wikipedia, 2008]}}

----

===How to use it===
Pie Charts are used to show percentages. The circle provides a visual concept of the whole (100%). Despite its popularity, pie charts should be used sparingly for two reasons [Statistics Canada, 2008]:
 
* Use less than six components - otherwise, the resulting picture will be too complex to understand.
* Not useful when the values of each component are similar because it is difficult to see the differences between slice sizes.
 
To present certain values in a pie chart it is necessary to determine how many degrees represent this share of data in comparison to the whole circle. This calculation is done by developing the equation:

percent ÷ 100 x 360 degrees = the number of degrees

This ratio works because the total percent of the pie chart represents 100% and there are 360 degrees in a circle [Statistics Canada, 2008].
 
To construct a pie chart the following steps are necessary [Concordia University, 1999]:
* '''Determine the proportions:''' find the total value for the entire category being studied and calculate the percentage for each segment or part.
* '''Calculate degrees:''' convert the percentage values for each segment into degrees relative to the 360 degrees in the circle.
* '''Construct the chart:''' draw a circle and divide it into appropriately sized segments.
* '''Add labels and a title:''' label each segment or add a legend to identify the segments. Then clearly title the chart.

----

=== Variants of the Pie Chart ===

There are different variants of Pie Charts [Wikipedia, 2008]:
* '''Exploded Pie Chart:''' One ore more sectores are seperated from the rest, to highlight it.
* '''Perspective (3D) pie chart:''' Mainly for aesthetic reasons the pie chart is displayed in 3D. But the third dimension does not improve the reading of the data. On the contrary, these plots are difficult to interpret because of the distorted effect of perspective associated with the third dimension. In generell use of superfluous dimensions not used to display the data of interest is discouraged for charts in general, not only for pie charts.
* '''Polar area diagram:''' The polar area diagram is similar to a usual pie chart, except that the sectors are each of an equal angle and differ rather in how far each sector extends from the centre of the circle, enabling multiple comparisons on one diagram.
* '''Multi-Level Pie Chart:''' Such charts are used for representing hierarchical data. The hierarchical structure of data is depicted by means of concentric circles.

----

=== Criticism on the Pie Chart ===

[[image:PiechartsAndBars.png| thumb | Comparison of Pie Chart vs. Bar Chart]]
While pie charts are common in business and journalism, they are uncommon in scientific literature. Pie charts can be an effective way of displaying information in some cases, in particular if the intent is to compare the size of a slice with the whole pie, rather than comparing the slices among them. It has been also shown that comparison by angle was less accurate than comparison by length. Also a comparison by angle (as in pie charts) was shown to be percieved less accurate than comparison by length. The Usage of bars instead of slices improves the capability to compare the different segments of data [Wikipedia, 2008].
----

=== Other Forms of Charts ===
Among Pie Charts, used to display percentages, other forms of charts can be used for different purposes [French, 2008]:

* '''Column Charts''' – are used to show comparisons between items of data. A column in the chart represents the value of one item of data.
* '''Bar Charts''' - are very similar to column charts, except they run horizontally on the page instead of vertically like column charts.
* '''Line Charts''' – are used to show trends over time. Each line in the graph shows the changes in the value of one item of data.

----

=== References ===
*[Hull, 1998] Stephen Hull, BusinessObjects Glossary. Last Modified at: November 5, 1998. Retreived at: November 4, 2008 http://planning.ucsc.edu/IRPS/dwh/BOBGLOSS.HTM
*[Concordia University, 1999] Unknown, Pie Chart. Last Modified at: 1999. Retrieved at: November 4, 2008 http://web2.concordia.ca/Quality/tools/21piechart.pdf
*[French, 2008] Ted French. Chart. http://spreadsheets.about.com/od/c/g/chart_def.htm
*[Wikipedia, 2008] Wikipedia. Pie chart. Created at: June 15, 2004. Retrieved at: November 2008. http://en.wikipedia.org/wiki/Pie_chart
*[Statistics Canada, 2008] Statistics Canada. Pie charts. Retrieved at: November 19, 2008. http://www.statcan.ca/english/edu/power/ch9/piecharts/pie.htm

Teaching talk:TUW - UE InfoVis WS 2008/09 - Gruppe 07 - Aufgabe 1 - Pie Chart

2009-11-06T06:11:10Z

UE-InfoVis0910 0625847:

--[[User:UE-InfoVis0910 0625847|UE-InfoVis0910 0625847]] 07:10, 6 November 2009 (CET) 

Ok, das Beispiel ist definitiv falsch! Wenn man die Prozente zusammenzählt, dann ergeben sie mehr
als 100 %, siehe selbst:

* Sleeping 40%
* Partying 15%
* Eating 10%
* Slacking 20%
* Working 30%
* Studying 20%

Hab das Beispiel jetzt mal rausgekick!

--[[User:UE-InfoVis0910 0625100|UE-InfoVis0910 0625100]] 20:04, 5 November 2009 (CET) 
zu lange sätze mit lückenfüllern habe ich gekürzt

der erste link in der referenzangabe ist wieder ein toter Link (würde ich daher wegkicken) 
eine defintion von pie charts ist genügend (finde ich) 
das beispiel ... weiß nicht ... könnte man weglassen (obsolent) .. referenzangabe fehlt auch

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 07 - Aufgabe 1 - Pie Chart

2009-11-05T12:14:11Z

UE-InfoVis0910 0625847:

== Pie Charts==

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 1 | Zurueck zu Aufgabe 1]]

=== Definition ===
{{Quotation | A '''pie chart''' (or a '''circle graph''') is a circular chart divided into sectors, illustrating relative magnitudes or frequencies. In a pie chart, the arc length of each sector (and consequently its central angle and area), is proportional to the quantity it represents. Together, the sectors create a full disk. It is named for its resemblance to a pie which has been sliced.| [Wikipedia, 2008]}}

----

===How to use it===
The popularity of the pie chart results from its simplicity. The circle provides a visual concept of the whole (100%). Despite its popularity, pie charts should be used sparingly for two reasons [Statistics Canada, 2008]:
 
* First, they are best used for displaying statistical information when there are no more than six components only - otherwise, the resulting picture will be too complex to understand.
* Second, pie charts are not useful when the values of each component are similar because it is difficult to see the differences between slice sizes.
 
A pie chart uses percentages to compare information. Percentages are used because they are the easiest way to represent a whole. The whole is equal to 100%. To present certain values in a pie chart it is necessary to determine how many degrees represent this share of data in comparison to the whole circle. This calculation is done by developing the equation:

percent ÷ 100 x 360 degrees = the number of degrees

This ratio works because the total percent of the pie chart represents 100% and there are 360 degrees in a circle [Statistics Canada, 2008].
 
To construct a pie chart the following steps are necessary [Concordia University, 1999]:
* '''Determine the proportions:''' find the total value for the entire category being studied and calculate the percentage for each segment or part.
* '''Calculate degrees:''' convert the percentage values for each segment into degrees relative to the 360 degrees in the circle.
* '''Construct the chart:''' draw a circle and divide it into appropriately sized segments.
* '''Add labels and a title:''' label each segment or add a legend to identify the segments. Then clearly title the chart.

----

=== Variants of the Pie Chart ===

There are different variants of Pie Charts [Wikipedia, 2008]:
* '''Exploded Pie Chart:''' One ore more sectores are seperated from the rest, to highlight it.
* '''Perspective (3D) pie chart:''' Mainly for aesthetic reasons the pie chart is displayed in 3D. But the third dimension does not improve the reading of the data. On the contrary, these plots are difficult to interpret because of the distorted effect of perspective associated with the third dimension. In generell use of superfluous dimensions not used to display the data of interest is discouraged for charts in general, not only for pie charts.
* '''Polar area diagram:''' The polar area diagram is similar to a usual pie chart, except that the sectors are each of an equal angle and differ rather in how far each sector extends from the centre of the circle, enabling multiple comparisons on one diagram.
* '''Multi-Level Pie Chart:''' Such charts are used for representing hierarchical data. The hierarchical structure of data is depicted by means of concentric circles.

----

=== Criticism on the Pie Chart ===

[[image:PiechartsAndBars.png| thumb | Comparison of Pie Chart vs. Bar Chart]]
While pie charts are common in business and journalism, they are uncommon in scientific literature. Pie charts can be an effective way of displaying information in some cases, in particular if the intent is to compare the size of a slice with the whole pie, rather than comparing the slices among them. It has been also shown that comparison by angle was less accurate than comparison by length. Also a comparison by angle (as in pie charts) was shown to be percieved less accurate than comparison by length. The Usage of bars instead of slices improves the capability to compare the different segments of data [Wikipedia, 2008].

----

===Example===
[[image:pie_chart_example.jpg|picture]]
==== Data ====
A student's life.
Hours per week.
* '''Sleeping''' '''40%'''
* '''Partying''' '''15%'''
* '''Eating''' '''10%'''
* '''Slacking''' '''20%'''
* '''Working''' '''30%'''
* '''Studying''' '''20%'''

----
=== Other Forms of Charts ===
Among Pie Charts, used to display percentages, other forms of charts can be used for different purposes [French, 2008]:

* '''Column Charts''' – are used to show comparisons between items of data. A column in the chart represents the value of one item of data.
* '''Bar Charts''' - are very similar to column charts, except they run horizontally on the page instead of vertically like column charts.
* '''Line Charts''' – are used to show trends over time. Each line in the graph shows the changes in the value of one item of data.

----

=== References ===
*[Hull, 1998] Stephen Hull, BusinessObjects Glossary. Last Modified at: November 5, 1998. Retreived at: November 4, 2008 http://planning.ucsc.edu/IRPS/dwh/BOBGLOSS.HTM
*[Concordia University, 1999] Unknown, Pie Chart. Last Modified at: 1999. Retrieved at: November 4, 2008 http://web2.concordia.ca/Quality/tools/21piechart.pdf
*[French, 2008] Ted French. Chart. http://spreadsheets.about.com/od/c/g/chart_def.htm
*[Wikipedia, 2008] Wikipedia. Pie chart. Created at: June 15, 2004. Retrieved at: November 2008. http://en.wikipedia.org/wiki/Pie_chart
*[Statistics Canada, 2008] Statistics Canada. Pie charts. Retrieved at: November 19, 2008. http://www.statcan.ca/english/edu/power/ch9/piecharts/pie.htm

Teaching:TUW - UE InfoVis WS 2006/07 - Gruppe 03 - Aufgabe 1 - Data Density

2009-11-05T12:08:55Z

UE-InfoVis0910 0625847:

== Data Density ==

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 1 | Zurueck zu Aufgabe 1]]

{{Quotation|Number of Data Points means the number of discrete data values represented on screen at an instant. |[Barth, 1997]}}

{{Quotation|Data Density means the number of data points / number of pixels in the display where number of pixels does not include the pixels in the window borders, menus, etc..|[Barth, 1997]}}

== Charts and Graphs ==
 
The cognitive complexity of an image can be measured by data density (which can be measured by the number of points in a graph) [Dal Sasso Freitas et al., 2002].
 
So data density provides information about how many information elements can be displayed on a defined panel. Therefore you need numerical values which relate the number of maximal at the same time presentable information elements to a display panel [Edlinger, 2006].
 
'''Example:''' 50 information elements on a 200x200 display panel delivers
50/(200*200) = 0,00125 information elements/pixel
 
In general, Tufte assumes that the greater amount of data represented per square centimeter of print, the more effective the resulting representation [Barth, 1997].
Hence he discussed in his work "The Visual Display of Quantitative Information", which is a milestone in the theory of graph design, principles of graph design .One of them is: Try to maximise the data density and the size of the data matrix, within reason. Furthermore as the volume of data increases, data measures must shrink and the graphics can be shrunk way down [Tufte, 1999].
 
So graphics are at their best when they represents very dense and rich datasets because the human eye/brain can select, filter, edit, group, structure, highlight, focus, blend, outline, cluster, itemize, winnow, sort, abstract, smooth, isolate, idealize, summarize, etc. Give people the data so they can exercise their full powers -- don't limit them.
Note that low data densities on computer displays force us to view information sequentially, rather than spatially, which is bad for comprehension [Smith, 2005].
 

Example for data densities include [Smith, 2005]:
* 110,000 numbers/sq-inch for an astronomical graph. This is the maximum known density for a graph to compare. For most scientific journals we get about 50-200 numbers/sq-inch.
* 150 Mbits = human eye
* 8 Mbits = typical computer screen
* 25 Mbits = color slide
* 150 Mbits = large foldout map
* 28,000 Characters = Reference book
* 18,000 Characters = phone book
* 15,000 Characters = non-fiction

== Example ==
To better understand this term, we show two graphs and compute their data density. Consider the result of a survey in which the gender, height and weight were recorded for 92 students. The charts are 5.6cm by 7.4cm, an area of 41.4cm2. [Hunt and Mashhoudy, 2002]

[[Image:Data1.10.gif]]
[[Image:Data1.11.gif]]

Above you see a bar graph showing the breakdown into males and females.
According to the definition you've 2 (female and male) areas with 2 kind of information (gender and number of students), what makes 4 data points. By deviding this over the size of its area, you get the data density of this graph. [Barth, 1997]

number of data points = 4

data density = 4 / 41.4 = 0.1 (to 1 dp)
[Hunt and Mashhoudy, 2002]

The computation of the second example works similiar to the first one. The second graph shows a labeled XY-chart, which additonaly shows the relationship between height and weight of the students. As a result you've 3 kind of information on 92 areas, what results 276 data points. [Barth, 1997]

number of data points = 92 x 3 = 276

data density = 276 / 41.4 = 6.7 (to 1 dp)
[Hunt and Mashhoudy, 2002]

== References ==

[Barth, 1997] R. Barth. Metrics for effective information visualization. In ''Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)'', pages 0–108, DC, USA, October 1997. IEEE Computer Society Washington

[Dal Sasso Freitas et al., 2002] Carla M. Dal Sasso Freitas, Paulo R. G. Luzzardi, Ricardo A. Cava, Marco A. A. Winckler, Marcelo S. Pimenta, Luciana P. Nedel. Evaluating Usability of Information Visualization Techniques. In ''Proceedings of 5th Symposium on Human Factors in Computer Systems (IHC)'', pages 10-11, Fortaleza, CE, 2002. Fortaleza:SBC

[Edlinger, 2006] Karl Edlinger, Informationsvisualisierung im Wissensmanagement – Eine Analyse unterschiedlicher Visualisierungstechniken auf ihre Eignung für das Wissensmanagement, Master's thesis, Fachhochschul-Studiengang Informationsberufe, Eisenstadt, 2006

[Hunt and Mashhoudy, 2002] Neville Hunt and Housh Mashhoudy, Discovering Important Statistical Concepts Using SpreadSheets. Created at: January 29, 2002. Retrieved at: October 28, 2006. http://home.ched.coventry.ac.uk/Volume/vol0/philosop.htm.

[Smith, 2005] Waynes Smith. Graphics and Web Design Based on Edward Tufte's Principles. Created at: January 17, 2005. Retrieved at: October 29, 2006. http://www.washington.edu/computing/training/560/zz-tufte.html

[Tufte, 1999] Edward R. Tufte. The Visual Display of Quantitative Information. Created at: January 26, 1999. Retrieved at: October 29, 2006. http://ldt.stanford.edu/ldt1999/Students/mizuno/Portfolio/Work/reports/tufte/ed229c-tufte-outline.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 07 - Aufgabe 1 - Pie Chart

2009-11-05T12:01:12Z

UE-InfoVis0910 0625847:

== Pie Charts==

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 1 | Zurueck zu Aufgabe 1]]

=== Definition ===
{{Quotation | A '''pie chart''' (or a '''circle graph''') is a circular chart divided into sectors, illustrating relative magnitudes or frequencies. In a pie chart, the arc length of each sector (and consequently its central angle and area), is proportional to the quantity it represents. Together, the sectors create a full disk. It is named for its resemblance to a pie which has been sliced.| [Wikipedia, 2008]}}

----

===How to use it===
The popularity of the pie chart results from its simplicity. The circle provides a visual concept of the whole (100%). Despite its popularity, pie charts should be used sparingly for two reasons [Statistics Canada, 2008]:
 
* First, they are best used for displaying statistical information when there are no more than six components only - otherwise, the resulting picture will be too complex to understand.
* Second, pie charts are not useful when the values of each component are similar because it is difficult to see the differences between slice sizes.
 
A pie chart uses percentages to compare information. Percentages are used because they are the easiest way to represent a whole. The whole is equal to 100%. To present certain values in a pie chart it is necessary to determine how many degrees represent this share of data in comparison to the whole circle. This calculation is done by developing the equation:

percent ÷ 100 x 360 degrees = the number of degrees

This ratio works because the total percent of the pie chart represents 100% and there are 360 degrees in a circle [Statistics Canada, 2008].
 
To construct a pie chart the following steps are necessary [Concordia University, 1999]:
* '''Determine the proportions:''' find the total value for the entire category being studied and calculate the percentage for each segment or part.
* '''Calculate degrees:''' convert the percentage values for each segment into degrees relative to the 360 degrees in the circle.
* '''Construct the chart:''' draw a circle and divide it into appropriately sized segments.
* '''Add labels and a title:''' label each segment or add a legend to identify the segments. Then clearly title the chart.

----

=== Variants of the Pie Chart ===

* '''Exploded Pie Chart:''' One ore more sectores are seperated from the rest, to highlight it.
* '''Perspective (3D) pie chart:''' Mainly for aesthetic reasons the pie chart is displayed in 3D. But the third dimension does not improve the reading of the data. On the contrary, these plots are difficult to interpret because of the distorted effect of perspective associated with the third dimension. In generell use of superfluous dimensions not used to display the data of interest is discouraged for charts in general, not only for pie charts.
* '''Polar area diagram:''' The polar area diagram is similar to a usual pie chart, except that the sectors are each of an equal angle and differ rather in how far each sector extends from the centre of the circle, enabling multiple comparisons on one diagram.
* '''Multi-Level Pie Chart:''' Such charts are used for representing hierarchical data. The hierarchical structure of data is depicted by means of concentric circles.

[Wikipedia, 2008]
----

=== Criticism on the Pie Chart ===

[[image:PiechartsAndBars.png| thumb | Comparison of Pie Chart vs. Bar Chart]]
While pie charts are common in business and journalism, they are uncommon in scientific literature. Pie charts can be an effective way of displaying information in some cases, in particular if the intent is to compare the size of a slice with the whole pie, rather than comparing the slices among them. It has been also shown that comparison by angle was less accurate than comparison by length. Also a comparison by angle (as in pie charts) was shown to be percieved less accurate than comparison by length. The Usage of bars instead of slices improves the capability to compare the different segments of data [Wikipedia, 2008].

----

===Example===
[[image:pie_chart_example.jpg|picture]]
==== Data ====
A student's life.
Hours per week.
* '''Sleeping''' '''40%'''
* '''Partying''' '''15%'''
* '''Eating''' '''10%'''
* '''Slacking''' '''20%'''
* '''Working''' '''30%'''
* '''Studying''' '''20%'''

----
=== Other Forms of Charts ===
Among Pie Charts, used to display percentages, other forms of charts can be used for different purposes [French, 2008]:

* '''Column Charts''' – are used to show comparisons between items of data. A column in the chart represents the value of one item of data.
* '''Bar Charts''' - are very similar to column charts, except they run horizontally on the page instead of vertically like column charts.
* '''Line Charts''' – are used to show trends over time. Each line in the graph shows the changes in the value of one item of data.

----

=== References ===
*[Hull, 1998] Stephen Hull, BusinessObjects Glossary. Last Modified at: November 5, 1998. Retreived at: November 4, 2008 http://planning.ucsc.edu/IRPS/dwh/BOBGLOSS.HTM
*[Concordia University, 1999] Unknown, Pie Chart. Last Modified at: 1999. Retrieved at: November 4, 2008 http://web2.concordia.ca/Quality/tools/21piechart.pdf
*[French, 2008] Ted French. Chart. http://spreadsheets.about.com/od/c/g/chart_def.htm
*[Wikipedia, 2008] Wikipedia. Pie chart. Created at: June 15, 2004. Retrieved at: November 2008. http://en.wikipedia.org/wiki/Pie_chart
*[Statistics Canada, 2008] Statistics Canada. Pie charts. Retrieved at: November 19, 2008. http://www.statcan.ca/english/edu/power/ch9/piecharts/pie.htm

Teaching:TUW - UE InfoVis WS 2006/07 - Gruppe 03 - Aufgabe 1 - Data Density

2009-11-05T11:58:53Z

UE-InfoVis0910 0625847:

== Data Density ==

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 1 | Zurueck zu Aufgabe 1]]

{{Quotation|Data Density means the number of data points / number of pixels in the display where number of pixels does not include the pixels in the window borders, menus, etc..|[Barth, 1997]}}

{{Quotation|Number of Data Points means the number of discrete data values represented on screen at an instant. |[Barth, 1997]}}

== Charts and Graphs ==
 
The cognitive complexity of an image can be measured by data density (which can be measured by the number of points in a graph) [Dal Sasso Freitas et al., 2002].
 
So data density provides information about how many information elements can be displayed on a defined panel. Therefore you need numerical values which relate the number of maximal at the same time presentable information elements to a display panel [Edlinger, 2006].
 
'''Example:''' 50 information elements on a 200x200 display panel delivers
50/(200*200) = 0,00125 information elements/pixel
 
In general, Tufte assumes that the greater amount of data represented per square centimeter of print, the more effective the resulting representation [Barth, 1997].
Hence he discussed in his work "The Visual Display of Quantitative Information, which is a milestone in the theory of graph design, principles of graph design .One of them is: Try to maximise the data density and the size of the data matrix, within reason [Tufte, 1999]. Furthermore as the volume of data increases, data measures must shrink and the graphics can be shrunk way down [Tufte, 1999].
 
So graphics are at their best when they represents very dense and rich datasets because the human eye/brain can select, filter, edit, group, structure, highlight, focus, blend, outline, cluster, itemize, winnow, sort, abstract, smooth, isolate, idealize, summarize, etc. Give people the data so they can exercise their full powers -- don't limit them [Smith, 2005].
Note that low data densities on computer displays force us to view information sequentially, rather than spatially, which is bad for comprehension [Smith, 2005].
 

Example for data densities include [Smith, 2005]:
* 110,000 numbers/sq-inch for an astronomical graph. This is the maximum known density for a graph to compare. For most scientific journals we get about 50-200 numbers/sq-inch.
* 150 Mbits = human eye
* 8 Mbits = typical computer screen
* 25 Mbits = color slide
* 150 Mbits = large foldout map
* 28,000 Characters = Reference book
* 18,000 Characters = phone book
* 15,000 Characters = non-fiction

== Example ==
To better understand this term, we show two graphs and compute their data density. Consider the result of a survey in which the gender, height and weight were recorded for 92 students. The charts are 5.6cm by 7.4cm, an area of 41.4cm2. [Hunt and Mashhoudy, 2002]

[[Image:Data1.10.gif]]
[[Image:Data1.11.gif]]

Above you see a bar graph showing the breakdown into males and females.
According to the definition you've 2 (female and male) areas with 2 kind of information (gender and number of students), what makes 4 data points. By deviding this over the size of its area, you get the data density of this graph. [Barth, 1997]

number of data points = 4

data density = 4 / 41.4 = 0.1 (to 1 dp)
[Hunt and Mashhoudy, 2002]

The computation of the second example works similiar to the first one. The second graph shows a labeled XY-chart, which additonaly shows the relationship between height and weight of the students. As a result you've 3 kind of information on 92 areas, what results 276 data points. [Barth, 1997]

number of data points = 92 x 3 = 276

data density = 276 / 41.4 = 6.7 (to 1 dp)
[Hunt and Mashhoudy, 2002]

== References ==

[Barth, 1997] R. Barth. Metrics for effective information visualization. In ''Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)'', pages 0–108, DC, USA, October 1997. IEEE Computer Society Washington

[Dal Sasso Freitas et al., 2002] Carla M. Dal Sasso Freitas, Paulo R. G. Luzzardi, Ricardo A. Cava, Marco A. A. Winckler, Marcelo S. Pimenta, Luciana P. Nedel. Evaluating Usability of Information Visualization Techniques. In ''Proceedings of 5th Symposium on Human Factors in Computer Systems (IHC)'', pages 10-11, Fortaleza, CE, 2002. Fortaleza:SBC

[Edlinger, 2006] Karl Edlinger, Informationsvisualisierung im Wissensmanagement – Eine Analyse unterschiedlicher Visualisierungstechniken auf ihre Eignung für das Wissensmanagement, Master's thesis, Fachhochschul-Studiengang Informationsberufe, Eisenstadt, 2006

[Hunt and Mashhoudy, 2002] Neville Hunt and Housh Mashhoudy, Discovering Important Statistical Concepts Using SpreadSheets. Created at: January 29, 2002. Retrieved at: October 28, 2006. http://home.ched.coventry.ac.uk/Volume/vol0/philosop.htm.

[Smith, 2005] Waynes Smith. Graphics and Web Design Based on Edward Tufte's Principles. Created at: January 17, 2005. Retrieved at: October 29, 2006. http://www.washington.edu/computing/training/560/zz-tufte.html

[Tufte, 1999] Edward R. Tufte. The Visual Display of Quantitative Information. Created at: January 26, 1999. Retrieved at: October 29, 2006. http://ldt.stanford.edu/ldt1999/Students/mizuno/Portfolio/Work/reports/tufte/ed229c-tufte-outline.html

Teaching:TUW - UE InfoVis WS 2006/07 - Gruppe 03 - Aufgabe 1 - Data Density

2009-11-05T11:57:32Z

UE-InfoVis0910 0625847:

== Data Density ==

[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 09 - Aufgabe 1 | Zurueck zu Aufgabe 1]]

{{Quotation|Data Density means the number of data points / number of pixels in the display where number of pixels does not include the pixels in the window borders, menus, etc..|[Barth, 1997]}}

{{Quotation|Number of Data Points means the number of discrete data values represented on screen at an instant. |[Barth, 1997]}}

== Charts and Graphs ==
 
The cognitive complexity of an image can be measured by data density (which can be measured by the number of points in a graph) [Dal Sasso Freitas et al., 2002].
 
So data density provides information about how many information elements can be displayed on a defined panel. Therefore you need numerical values which relate the number of maximal at the same time presentable information elements to a display panel [Edlinger, 2006].
 
'''Example:''' 50 information elements on a 200x200 display panel delivers
50/(200*200) = 0,00125 information elements/pixel
 
In general, Tufte assumes that the greater amount of data represented per square centimeter of print, the more effective the resulting representation [Barth, 1997].
Hence he discussed in his work "The Visual Display of Quantitative Information, which is a milestone in the theory of graph design, principles of graph design .One of them is: Try to maximise the data density and the size of the data matrix, within reason [Tufte, 1999]. Furthermore as the volume of data increases, data measures must shrink and the graphics can be shrunk way down [Tufte, 1999].
 
So graphics are at their best when they represents very dense and rich datasets because the human eye/brain can select, filter, edit, group, structure, highlight, focus, blend, outline, cluster, itemize, winnow, sort, abstract, smooth, isolate, idealize, summarize, etc. Give people the data so they can exercise their full powers -- don't limit them [Smith, 2005].
Note that low data densities on computer displays force us to view information sequentially, rather than spatially, which is bad for comprehension [Smith, 2005].
 

Example for data densities include [Smith, 2005]:
* 110,000 numbers/sq-inch for an astronomical graph. This is the maximum known density for a graph to compare. For most scientific journals we get about 50-200 numbers/sq-inch.
* 150 Mbits = human eye
* 8 Mbits = typical computer screen
* 25 Mbits = color slide
* 150 Mbits = large foldout map
* 28,000 Characters = Reference book
* 18,000 Characters = phone book
* 15,000 Characters = non-fiction

== Example ==
To better understand this term, we show two graphs and compute their data density. Consider the result of a survey in which the gender, height and weight were recorded for 92 students. The charts are 5.6cm by 7.4cm, an area of 41.4cm2. [Hunt and Mashhoudy, 2002]

[[Image:Data1.10.gif]]
[[Image:Data1.11.gif]]

Above you see a bar graph showing the breakdown into males and females.
According to the definition you've 2 (female and male) areas with 2 kind of information (gender and number of students), what makes 4 data points. By deviding this over the size of its area, you get the data density of this graph. [Barth, 1997]

number of data points = 4

data density = 4 / 41.4 = 0.1 (to 1 dp)
[Hunt and Mashhoudy, 2002]

The computation of the second example works similiar to the first one. The second graph shows a labeled XY-chart, which additonaly shows the relationship between height and weight of the students. As a result you've 3 kind of information on 92 areas, what results 276 data points. [Barth, 1997]

number of data points = 92 x 3 = 276

data density = 276 / 41.4 = 6.7 (to 1 dp)
[Hunt and Mashhoudy, 2002]

== References ==

[Barth, 1997] R. Barth. Metrics for effective information visualization. In ''Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)'', pages 0–108, DC, USA, October 1997. IEEE Computer Society Washington

[Dal Sasso Freitas et al., 2002] Carla M. Dal Sasso Freitas, Paulo R. G. Luzzardi, Ricardo A. Cava, Marco A. A. Winckler, Marcelo S. Pimenta, Luciana P. Nedel. Evaluating Usability of Information Visualization Techniques. In ''Proceedings of 5th Symposium on Human Factors in Computer Systems (IHC)'', pages 10-11, Fortaleza, CE, 2002. Fortaleza:SBC

[Edlinger, 2006] Karl Edlinger, Informationsvisualisierung im Wissensmanagement – Eine Analyse unterschiedlicher Visualisierungstechniken auf ihre Eignung für das Wissensmanagement, Master's thesis, Fachhochschul-Studiengang Informationsberufe, Eisenstadt, 2006

[Hunt and Mashhoudy, 2002] Neville Hunt and Housh Mashhoudy, Discovering Important Statistical Concepts Using SpreadSheets. Created at: January 29, 2002. Retrieved at: October 28, 2006. http://home.ched.coventry.ac.uk/Volume/vol0/philosop.htm.

[Smith, 2005] Waynes Smith. Graphics and Web Design Based on Edward Tufte's Principles. Created at: January 17, 2005. Retrieved at: October 29, 2006. http://www.washington.edu/computing/training/560/zz-tufte.html

[Tufte, 1999] Edward R. Tufte. The Visual Display of Quantitative Information. Created at: January 26, 1999. Retrieved at: October 29, 2006. http://ldt.stanford.edu/ldt1999/Students/mizuno/Portfolio/Work/reports/tufte/ed229c-tufte-outline.html

Teaching talk:TUW - UE InfoVis WS 2008/09 - Gruppe 07 - Aufgabe 1 - Pie Chart

2009-11-05T11:54:56Z

UE-InfoVis0910 0625847: New page: der erste link in der referenzangabe ist wieder ein toter Link (würde ich daher wegkicken) eine defintion von pie charts ist genügend (finde ich) das beispiel ... weiß nic...

der erste link in der referenzangabe ist wieder ein toter Link (würde ich daher wegkicken) 
eine defintion von pie charts ist genügend (finde ich) 
das beispiel ... weiß nicht ... könnte man weglassen (obsolent) .. referenzangabe fehlt auch

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 07 - Aufgabe 1 - Pie Chart

2009-11-05T11:51:50Z

UE-InfoVis0910 0625847:

== Pie Charts==

[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 07 - Aufgabe 1 | Zurueck zu Aufgabe 1]]

=== Definition ===
{{Quotation | A '''pie chart''' (or a '''circle graph''') is a circular chart divided into sectors, illustrating relative magnitudes or frequencies. In a pie chart, the arc length of each sector (and consequently its central angle and area), is proportional to the quantity it represents. Together, the sectors create a full disk. It is named for its resemblance to a pie which has been sliced.| [Wikipedia, 2008]}}

----

===How to use it===
The popularity of the pie chart results from its simplicity. The circle provides a visual concept of the whole (100%). Despite its popularity, pie charts should be used sparingly for two reasons [Statistics Canada, 2008]:
 
* First, they are best used for displaying statistical information when there are no more than six components only - otherwise, the resulting picture will be too complex to understand.
* Second, pie charts are not useful when the values of each component are similar because it is difficult to see the differences between slice sizes.
 
A pie chart uses percentages to compare information. Percentages are used because they are the easiest way to represent a whole. The whole is equal to 100%. To present certain values in a pie chart it is necessary to determine how many degrees represent this share of data in comparison to the whole circle. This calculation is done by developing the equation:

percent ÷ 100 x 360 degrees = the number of degrees

This ratio works because the total percent of the pie chart represents 100% and there are 360 degrees in a circle [Statistics Canada, 2008].
 
To construct a pie chart the following steps are necessary [Concordia University, 1999]:
* '''Determine the proportions:''' find the total value for the entire category being studied and calculate the percentage for each segment or part.
* '''Calculate degrees:''' convert the percentage values for each segment into degrees relative to the 360 degrees in the circle.
* '''Construct the chart:''' draw a circle and divide it into appropriately sized segments.
* '''Add labels and a title:''' label each segment or add a legend to identify the segments. Then clearly title the chart.

----

=== Variants of the Pie Chart ===

* '''Exploded Pie Chart:''' One ore more sectores are seperated from the rest, to highlight it.
* '''Perspective (3D) pie chart:''' Mainly for aesthetic reasons the pie chart is displayed in 3D. But the third dimension does not improve the reading of the data. On the contrary, these plots are difficult to interpret because of the distorted effect of perspective associated with the third dimension. In generell use of superfluous dimensions not used to display the data of interest is discouraged for charts in general, not only for pie charts.
* '''Polar area diagram:''' The polar area diagram is similar to a usual pie chart, except that the sectors are each of an equal angle and differ rather in how far each sector extends from the centre of the circle, enabling multiple comparisons on one diagram.
* '''Multi-Level Pie Chart:''' Such charts are used for representing hierarchical data. The hierarchical structure of data is depicted by means of concentric circles.

[Wikipedia, 2008]
----

=== Criticism on the Pie Chart ===

[[image:PiechartsAndBars.png| thumb | Comparison of Pie Chart vs. Bar Chart]]
While pie charts are common in business and journalism, they are uncommon in scientific literature. Pie charts can be an effective way of displaying information in some cases, in particular if the intent is to compare the size of a slice with the whole pie, rather than comparing the slices among them. It has been also shown that comparison by angle was less accurate than comparison by length. Also a comparison by angle (as in pie charts) was shown to be percieved less accurate than comparison by length. The Usage of bars instead of slices improves the capability to compare the different segments of data [Wikipedia, 2008].

----

===Example===
[[image:pie_chart_example.jpg|picture]]
==== Data ====
A student's life.
Hours per week.
* '''Sleeping''' '''40%'''
* '''Partying''' '''15%'''
* '''Eating''' '''10%'''
* '''Slacking''' '''20%'''
* '''Working''' '''30%'''
* '''Studying''' '''20%'''

----
=== Other Forms of Charts ===
Among Pie Charts, used to display percentages, other forms of charts can be used for different purposes [French, 2008]:

* '''Column Charts''' – are used to show comparisons between items of data. A column in the chart represents the value of one item of data.
* '''Bar Charts''' - are very similar to column charts, except they run horizontally on the page instead of vertically like column charts.
* '''Line Charts''' – are used to show trends over time. Each line in the graph shows the changes in the value of one item of data.

----

=== References ===
*[Hull, 1998] Stephen Hull, BusinessObjects Glossary. Last Modified at: November 5, 1998. Retreived at: November 4, 2008 http://planning.ucsc.edu/IRPS/dwh/BOBGLOSS.HTM
*[Concordia University, 1999] Unknown, Pie Chart. Last Modified at: 1999. Retrieved at: November 4, 2008 http://web2.concordia.ca/Quality/tools/21piechart.pdf
*[French, 2008] Ted French. Chart. http://spreadsheets.about.com/od/c/g/chart_def.htm
*[Wikipedia, 2008] Wikipedia. Pie chart. Created at: June 15, 2004. Retrieved at: November 2008. http://en.wikipedia.org/wiki/Pie_chart
*[Statistics Canada, 2008] Statistics Canada. Pie charts. Retrieved at: November 19, 2008. http://www.statcan.ca/english/edu/power/ch9/piecharts/pie.htm

User talk:UE-InfoVis0910 0625847

2009-11-05T11:33:21Z

UE-InfoVis0910 0625847: New page: Ich habe mal ein Paar Änderungen durchgeführt! Was mich noch ein wenig beschäftigt ist das Example (hierbei findet man keine Referenz, von woher die Bilder stammen) Habe a...

Ich habe mal ein Paar Änderungen durchgeführt! Was mich noch ein wenig beschäftigt ist
das Example (hierbei findet man keine Referenz, von woher die Bilder stammen)
 
Habe auch nachgefragt, was zu tun ist, wenn ein toter Link in der Referenzliste erscheint. Antwort:
 
gut wärs, wenn ihr die Quelle wiederfindet.
Ansonsten eine andere Quelle für den referenzierten Inhalt finden - natürlich nur,
wenn dieser Inhalt sinnvoll ist!
Wenn das nicht möglich ist, dann Inhalt löschen.
Der Artikel soll sowieso auf Vollständigkeit überprüft werden. Falls also wichtige
Inhalte fehlen, dann sollen diese ergänzt werden.

Teaching:TUW - UE InfoVis WS 2006/07 - Gruppe 03 - Aufgabe 1 - Data Density

2009-11-05T11:29:57Z

UE-InfoVis0910 0625847:

== Quotation ==
{{Quotation|Data Density means the number of data points / number of pixels in the display where number of pixels does not include the pixels in the window borders, menus, etc..|[Barth, 1997]}}

{{Quotation|Number of Data Points means the number of discrete data values represented on screen at an instant. |[Barth, 1997]}}

== Charts and Graphs ==
 
The cognitive complexity of an image can be measured by data density (which can be measured by the number of points in a graph) [Dal Sasso Freitas et al., 2002].
 
So data density provides information about how many information elements can be displayed on a defined panel. Therefore you need numerical values which relate the number of maximal at the same time presentable information elements to a display panel [Edlinger, 2006].
 
'''Example:''' 50 information elements on a 200x200 display panel delivers
50/(200*200) = 0,00125 information elements/pixel
 
In general, Tufte assumes that the greater amount of data represented per square centimeter of print, the more effective the resulting representation [Barth, 1997].
Hence he discussed in his work "The Visual Display of Quantitative Information, which is a milestone in the theory of graph design, principles of graph design .One of them is: Try to maximise the data density and the size of the data matrix, within reason [Tufte, 1999]. Furthermore as the volume of data increases, data measures must shrink and the graphics can be shrunk way down [Tufte, 1999].
 
So graphics are at their best when they represents very dense and rich datasets because the human eye/brain can select, filter, edit, group, structure, highlight, focus, blend, outline, cluster, itemize, winnow, sort, abstract, smooth, isolate, idealize, summarize, etc. Give people the data so they can exercise their full powers -- don't limit them [Smith, 2005].
Note that low data densities on computer displays force us to view information sequentially, rather than spatially, which is bad for comprehension [Smith, 2005].
 

Example for data densities include [Smith, 2005]:
* 110,000 numbers/sq-inch for an astronomical graph. This is the maximum known density for a graph to compare. For most scientific journals we get about 50-200 numbers/sq-inch.
* 150 Mbits = human eye
* 8 Mbits = typical computer screen
* 25 Mbits = color slide
* 150 Mbits = large foldout map
* 28,000 Characters = Reference book
* 18,000 Characters = phone book
* 15,000 Characters = non-fiction

== Example ==
To better understand this term, we show two graphs and compute their data density. Consider the result of a survey in which the gender, height and weight were recorded for 92 students. The charts are 5.6cm by 7.4cm, an area of 41.4cm2. [Hunt and Mashhoudy, 2002]

[[Image:Data1.10.gif]]
[[Image:Data1.11.gif]]

Above you see a bar graph showing the breakdown into males and females.
According to the definition you've 2 (female and male) areas with 2 kind of information (gender and number of students), what makes 4 data points. By deviding this over the size of its area, you get the data density of this graph. [Barth, 1997]

number of data points = 4

data density = 4 / 41.4 = 0.1 (to 1 dp)
[Hunt and Mashhoudy, 2002]

The computation of the second example works similiar to the first one. The second graph shows a labeled XY-chart, which additonaly shows the relationship between height and weight of the students. As a result you've 3 kind of information on 92 areas, what results 276 data points. [Barth, 1997]

number of data points = 92 x 3 = 276

data density = 276 / 41.4 = 6.7 (to 1 dp)
[Hunt and Mashhoudy, 2002]

== References ==

[Barth, 1997] R. Barth. Metrics for effective information visualization. In ''Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)'', pages 0–108, DC, USA, October 1997. IEEE Computer Society Washington

[Dal Sasso Freitas et al., 2002] Carla M. Dal Sasso Freitas, Paulo R. G. Luzzardi, Ricardo A. Cava, Marco A. A. Winckler, Marcelo S. Pimenta, Luciana P. Nedel. Evaluating Usability of Information Visualization Techniques. In ''Proceedings of 5th Symposium on Human Factors in Computer Systems (IHC)'', pages 10-11, Fortaleza, CE, 2002. Fortaleza:SBC

[Edlinger, 2006] Karl Edlinger, Informationsvisualisierung im Wissensmanagement – Eine Analyse unterschiedlicher Visualisierungstechniken auf ihre Eignung für das Wissensmanagement, Master's thesis, Fachhochschul-Studiengang Informationsberufe, Eisenstadt, 2006

[Hunt and Mashhoudy, 2002] Neville Hunt and Housh Mashhoudy, Discovering Important Statistical Concepts Using SpreadSheets. Created at: January 29, 2002. Retrieved at: October 28, 2006. http://home.ched.coventry.ac.uk/Volume/vol0/philosop.htm.

[Smith, 2005] Waynes Smith. Graphics and Web Design Based on Edward Tufte's Principles. Created at: January 17, 2005. Retrieved at: October 29, 2006. http://www.washington.edu/computing/training/560/zz-tufte.html

[Tufte, 1999] Edward R. Tufte. The Visual Display of Quantitative Information. Created at: January 26, 1999. Retrieved at: October 29, 2006. http://ldt.stanford.edu/ldt1999/Students/mizuno/Portfolio/Work/reports/tufte/ed229c-tufte-outline.html

Teaching:TUW - UE InfoVis WS 2006/07 - Gruppe 03 - Aufgabe 1 - Data Density

2009-11-05T11:27:49Z

UE-InfoVis0910 0625847:

== Quotation ==
{{Quotation|Data Density means the number of data points / number of pixels in the display where number of pixels does not include the pixels in the window borders, menus, etc..|[Barth, 1997]}}

{{Quotation|Number of Data Points means the number of discrete data values represented on screen at an instant. |[Barth, 1997]}}

== Charts and Graphs ==
 
The cognitive complexity of an image can be measured by data density (which can be measured by the number of points in a graph) [Dal Sasso Freitas et al., 2002].
 
So data density provides information about how many information elements can be displayed on a defined panel. Therefore you need numerical values which relate the number of maximal at the same time presentable information elements to a display panel [Edlinger, 2006].
 
'''Example:''' 50 information elements on a 200x200 display panel delivers
50/(200*200) = 0,00125 information elements/pixel
 
In general, Tufte assumes that the greater amount of data represented per square centimeter of print, the more effective the resulting representation [Barth, 1997].
Hence he discussed in his work "The Visual Display of Quantitative Information, which is a milestone in the theory of graph design, principles of graph design .One of them is: Try to maximise the data density and the size of the data matrix, within reason [Tufte, 1999]. Furthermore as the volume of data increases, data measures must shrink and the graphics can be shrunk way down [Tufte, 1999].
 
So graphics are at their best when they represents very dense and rich datasets because the human eye/brain can select, filter, edit, group, structure, highlight, focus, blend, outline, cluster, itemize, winnow, sort, abstract, smooth, isolate, idealize, summarize, etc. Give people the data so they can exercise their full powers -- don't limit them [Smith, 2005].
Note that low data densities on computer displays force us to view information sequentially, rather than spatially, which is bad for comprehension [Smith, 2005].
 

Example for data densities include [Smith, 2005]:
* 110,000 numbers/sq-inch for an astronomical graph. This is the maximum known density for a graph To compare, for most scientific journals we get about 50-200 numbers/sq-inch.150 Mbits = human eye
* 150 Mbits = human eye
* 8 Mbits = typical computer screen
* 25 Mbits = color slide
* 150 Mbits = large foldout map
* 28,000 Characters = Reference book
* 18,000 Characters = phone book
* 15,000 Characters = non-fiction

== Example ==
To better understand this term, we show two graphs and compute their data density. Consider the result of a survey in which the gender, height and weight were recorded for 92 students. The charts are 5.6cm by 7.4cm, an area of 41.4cm2. [Hunt and Mashhoudy, 2002]

[[Image:Data1.10.gif]]
[[Image:Data1.11.gif]]

Above you see a bar graph showing the breakdown into males and females.
According to the definition you've 2 (female and male) areas with 2 kind of information (gender and number of students), what makes 4 data points. By deviding this over the size of its area, you get the data density of this graph. [Barth, 1997]

number of data points = 4

data density = 4 / 41.4 = 0.1 (to 1 dp)
[Hunt and Mashhoudy, 2002]

The computation of the second example works similiar to the first one. The second graph shows a labeled XY-chart, which additonaly shows the relationship between height and weight of the students. As a result you've 3 kind of information on 92 areas, what results 276 data points. [Barth, 1997]

number of data points = 92 x 3 = 276

data density = 276 / 41.4 = 6.7 (to 1 dp)
[Hunt and Mashhoudy, 2002]

== References ==

[Barth, 1997] R. Barth. Metrics for effective information visualization. In ''Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)'', pages 0–108, DC, USA, October 1997. IEEE Computer Society Washington

[Dal Sasso Freitas et al., 2002] Carla M. Dal Sasso Freitas, Paulo R. G. Luzzardi, Ricardo A. Cava, Marco A. A. Winckler, Marcelo S. Pimenta, Luciana P. Nedel. Evaluating Usability of Information Visualization Techniques. In ''Proceedings of 5th Symposium on Human Factors in Computer Systems (IHC)'', pages 10-11, Fortaleza, CE, 2002. Fortaleza:SBC

[Edlinger, 2006] Karl Edlinger, Informationsvisualisierung im Wissensmanagement – Eine Analyse unterschiedlicher Visualisierungstechniken auf ihre Eignung für das Wissensmanagement, Master's thesis, Fachhochschul-Studiengang Informationsberufe, Eisenstadt, 2006

[Hunt and Mashhoudy, 2002] Neville Hunt and Housh Mashhoudy, Discovering Important Statistical Concepts Using SpreadSheets. Created at: January 29, 2002. Retrieved at: October 28, 2006. http://home.ched.coventry.ac.uk/Volume/vol0/philosop.htm.

[Smith, 2005] Waynes Smith. Graphics and Web Design Based on Edward Tufte's Principles. Created at: January 17, 2005. Retrieved at: October 29, 2006. http://www.washington.edu/computing/training/560/zz-tufte.html

[Tufte, 1999] Edward R. Tufte. The Visual Display of Quantitative Information. Created at: January 26, 1999. Retrieved at: October 29, 2006. http://ldt.stanford.edu/ldt1999/Students/mizuno/Portfolio/Work/reports/tufte/ed229c-tufte-outline.html

User:UE-InfoVis0910 0625847

2009-10-14T17:15:39Z

UE-InfoVis0910 0625847: New page: Image:myPic1.jpg '''Richard Kloibhofer''' Forsthausgasse 2-8, 1200 Wien E: e0625847 (at) student.tuwien.ac.at

[[Image:myPic1.jpg]] 
 
'''Richard Kloibhofer''' 
 
Forsthausgasse 2-8, 1200 Wien 
 
E: e0625847 (at) student.tuwien.ac.at

File:MyPic1.jpg

2009-10-14T17:12:23Z

UE-InfoVis0910 0625847: Acdc has been simpsonized!!!

== Beschreibung ==
Acdc has been simpsonized!!!
== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09

2009-10-14T17:05:16Z

UE-InfoVis0910 0625847:

== Gruppenmitglieder ==
[[User:UE-InfoVis0910_9930270|Hubmann-Haidvogel, Alexander]] 
[[User:UE-InfoVis0910_0625847|Kloibhofer, Richard]] 
[[User:UE-InfoVis0910_0625100|Riederer, Martin]] 
== Aufgaben ==
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 0|Aufgabe 0]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 1|Aufgabe 1]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 2|Aufgabe 2]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 3|Aufgabe 3]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 4|Aufgabe 4]]

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09

2009-10-14T17:03:07Z

UE-InfoVis0910 0625847:

== Gruppenmitglieder ==
[[User:UE-InfoVis0910_9930270|Hubmann-Haidvogel, Alexander]]
[[User:UE-InfoVis0910_0625847|Kloibhofer, Richard]]
[[User:UE-InfoVis0910_0625100|Riederer, Martin]]
== Aufgaben ==
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 0|Aufgabe 0]]
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 1|Aufgabe 1]]
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 2|Aufgabe 2]]
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 3|Aufgabe 3]]
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 4|Aufgabe 4]]

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09

2009-10-14T17:00:05Z

UE-InfoVis0910 0625847:

== Gruppenmitglieder ==
[[User:UE-InfoVis0910_9930270|Hubmann-Haidvogel, Alexander]] 
[[User:UE-InfoVis0910_0625847|Kloibhofer, Richard]] 
[[User:UE-InfoVis0910_0625100|Riederer, Martin]] 
== Aufgaben ==
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 0|Aufgabe 0]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 1|Aufgabe 1]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 2|Aufgabe 2]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 3|Aufgabe 3]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09 - Aufgabe 4|Aufgabe 4]]

Teaching:TUW - UE InfoVis WS 2009/10

2009-10-14T16:55:57Z

UE-InfoVis0910 0625847:

[[Image:Aigner03infovis ue.gif]] <big>WS 2009/10</big>

'''LVA Nr:''' 188.308 ([http://tuwis.tuwien.ac.at/lva/tuwien/188308 TUWIS++ Seite])

'''LVA Homepage:''' http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/index.html

'''Leitung:''' [[Gschwandtner, Theresia|Theresia Gschwandtner]] [gschwandtner (at) ifs.tuwien.ac.at] 

== Gruppen ==


[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 03|Gruppe 03 (Lang, Burger, Hackl)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 05|Gruppe 05 (Paizoni, Wuttej, Hudl)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 06|Gruppe 06 (Fried, Fritz, Hiller)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09|Gruppe 09 (Hubmann-Haidvogel, Kloibhofer, Riederer)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15|Gruppe 15 (Martin, Stix, Lenzhofer)]]

== News / Bemerkungen ==

Liebe TeilnehmerInnen! 
Um diese Seite einheitlich zu gestalten (auch bezüglich der Vorjahre), schlage ich vor die Nachnahmen
der Gruppenmitglieder in Klammer neben der Gruppe anzugeben, 
z.B.: Gruppe XX (Maier, Müller, Mustermann). 
-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 10:05, 01 October 2009 (CEST)