InfoVis:Wiki - User contributions [en]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 4

2009-01-07T19:47:25Z

UE-InfoVis0809 9607701:

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

=== Gegebene Daten ===

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

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

===Description===
*Application area analysis & dataset analysis
The predefined dataset describes Homer Simpson's lifetime beverage consumption. 
We distinguish between beverages (water, milk, juice, cola, café, and beer), contained substances (sugar, alcohol, caffeine), and their according recommended daily allowance (RDA). As actual data, we store ingested liters of fluids and the time of consumption (=life-period like child ship, puberty, unemployment, marriage, birth of children, retirement, divorce, illness,..). The exact amounts of substances contained in drinks are stored separately and are not subject of primary interest. A special issue is the unknown number of sugar cubes per café. 
For measurement of consumed drinks, we use the ordinal data type Liter and for substances kilogram or gram, the life period is considered as nominal type and cannot directly be linked to a specific age or time span. This results in a dataset structure of two dimensions.
 
[[Image:Homer.gif]]

*Target group analysis
The visualization is tended for medical and psychological stakeholders. The medical view expresses the consumption of substances like sugar, alcohol and caffeine whereas the psychological view details the intake of fluids. With Homer Simpson’s dataset it is also possible to demonstrate the overall intake of amounts of sugar and similar harmful substances to children. Nevertheless the interface for the visualization should be easily comprehended and user friendly to be able to be operated by different target groups.

*Goal
The goal of the visualization is to get a quick and detailed overview on Homer Simpson’s drinking behavior depending his current life period and circumstances. It should be possible to identify the accumulated quantities of substances in relation to the recommended maximum dose. The interactive visualization should also offer an option to select considered fluids.
===Concept ===

*Visualization
We should use graphs, when the message is contained in the shape of the values and the document you produce will be used to reveal relationship among multiple values [Few, 2004]. In our task about Homer Simpson's drink consumption are too much different values that have to be shown, that a table all alone won't show the information clearly enough.

* Visual Mapping
We separate the two different views for medical and psychologic interests: 
On the psychologic or "drink"-view of our data, the drinks to be shown are selected:
The different life periods are mapped onto the x-axis and the consumed amount (in liters) onto the y-axis. 
On the medical or "substance"-view, the sugar-, coffein- and alcohol-rates can be examined:
Again, the various life periods are shown on the x-axis. For each period, the amounts of consumed substances sugar, caffeine and alcohol are mapped onto the y-axis in multiples of the according RDA. We put a horizontal line where y equals one RDA. This That way, it is easy recognizable which substance consumption exceeds one RDA.

*Description of the technologies
We use a bar diagram to show our nominal dataset, because a simple comparison of the categorical subdivision of one or more measures in no particular order can be shown very easy by this visualization[Few, 2004]. 
On the "substance"-view, we decided to show (for the medicine view) the differences to the maximal dosis by mapping the substances on an RDA scale, where the maximum dose is clearly shown by the horizontal line [Few, 2004]. The bars then show whether Homer Simpson's drinking behavior was adequate for each period, and if not, by how much one RDA was exceeded.

*Interaction
The user can choose between the drinks-view and the substances-view by selecting the according radio button left of the currently displayed diagram (panel "Anzeige"). In the panel "Getränke", one can select up to three drinks for simultanous comparison. This can be done independently form the current view. 
In the "drinks"-view, the legend above the diagram defines the bar colors for all drinks. When a single bar is selected, a floating panel emerges showing the exact consumption of the according drink for that period. Further, all contained substances are listed with their exact value and unit.
In the substances-view, the legend above the main diagram defines the colors for the three covered substances. If a single bar is selected, the emergin panel reveals the exact amount and unit of the according substance consumed in that period, the appropriate RDA value, the substance-specific RDA in its unit (gram, milligram) and a breakdown into drinks, so that one can track the ratio of each drink that contributes to the total.

*MockUp

Figure 1 shows three different drinks in the all life periods, so it is made for the physiological interest on the relation between the drinking habits and the time of his life.

[[Image:Aufgabe4_G.jpg|center|400px|thumb|Fig.1: Drinks-view with selected bar.]]

Figure 2 is made for the medicinical interest on the deviation to the recommended maximum dose.

[[Image:Aufgabe4_S.jpg|center|400px|thumb|Fig.2: Substance-view with selected bar.]]

Additionally we think about a drag-and-drop and copy-option for the different life periods, because maybe Homer has more than one time lovesickness.
Also more than the given drinks could be added.

===References===
*[Few, 2004]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Appendix A - Table and Graph Design at a Glance.

== Links ==

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

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

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 4

2009-01-07T19:39:05Z

UE-InfoVis0809 9607701:

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

=== Gegebene Daten ===

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

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

===Description===
*Application area analysis & dataset analysis
The predefined dataset describes Homer Simpson's lifetime beverage consumption. 
We distinguish between beverages (water, milk, juice, cola, café, and beer), contained substances (sugar, alcohol, caffeine), and their according recommended daily allowance (RDA). As actual data, we store ingested liters of fluids and the time of consumption (=life-period like child ship, puberty, unemployment, marriage, birth of children, retirement, divorce, illness,..). The exact amounts of substances contained in drinks are stored separately and are not subject of primary interest. A special issue is the unknown number of sugar cubes per café. 
For measurement of consumed drinks, we use the ordinal data type Liter and for substances kilogram or gram, the life period is considered as nominal type and cannot directly be linked to a specific age or time span. This results in a dataset structure of two dimensions.
 
[[Image:Homer.gif]]

*Target group analysis
The visualization is tended for medical and psychological stakeholders. The medical view expresses the consumption of substances like sugar, alcohol and caffeine whereas the psychological view details the intake of fluids. With Homer Simpson’s dataset it is also possible to demonstrate the overall intake of amounts of sugar and similar harmful substances to children. Nevertheless the interface for the visualization should be easily comprehended and user friendly to be able to be operated by different target groups.

*Goal
The goal of the visualization is to get a quick and detailed overview on Homer Simpson’s drinking behavior depending his current life period and circumstances. It should be possible to identify the accumulated quantities of substances in relation to the recommended maximum dose. The interactive visualization should also offer an option to select considered fluids.
===Concept ===

*Visualization
We should use graphs, when the message is contained in the shape of the values and the document you produce will be used to reveal relationship among multiple values [Few, 2004]. In our task about Homer Simpson's drink consumption are too much different values that have to be shown, that a table all alone won't show the information clearly enough.

* Visual Mapping
We separate the two different views for medical and psychologic interests: 
On the psychologic or "drink"-view of our data, the drinks to be shown are selected:
The different life periods are mapped onto the x-axis and the consumed amount (in liters) onto the y-axis. 
On the medical or "substance"-view, the sugar-, coffein- and alcohol-rates can be examined:
Again, the various life periods are shown on the x-axis. For each period, the amounts of consumed substances sugar, caffeine and alcohol are mapped onto the y-axis in multiples of the according RDA. We put a horizontal line where y equals one RDA. This That way, it is easy recognizable which substance consumption exceeds one RDA.

*Description of the technologies
We use a bar diagram to show our nominal dataset, because a simple comparison of the categorical subdivision of one or more measures in no particular order can be shown very easy by this visualization[Few, 2004]. 
On the "substance"-view, we decided to show (for the medicine view) the differences to the maximal dosis by mapping the substances on an RDA scale, where the maximum dose is clearly shown by the horizontal line [Few, 2004]. The bars then show whether Homer Simpson's drinking behavior was adequate for each period, and if not, by how much one RDA was exceeded.

*Interaction
The user can choose between the drinks-view and the substances-view by selecting the according radio button left of the currently displayed diagram (panel "Anzeige"). In the panel "Getränke", one can select up to three drinks for simultanous comparison. This can be done independently form the current view. 
In the "drinks"-view, the legend above the diagram defines the bar colors for all drinks. When a single bar is selected, a floating panel emerges showing the exact consumption of the according drink for that period. Further, all contained substances are listed with their exact value and unit.
In the substances-view, the legend above the main diagram defines the colors for the three covered substances. If a single bar is selected, the emergin panel reveals the exact amount and unit of the according substance consumed in that period, the appropriate RDA value, the substance-specific RDA in its unit (gram, milligram) and a breakdown into drinks, so that one can track the ratio of each drink that contributes to the total.

*MockUp

Figure 1 shows the different drinks in the different life periodes, so it is made for the physiological interest on the relation between the drinking habits and the time of his life.

[[Image:Aufgabe4_G.jpg|center|400px|thumb|Fig.1: Drinks-view with selected bar.]]

Figure 2 is made for the medicinical interest on the deviation to the recommended maximum dose.

[[Image:Aufgabe4_S.jpg|center|400px|thumb|Fig.2: Substance-view with selected bar.]]

Additionally we think about an drag-and-drop- and copy-option for the different-life-periodes, because maybe Homer has more than one time lovesickness.
Also more than the given drinks could be added.

===References===
*[Few, 2004]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Appendix A - Table and Graph Design at a Glance.

== Links ==

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

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

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 4

2009-01-07T19:34:14Z

UE-InfoVis0809 9607701:

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

=== Gegebene Daten ===

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

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

===Description===
*Application area analysis & dataset analysis
The predefined dataset describes Homer Simpson's lifetime beverage consumption. 
We distinguish between beverages (water, milk, juice, cola, café, and beer), contained substances (sugar, alcohol, caffeine), and their according recommended daily allowance (RDA). As actual data, we store ingested liters of fluids and the time of consumption (=life-period like child ship, puberty, unemployment, marriage, birth of children, retirement, divorce, illness,..). The exact amounts of substances contained in drinks are stored separately and are not subject of primary interest. A special issue is the unknown number of sugar cubes per café. 
For measurement of consumed drinks, we use the ordinal data type Liter and for substances kilogram or gram, the life period is considered as nominal type and cannot directly be linked to a specific age or time span. This results in a dataset structure of two dimensions.
 
[[Image:Homer.gif]]

*Target group analysis
The visualization is tended for medical and psychological stakeholders. The medical view expresses the consumption of substances like sugar, alcohol and caffeine whereas the psychological view details the intake of fluids. With Homer Simpson’s dataset it is also possible to demonstrate the overall intake of amounts of sugar and similar harmful substances to children. Nevertheless the interface for the visualization should be easily comprehended and user friendly to be able to be operated by different target groups.

*Goal
The goal of the visualization is to get a quick and detailed overview on Homer Simpson’s drinking behavior depending his current life period and circumstances. It should be possible to identify the accumulated quantities of substances in relation to the recommended maximum dose. The interactive visualization should also offer an option to select considered fluids.
===Concept ===

*Visualization
We should use graphs, when the message is contained in the shape of the values and the document you produce will be used to reveal relationship among multiple values [Few, 2004]. In our task about Homer Simpson's drink consumption are too much different values that have to be shown, that a table all alone won't show the information clearly enough.

* Visual Mapping
We separate the two different views for medical and psychologic interests: 
On the psychologic or "drink"-view of our data, the drinks to be shown are selected:
The different life periods are mapped onto the x-axis and the consumed amount (in liters) onto the y-axis. 
On the medical or "substance"-view, the sugar-, coffein- and alcohol-rates can be examined:
Again, the various life periods are shown on the x-axis. For each period, the amounts of consumed substances sugar, caffeine and alcohol are mapped onto the y-axis in multiples of the according RDA. We put a horizontal line where y equals one RDA. This That way, it is easy recognizable which substance consumption exceeds one RDA.

*Description of the technologies
We use a bar diagram to show our nominal dataset, because a simple comparison of the categorical subdivision of one or more measures in no particular order can be shown very easy by this visualization[Few, 2004]. 
On the "substance"-view, we decided to show (for the medicine view) the differences to the maximal dosis by mapping the substances on an RDA scale, where the maximum dose is clearly shown by the horizontal line [Few, 2004]. The bars then show whether Homer Simpson's drinking behavior was adequate for each period, and if not, by how much one RDA was exceeded.

*Interaction
The user can choose between the drinks-view and the substances-view by selecting the according radio button left of the currently displayed diagram (panel "Anzeige"). In the panel "Getränke", one can select up to three drinks for simultanous comparison. This can be done independently form the current view. 
In the "drinks"-view, the legend above the diagram defines the bar colors for all drinks. When a single bar is selected, a floating panel emerges showing the exact consumption of the according drink for that period. Further, all contained substances are listed with their exact value and unit.
In the substances-view, the legend above the main diagram defines the colors for the three covered substances. If a single bar is selected, the emergin panel reveals the exact amount and unit of the according substance consumed in that period, the appropriate RDA value, the substance-specific RDA in its unit (gram, milligram) and a breakdown into drinks, so that one can track the ratio of each drink that contributes to the total.

*MockUp

Figure1=drinks-view with selected bar
[[Image:Aufgabe4_G.jpg|400px|Redesigned diargram]]

Figure 1 shows the different drinks in the different life periodes, so it is made for the physiological interest on the relation between the drinking habits and the time of his life.

Figure2=substance-view with selected bar

[[Image:Aufgabe4_S.jpg|400px|Redesigned diargram]]
Figure 2 is made for the medicinical interest on the deviation to the recommended maximum dose.

Additionally we think about an drag-and-drop- and copy-option for the different-life-periodes, because maybe Homer has more than one time lovesickness.
Also more than the given drinks could be added.

===References===
*[Few, 2004]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Appendix A - Table and Graph Design at a Glance.

== Links ==

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

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

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 4

2009-01-07T19:09:19Z

UE-InfoVis0809 9607701:

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

=== Gegebene Daten ===

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

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

===Description===
*Application area analysis & dataset analysis
The predefined dataset describes Homer Simpson's lifetime beverage consumption. 
We distinguish between beverages (water, milk, juice, cola, café, and beer), contained substances (sugar, alcohol, caffeine), and their according recommended daily allowance (RDA). As actual data, we store ingested liters of fluids and the time of consumption (=life-period like child ship, puberty, unemployment, marriage, birth of children, retirement, divorce, illness,..). The exact amounts of substances contained in drinks are stored separately and are not subject of primary interest. A special issue is the unknown number of sugar cubes per café. 
For measurement of consumed drinks, we use the ordinal data type Liter and for substances kilogram or gram, the life period is considered as nominal type and cannot directly be linked to a specific age or time span. This results in a dataset structure of two dimensions.
 
[[Image:Homer.gif]]

*Target group analysis

*Goal

===Concept ===

*Visualization
We should use graphs, when the message is contained in the shape of the values and the document you produce will be used to reveal relationship among multiple values [Few, 2004]. In our task about Homer Simpson's drink consumption are too much different values that have to be shown, that a table all alone won't show the information clearly enough.

* Visual Mapping
We separate the two different views for medical and psychologic interests: 
On the psychologic or "drink"-view of our data, the drinks to be shown are selected:
The different life periods are mapped onto the x-axis and the consumed amount (in liters) onto the y-axis. 
On the medical or "substance"-view, the sugar-, coffein- and alcohol-rates can be examined:
Again, the various life periods are shown on the x-axis. For each period, the amounts of consumed substances sugar, caffeine and alcohol are mapped onto the y-axis in multiples of the according RDA. We put a horizontal line where y equals one RDA. This That way, it is easy recognizable which substance consumption exceeds one RDA.

*Description of the technologies
We use a bar diagram to show our nominal dataset, because a simple comparison of the categorical subdivision of one or more measures in no particular order can be shown very easy by this visualization[Few, 2004]. 
On the "substance"-view, we decided to show (for the medicine view) the differences to the maximal dosis by mapping the substances on an RDA scale, where the maximum dose is clearly shown by the horizontal line [Few, 2004]. The bars then show whether Homer Simpson's drinking behavior was adequate for each period, and if not, by how much one RDA was exceeded.

*Interaction
The user can use between the two views by clicking on the arrows between the two selection windows, where the different drinks and the different substances can be selected. He can select one or more drinks/substances, which will be shown on the diagram. 
In the "drinks"-view by selecting a bar he can see a label, where the content of substances will be shown and their recommended maximum dose. 
So in the substances-view the liters of the drink and the g, mg and ml are popped-up in a label, when the bar is selected. 
The selection of the different views is made for the different viewers and interests.

*MockUp

Figure1=drinks-view with selected bar
[[Image:Aufgabe4_G.jpg|400px|Redesigned diargram]]

Figure 1 shows the different drinks in the different life periodes, so it is made for the physiological interest on the relation between the drinking habits and the time of his life.

Figure2=substance-view with selected bar

[[Image:Aufgabe4_S.jpg|400px|Redesigned diargram]]
Figure 2 is made for the medicinical interest on the deviation to the recommended maximum dose.

Additionally we think about an drag-and-drop- and copy-option for the different-life-periodes, because maybe Homer has more than one time lovesickness.
Also more than the given drinks could be added.

===References===
*[Few, 2004]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Appendix A - Table and Graph Design at a Glance.

== Links ==

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

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

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 4

2009-01-07T18:51:18Z

UE-InfoVis0809 9607701:

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

=== Gegebene Daten ===

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

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

===Description===
*Application area analysis & dataset analysis
The predefined dataset describes Homer Simpson's lifetime beverage consumption. 
We distinguish between beverages (water, milk, juice, cola, café, and beer), contained substances (sugar, alcohol, caffeine), and their according recommended daily allowance (RDA). As actual data, we store ingested liters of fluids and the time of consumption (=life-period like child ship, puberty, unemployment, marriage, birth of children, retirement, divorce, illness,..). The exact amounts of substances contained in drinks are stored separately and are not subject of primary interest. A special issue is the unknown number of sugar cubes per café. 
For measurement of consumed drinks, we use the ordinal data type Liter and for substances kilogram or gram, the life period is considered as nominal type and cannot directly be linked to a specific age or time span. This results in a dataset structure of two dimensions.
 
[[Image:Homer.gif]]

*Target group analysis

*Goal

===Concept ===

*Visualization
We should use graphs, when the message is contained in the shape of the values and the document you produce will be used to reveal relationship among multiple values[Few, 2004]. In our task about Homer Simpson's Drinking are too much different values that have to be shown, that a table all alone wouldn't show the information clearly enough.

* Visual Mapping
We separate the two different views for medical and psychologic interests: 
On the psychologic or "drink"-view of our data, the drinks to be shown are selected:
The different life-periods are mapped onto the x-axis and the liters onto the y-axis. 
On the medical or "substance"-view, the sugar-, coffein- and alcohol-rates can be seen:
The different life-periodes are shown on the x-axis.
The percent, in relation to the recommended maximum dose (=100%) of different substances in the drinks per day, is mapped on the y-axis.

*Description of the technologies
We use a bar diagram to show our nominal dataset, because a simple comparison of the categorical subdivision of one or more measures in no particular order can be shown very easy by this visualization[Few, 2004]. 
On the "substance"-view, we decided to show (for the medicine view) the differences to the maximal dosis by mapping the substances on a percent-scale, where the maximal dosis is shown by a line[Few, 2004]. The bars show the plus or minus of the substances in Homer Simpson's drinking to these lines.

*Interaction
The user can use between the two views by clicking on the arrows between the two selection windows, where the different drinks and the different substances can be selected. He can select one or more drinks/substances, which will be shown on the diagram. 
In the "drinks"-view by selecting a bar he can see a label, where the content of substances will be shown and their recommended maximum dose. 
So in the substances-view the liters of the drink and the g, mg and ml are popped-up in a label, when the bar is selected. 
The selection of the different views is made for the different viewers and interests.

*MockUp

Figure1=drinks-view with selected bar
[[Image:Aufgabe4_G.jpg|400px|Redesigned diargram]]

Figure 1 shows the different drinks in the different life periodes, so it is made for the physiological interest on the relation between the drinking habits and the time of his life.

Figure2=substance-view with selected bar

[[Image:Aufgabe4_S.jpg|400px|Redesigned diargram]]
Figure 2 is made for the medicinical interest on the deviation to the recommended maximum dose.

Additionally we think about an drag-and-drop- and copy-option for the different-life-periodes, because maybe Homer has more than one time lovesickness.
Also more than the given drinks could be added.

===References===
*[Few, 2004]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Appendix A - Table and Graph Design at a Glance.

== Links ==

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

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

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 4

2009-01-07T18:12:27Z

UE-InfoVis0809 9607701:

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

=== Gegebene Daten ===

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

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

===Description===
*Application area analysis & dataset analysis
The predefined dataset describes the fluids consume of Homer Simpson in his life. 
We distinguish between fluids (water, milk, juice, cola, café, and beer), contained substances (sugar, alcohol, caffeine), and their recommended maximum dose. As actual data we store ingested liters of fluids and the time of consumption (=life-period like child ship, puberty, unemployment, marriage, birth of children, retirement, divorce, illness,..). The exact amounts of substances in liquids are stored separately and are not subject of primary interest. A special issue is the unknown number of sugar cubes per café. 
For measurement of fluids we use the ordinal data type Liter and for substances Kilogram or Gram, the life period is considered as nominal type and cannot directly linked to a specific age. This results in a dataset structure of 2 dimensions.
 
[[Image:Homer.gif]]

*Target group analysis

*Goal

===Concept ===

*Visualization
We should use graphs, when the message is contained in the shape of the values and the document you produce will be used to reveal relationship among multiple values[Few, 2004]. In our task about Homer Simpson's Drinking are too much different values that have to be shown, that a table all alone wouldn't show the information clearly enough.

* Visual Mapping
We separate the two different views for medical and psychologic interests: 
On the psychologic or "drink"-view of our data, the drinks to be shown are selected:
The different life-periods are mapped onto the x-axis and the liters onto the y-axis. 
On the medical or "substance"-view, the sugar-, coffein- and alcohol-rates can be seen:
The different life-periodes are shown on the x-axis.
The percent, in relation to the recommended maximum dose (=100%) of different substances in the drinks per day, is mapped on the y-axis.

*Description of the technologies
We use a bar diagram to show our nominal dataset, because a simple comparison of the categorical subdivision of one or more measures in no particular order can be shown very easy by this visualization[Few, 2004]. 
On the "substance"-view, we decided to show (for the medicine view) the differences to the maximal dosis by mapping the substances on a percent-scale, where the maximal dosis is shown by a line[Few, 2004]. The bars show the plus or minus of the substances in Homer Simpson's drinking to these lines.

*Interaction
The user can use between the two views by clicking on the arrows between the two selection windows, where the different drinks and the different substances can be selected. He can select one or more drinks/substances, which will be shown on the diagram. 
In the "drinks"-view by selecting a bar he can see a label, where the content of substances will be shown and their recommended maximum dose. 
So in the substances-view the liters of the drink and the g, mg and ml are popped-up in a label, when the bar is selected. 
The selection of the different views is made for the different viewers and interests.

*MockUp

Figure1=drinks-view with selected bar
[[Image:Aufgabe4_G.jpg|400px|Redesigned diargram]]

Figure 1 shows the different drinks in the different life periodes, so it is made for the physiological interest on the relation between the drinking habits and the time of his life.

Figure2=substance-view with selected bar

[[Image:Aufgabe4_S.jpg|400px|Redesigned diargram]]
Figure 2 is made for the medicinical interest on the deviation to the recommended maximum dose.

Additionally we think about an drag-and-drop- and copy-option for the different-life-periodes, because maybe Homer has more than one time lovesickness.
Also more than the given drinks could be added.

===References===
*[Few, 2004]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Appendix A - Table and Graph Design at a Glance.

== Links ==

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

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

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

File:Aufgabe4 S.jpg

2009-01-07T18:11:38Z

UE-InfoVis0809 9607701: View of substance RDA per period.

== Summary ==
View of substance RDA per period.
== Copyright status ==

== Source ==

File:Aufgabe4 G.jpg

2009-01-07T18:10:49Z

UE-InfoVis0809 9607701: View of three selected drinks per period.

== Summary ==
View of three selected drinks per period.
== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 4

2009-01-07T18:09:00Z

UE-InfoVis0809 9607701:

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

=== Gegebene Daten ===

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

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

===Description===
*Application area analysis & dataset analysis
The predefined dataset describes the fluids consume of Homer Simpson in his life. 
We distinguish between fluids (water, milk, juice, cola, café, and beer), contained substances (sugar, alcohol, caffeine), and their recommended maximum dose. As actual data we store ingested liters of fluids and the time of consumption (=life-period like child ship, puberty, unemployment, marriage, birth of children, retirement, divorce, illness,..). The exact amounts of substances in liquids are stored separately and are not subject of primary interest. A special issue is the unknown number of sugar cubes per café. 
For measurement of fluids we use the ordinal data type Liter and for substances Kilogram or Gram, the life period is considered as nominal type and cannot directly linked to a specific age. This results in a dataset structure of 2 dimensions.
 
[[Image:Homer.gif]]

*Target group analysis

*Goal

===Concept ===

*Visualization
We should use graphs, when the message is contained in the shape of the values and the document you produce will be used to reveal relationship among multiple values[Few, 2004]. In our task about Homer Simpson's Drinking are too much different values that have to be shown, that a table all alone wouldn't show the information clearly enough.

* Visual Mapping
We separate the two different views for medical and psychologic interests: 
On the psychologic or "drink"-view of our data, the drinks to be shown are selected:
The different life-periods are mapped onto the x-axis and the liters onto the y-axis. 
On the medical or "substance"-view, the sugar-, coffein- and alcohol-rates can be seen:
The different life-periodes are shown on the x-axis.
The percent, in relation to the recommended maximum dose (=100%) of different substances in the drinks per day, is mapped on the y-axis.

*Description of the technologies
We use a bar diagram to show our nominal dataset, because a simple comparison of the categorical subdivision of one or more measures in no particular order can be shown very easy by this visualization[Few, 2004]. 
On the "substance"-view, we decided to show (for the medicine view) the differences to the maximal dosis by mapping the substances on a percent-scale, where the maximal dosis is shown by a line[Few, 2004]. The bars show the plus or minus of the substances in Homer Simpson's drinking to these lines.

*Interaction
The user can use between the two views by clicking on the arrows between the two selection windows, where the different drinks and the different substances can be selected. He can select one or more drinks/substances, which will be shown on the diagram. 
In the "drinks"-view by selecting a bar he can see a label, where the content of substances will be shown and their recommended maximum dose. 
So in the substances-view the liters of the drink and the g, mg and ml are popped-up in a label, when the bar is selected. 
The selection of the different views is made for the different viewers and interests.

*MockUp

Figure1=drinks-view with selected bar
[[Image:Aufgabe4_G|400px|Redesigned diargram]]

Figure 1 shows the different drinks in the different life periodes, so it is made for the physiological interest on the relation between the drinking habits and the time of his life.

Figure2=substance-view with selected bar

[[Image:Aufgabe4_S|400px|Redesigned diargram]]
Figure 2 is made for the medicinical interest on the deviation to the recommended maximum dose.

Additionally we think about an drag-and-drop- and copy-option for the different-life-periodes, because maybe Homer has more than one time lovesickness.
Also more than the given drinks could be added.

===References===
*[Few, 2004]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Appendix A - Table and Graph Design at a Glance.

== Links ==

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

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

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 4

2009-01-03T12:41:27Z

UE-InfoVis0809 9607701:

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 3

2008-12-23T15:46:01Z

UE-InfoVis0809 9607701:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe3.html Beschreibung der Aufgabe 3]

=== Zu beurteilende Grafik ===
[[Image:GeldfuerdenStaat.gif]] 
Beitrag der Steuerpflichtigen zum Einkommensteueraufkommen

===Critics on the table===
*little data-ink ratio
There is too much unnecessary ink used for the graphic, because the important text and values need less place against the borders around them, but not enough to stand out clearly.
The greater the ratio of the ink that you use to communicate the data to the total
amount of ink in the table or graph (i.e., the closer its value is to one), the better
you’ve highlighted the data [Few 2004b].

*visual clutter
**The misalignment of the spending graphic makes the scanning down and across difficult, because we are more sensitve to the vertical and horizontal alignment than we might imagine [Few, 2004a]. If we want to stand out some data, it would be helpful, but in this scenario it isn't.
**The objects positioned on the right are also less seen than the objects positioned on the top, left or in the center and this graphic spent to the right. So the region which could include the easy seen data is unnecessarily white[Few 2004a].
**The unstructured ordering of the pie charts makes it difficult to separatly scan the major content. Firstly there are too much little pie charts which stand nearly in front of each other and enclosure data "too much".
**Secondly the alignmet of the pie charts is different from line to line. But when using indentation, it should be far enough to make the intention clear[Few, 2004a].
**Darker and brighter color makes the contrasting information stand out from the norm[Few, 2004a]. So the red data and pie charts stand out in the graphic, but they aren't as important as they look. The last "100%- and lowest-income-row" doesn't underline the title of the graphic "The richest pay the most". This information would show the first row, but this one isn't red-coloured.
So the most important data isn't standing out in this graphic.

*Organisation of the data

The way the diagram presents the data doesn't fit the message in the header at all. It's not crucial to know that 50% of taxpayers whose gross income is more than 26600€ are paying 93.7% of the collective income tax. To deliver the message correctly it's better to segment the data into meaningful subsets [Few 2004b]. Thus you group the gross income data into groups with upper and lower limits and recalculate the collective tax proportion for these boundaries. Accordingly you have separated the different income groups and can point out the specific tax proportion. Now it's clearly visible that the biggest fraction of taxpayers whose income is between 8,100€ and 29,450€ (55%) are in fact paying only 9 percentage and the smallest and richest fraction are nearly paying half of the overall income tax (45.7%).

Finally, we didn't do that, instead, we left the number of records untouched but converted percentage values so that each value represents the income class from the lower boundary to the next highest one.

===Correction of the table ===

[[Image:CorrectedDiagram.png|400px|Redesigned diargram]]

===Which changes have been made and why is the changed one better than the original table?===

*Data Type Analysis
There are three dimensions: income, percentage in population and percentage in tax revenue of which the latter two share the same unit, percentage. The dimension of income boundaries is discrete with a range of eleven possible values, but with a rather continuous underlying domain (Euro). Further, the highest income class is special in that it lacks an upper bound.

*Diagram redesign

We changed the diagram to the simplest variant, a line plot of income classes on the x-axis against percentage on the y-axis with linearly interconnected data points. In order to keep income classes to scale the x-axis’s unit was set to Euro so classes were represented by their boundaries like in the original, with the addition of proper ralations however.
The two resulting curves clearly show the weight that high income classes have on the total income tax. While the population percentage drops after the lowest income class, tax portions rise from the second income boundary onwards.

Although lowering the data ink ration, we kept the vertical boundary on the right side of the diagram in order not to lose the effect of delimitation.

===References===
*[Few, 2004a]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Chapter 7 - General Design for Communication.

*[Few, 2004b]: Stephen Few, Elegance Through Simplicity, intelligent enterprise, Oct 16, 2004.
[http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920 http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920]

*

== Links ==

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

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

* [[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 3

2008-12-23T14:54:45Z

UE-InfoVis0809 9607701:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe3.html Beschreibung der Aufgabe 3]

=== Zu beurteilende Grafik ===
[[Image:GeldfuerdenStaat.gif]] 
Beitrag der Steuerpflichtigen zum Einkommensteueraufkommen

===Critics on the table===
*little data-ink ratio
There is too much unnecessary ink used for the graphic, because the important text and values need less place against the borders around them, but not enough to stand out clearly.
The greater the ratio of the ink that you use to communicate the data to the total
amount of ink in the table or graph (i.e., the closer its value is to one), the better
you’ve highlighted the data [Few 2004b].

*visual clutter
**The misalignment of the spending graphic makes the scanning down and across difficult, because we are more sensitve to the vertical and horizontal alignment than we might imagine [Few, 2004a]. If we want to stand out some data, it would be helpful, but in this scenario it isn't.
**The objects positioned on the right are also less seen than the objects positioned on the top, left or in the center and this graphic spent to the right. So the region which could include the easy seen data is unnecessarily white[Few 2004a].
**The unstructured ordering of the pie charts makes it difficult to separatly scan the major content. Firstly there are too much little pie charts which stand nearly in front of each other and enclosure data "too much".
**Secondly the alignmet of the pie charts is different from line to line. But when using indentation, it should be far enough to make the intention clear[Few, 2004a].
**Darker and brighter color makes the contrasting information stand out from the norm[Few, 2004a]. So the red data and pie charts stand out in the graphic, but they aren't as important as they look. The last "100%- and lowest-income-row" doesn't underline the title of the graphic "The richest pay the most". This information would show the first row, but this one isn't red-coloured.
So the most important data isn't standing out in this graphic.

*Organisation of the data

The way the diagram presents the data doesn't fit the message in the header at all. It's not crucial to know that 50% of taxpayers whose gross income is more than 26600€ are paying 93.7% of the collective income tax. To deliver the message correctly it's better to segment the data into meaningful subsets [Few 2004b]. Thus you group the gross income data into groups with upper and lower limits and recalculate the collective tax proportion for these boundaries. Accordingly you have separated the different income groups and can point out the specific tax proportion. Now it's clearly visible that the biggest fraction of taxpayers whose income is between 8,100€ and 29,450€ (55%) are in fact paying only 9 percentage and the smallest and richest fraction are nearly paying half of the overall income tax (45.7%).

===Correction of the table ===

[[Image:CorrectedDiagram.png|400px|Redesigned diargram]]

===Which changes have been made and why is the changed one better than the original table?===

*Data Type Analysis
There are three dimensions: income, percentage in population and percentage in tax revenue of which the latter two share the same unit. The dimension of income class is discrete with a range of six possible values, but with a rather continuous underlying domain (Euro). Further, the highest income class is special in that it lacks an upper bound.

*Diagram redesign

We chose the simplest variant, a plot of income classes on the x-axis against percentage on the y-axis with linearly interconnected data points. In order to keep income classes to scale the x-axis’s unit was set to Euro so classes were represented by their boundaries like in the original.
The two resulting curves clearly show the weight that high income classes have on the total income tax.

===References===
*[Few, 2004a]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Chapter 7 - General Design for Communication.

*[Few, 2004b]: Stephen Few, Elegance Through Simplicity, intelligent enterprise, Oct 16, 2004.
[http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920 http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920]

*

== Links ==

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

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

* [[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

File:CorrectedDiagram.png

2008-12-23T14:46:39Z

UE-InfoVis0809 9607701: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 3

2008-12-06T22:46:36Z

UE-InfoVis0809 9607701: fertig einstweilen

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe3.html Beschreibung der Aufgabe 3]

=== Zu beurteilende Grafik ===
[[Image:GeldfuerdenStaat.gif]] 
Beitrag der Steuerpflichtigen zum Einkommensteueraufkommen

===Critics on the table===
*little data-ink ratio
There is too many unnecessary ink used for the graphic, because the important text and values need less place against the borders around them, but not enough to stand out clearly.
The greater the ratio of the ink that you use to communicate the data to the total
amount of ink in the table or graph (i.e., the closer its value is to one), the better
you’ve highlighted the data [Few 2004b].

*visual clutter
**The misalignment of the spending graphic makes the scanning down and across difficult, because we are more sensitve to the vertical and horizontal alignment than we might imagine [Few, 2004a]. If we want to stand out some data, it would be helpful, but in this scenario it isn't.
**The objects positioned on the right are also less seen than the objects positioned on the top, left or in the center and this graphic spent to the right. So the region which could include the easy seen data is unnecessarily white[Few 2004a].
**The unstructured ordering of the pie charts makes it difficult to separatly scan the major content. Firstly there are too much little pie charts which stand nearly in front of each other and enclosure data "too much".
**Secondly the alignmet of the pie charts is different from line to line. But when using indentation, it should be far enough to make the intention clear[Few, 2004a].
**Darker and brighter color makes the contrasting information stand out from the norm[Few, 2004a]. So the red data and pie charts stand out in the graphic, but they aren't as important as they look. The last "100%- and lowest-income-row" doesn't underline the title of the graphic "The richest pay the most". This information would show the first row, but this one isn't red-coloured.
So the most important data isn't standing out in this graphic.

*Organisation of the data

The way the diagram presents the data doesn't fit the message in the header at all. It's not crucial to know that 50 percentage of taxpayers whose gross income is more than 26600 € are paying 93.7 percentage of the collective income tax. To deliver the message correctly it's better to segment the data into meaningful subsets [Few 2004b]. Thus you group the gross income data into groups with upper and lower limits and recalculate the collective tax proportion for these boundaries. Accordingly you have separated the different income groups and can point out the specific tax proportion. Now it's clearly visible that the biggest fraction of taxpayers whose income is between 8,100€ and 29,450€ (55 percentage) are in fact paying only 9 percentage and the smallest and richest fraction are nearly paying half of the overall income tax (45.7%).

[[Image:Teaching_DiagramVariante2d.jpg|right|200px|thumb|Redesigned diargram]]

===Correction of the table ===

*Data Type Analysis
There are three dimensions: income, percentage in population and percentage in tax revenue of which the latter two share the same unit. The dimension of income class is discrete with a range of six possible values, but with a rather continuous underlying domain (Euro). Further, the highest income class is special in that it lacks an upper bound.

*Diagram redesign

First, we thought of the simplest variant, a plot of income classes on the x-axis against percentage on the y-axis with linearly interconnected data points. In order to keep income classes to scale and thus minimizing the lie factor, the x-axis’s unit was set to Euro so classes were represented by the mean of their bounds. The result was two intersecting curves, each made up of six points with non-equidistant x-components due to non-uniform class width. The center of the upwardly boundless income class was chosen arbitrarily (about the mean class width). The disadvantage of this solution is having two curves in the same display range.

The second approach was a cutom diagram, quite similar to the well known population pyramid: one axis (or column) of classes with laterally protruding percentage indicators. As in the first approach, the class width (height in this case) was chosen to scale of according Euro bounds, avoiding lie effects. The problem with adjacent bars as indicators is that without filling, their contours have a distracting effect. Also, the observer then might misinterpret the bars’ surface area as a representation of data (fine idea, but impossible with the constraint of having both axis to scale). Therefore, we chose pins, again with origins centered on class means. The advantage of this diagram is having separated two variables with the same unit plotted against the dependent variable (class). The effect, especially with a dataset like this, is like the one of an unbalanced balance and immediately reveals the situation.

First, income classes were only indicated on a vertical line with horizontal dashes. Although this amplified the described balance effect, there was no space for calss labels. One would have had to add a scale and labels at the left and right border of the diagram, unnecessarily doubling ink. So we decided to adhere to the population pyramid and place the class labeling in the center.

===Which changes have been made and why is the changed one better than the original table?===

===References===
*[Few, 2004a]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Chapter 7 - General Design for Communication.

*[Few, 2004b]: Stephen Few, Elegance Through Simplicity, intelligent enterprise, Oct 16, 2004.
[http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920 http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920]

*

== Links ==

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

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

* [[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

File:Teaching DiagramVariante2d.jpg

2008-12-06T22:43:38Z

UE-InfoVis0809 9607701: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 3

2008-12-06T21:15:34Z

UE-InfoVis0809 9607701:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe3.html Beschreibung der Aufgabe 3]

=== Zu beurteilende Grafik ===
[[Image:GeldfuerdenStaat.gif]] 
Beitrag der Steuerpflichtigen zum Einkommensteueraufkommen

===Critics on the table===
*little data-ink ratio
There is too many unnecessary ink used for the graphic, because the important text and values need less place against the borders around them, but not enough to stand out clearly.
The greater the ratio of the ink that you use to communicate the data to the total
amount of ink in the table or graph (i.e., the closer its value is to one), the better
you’ve highlighted the data [Few 2004b].

*visual clutter
**The misalignment of the spending graphic makes the scanning down and across difficult, because we are more sensitve to the vertical and horizontal alignment than we might imagine [Few, 2004a]. If we want to stand out some data, it would be helpful, but in this scenario it isn't.
**The objects positioned on the right are also less seen than the objects positioned on the top, left or in the center and this graphic spent to the right. So the region which could include the easy seen data is unnecessarily white[Few 2004a].
**The unstructured ordering of the pie charts makes it difficult to separatly scan the major content. Firstly there are too much little pie charts which stand nearly in front of each other and enclosure data "too much".
**Secondly the alignmet of the pie charts is different from line to line. But when using indentation, it should be far enough to make the intention clear[Few, 2004a].
**Darker and brighter color makes the contrasting information stand out from the norm[Few, 2004a]. So the red data and pie charts stand out in the graphic, but they aren't as important as they look. The last "100%- and lowest-income-row" doesn't underline the title of the graphic "The richest pay the most". This information would show the first row, but this one isn't red-coloured.
So the most important data isn't standing out in this graphic.

*Organisation of the data

The way the diagram presents the data doesn't fit the message in the header at all. It's not crucial to know that 50 percentage of taxpayers whose gross income is more than 26600 € are paying 93.7 percentage of the collective income tax. To deliver the message correctly it's better to segment the data into meaningful subsets [Few 2004b]. Thus you group the gross income data into groups with upper and lower limits and recalculate the collective tax proportion for these boundaries. Accordingly you have separated the different income groups and can point out the specific tax proportion. Now it's clearly visible that the biggest fraction of taxpayers whose income is between 8,100€ and 29,450€ (55 percentage) are in fact paying only 9 percentage and the smallest and richest fraction are nearly paying half of the overall income tax (45.7%).

===Correction of the table ===

*Data Type Analysis
There are three dimensions: income, percentage in population and percentage in tax revenue of which the latter two share the same unit. The dimension of income class is discrete with a range of six possible values, but with a rather continuous underlying domain (Euro). Further, the highest income class is special in that it lacks an upper bound.

*Diagram redesign

First, we thought of the simplest variant, a plot of income classes on the x-axis against percentage on the y-axis with linearly interconnected data points. In order to keep income classes to scale and thus minimizing the lie factor, the x-axis’s unit was set to Euro so classes were represented by the mean of their bounds. The result was two intersecting curves, each made up of six points with non-equidistant x-components due to non-uniform class width. The center of the upwardly boundless income class was chosen arbitrarily (about the mean class width). The disadvantage of this solution is having two curves in the same display range.

The second approach was a cutom diagram, quite similar to the well known population pyramid: one axis (or column) of classes with laterally protruding percentage indicators. As in the first approach, the class width (height in this case) was chosen to scale of according Euro bounds, avoiding lie effects. The problem with adjacent bars as indicators is that without filling, their contours have a distracting effect. Also, the observer then might misinterpret the bars’ surface area as a representation of data (fine idea, but impossible with the constraint of having both axis to scale). Therefore, we chose pins, again with origins centered on class means. The advantage of this diagram is having separated two variables with the same unit plotted against the dependent variable (class). The effect, especially with a dataset like this, is like the one of an unbalanced balance and immediately reveals the situation.

First, income classes were only indicated on a vertical line with horizontal dashes. Although this amplified the described balance effect, there was no space for calss labels. One would have had to add a scale and labels at the left and right border of the diagram, unnecessarily doubling ink. So we decided to adhere to the population pyramid and place the class labeling in the center.

===Which changes have been made and why is the changed one better than the original table?===

===References===
*[Few, 2004a]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Chapter 7 - General Design for Communication.

*[Few, 2004b]: Stephen Few, Elegance Through Simplicity, intelligent enterprise, Oct 16, 2004.
[http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920 http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920]

*

== Links ==

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

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

* [[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 3

2008-12-06T17:48:40Z

UE-InfoVis0809 9607701:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe3.html Beschreibung der Aufgabe 3]

=== Zu beurteilende Grafik ===
[[Image:GeldfuerdenStaat.gif]] 
Beitrag der Steuerpflichtigen zum Einkommensteueraufkommen

===Critics on the table===
*little data-ink ratio
There is too many unnecessary ink used for the graphic, because the important text and values need less place against the borders around them, but not enough to stand out clearly.
The greater the ratio of the ink that you use to communicate the data to the total
amount of ink in the table or graph (i.e., the closer its value is to one), the better
you’ve highlighted the data [Few 2004b].

*visual clutter
**The misalignment of the spending graphic makes the scanning down and across difficult, because we are more sensitve to the vertical and horizontal alignment than we might imagine [Few, 2004a]. If we want to stand out some data, it would be helpful, but in this scenario it isn't.
**The objects positioned on the right are also less seen than the objects positioned on the top, left or in the center and this graphic spent to the right. So the region which could include the easy seen data is unnecessarily white[Few 2004a].
**The unstructured ordering of the pie charts makes it difficult to separatly scan the major content. Firstly there are too much little pie charts which stand nearly in front of each other and enclosure data "too much".
**Secondly the alignmet of the pie charts is different from line to line. But when using indentation, it should be far enough to make the intention clear[Few, 2004a].
**Darker and brighter color makes the contrasting information stand out from the norm[Few, 2004a]. So the red data and pie charts stand out in the graphic, but they aren't as important as they look. The last "100%- and lowest-income-row" doesn't underline the title of the graphic "The richest pay the most". This information would show the first row, but this one isn't red-coloured.
So the most important data isn't standing out in this graphic.

*Organisation of the data

The way the diagram presents the data doesn't fit the message in the header at all. It's not crucial to know that 50 percentage of taxpayers whose gross income is more than 26600 € are paying 93.7 percentage of the collective income tax. To deliver the message correctly it's better to segment the data into meaningful subsets [Few 2004b]. Thus you group the gross income data into groups with upper and lower limits and recalculate the collective tax proportion for these boundaries. Accordingly you have separated the different income groups and can point out the specific tax proportion. Now it's clearly visible that the biggest fraction of taxpayers whose income is between 8,100€ and 29,450€ (55 percentage) are in fact paying only 9 percentage and the smallest and richest fraction are nearly paying half of the overall income tax (45.7%).

===Correction of the table ===

Data Type Analysis:
There are three dimensions: income, percentage in population and percentage in tax revenue of which the latter two share the same unit. The dimension of income class is discrete with a range of six possible values, but with a rather continuous underlying domain (Euro). Further, the highest income class is special in that it lacks an upper bound.

Diagram:
First, we thought of the simplest variant, a plot of income classes on the x-axis against percentage on the y-axis with linearly interconnected data points. In order to keep income classes to scale and thus minimizing the lie factor, the x-axis’s unit was set to Euro so classes were represented by the mean of their bounds. The result was two intersecting curves, each made up of six points with non-equidistant x-components due to non-uniform class width. The center of the upwardly boundless income class was chosen arbitrarily (about the mean class width). The disadvantage of this solution is having two curves in the same display range.

The second approach was a cutom diagram, quite similar to the well known population pyramid: one axis (or column) of classes with laterally protruding percentage indicators. As in the first approach, the class width (height in this case) was chosen to scale of according Euro bounds, avoiding lie effects. The problem with adjacent bars as indicators is that without filling, their contours have a distracting effect. Also, the observer then might misinterpret the bars’ surface area as a representation of data (fine idea, but impossible with the constraint of having both axis to scale). Therefore, we chose pins, again with origins centered on class means. The advantage of this diagram is having separated two variables with the same unit plotted against the dependent variable (class). The effect, especially with a dataset like this, is like the one of an unbalanced balance and immediately reveals the situation.

First, income classes were only indicated on a vertical line with horizontal dashes. Although this amplified the described balance effect, there was no space for calss labels. One would have had to add a scale and labels at the left and right border of the diagram, unnecessarily doubling ink. So we decided to adhere to the population pyramid and place the class labeling in the center.

===Which changes have been made and why is the changed one better than the original table?===

===References===
*[Few, 2004a]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Chapter 7 - General Design for Communication.

*[Few, 2004b]: Stephen Few, Elegance Through Simplicity, intelligent enterprise, Oct 16, 2004.
[http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920 http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920]

*

== Links ==

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

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

* [[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Scatterplot

2008-11-16T20:25:49Z

UE-InfoVis0809 9607701: Update after feedback

A scatterplot allows effective visualization of the relation between variables in multidimensional data.

Also called a ''scatter chart'', ''scatter diagram'' or ''scatter graph'' [Wikipedia1, 2008], it is in its most basic form a diagram in which the values of two ''metric'' variables are applied to the horizontal and vertical axes of a cartesian coordinate system. The resulting point in the graph represents one record from a data set. The distribution pattern of points from multiple records reveals, among other qualities, the correlation between the selected variables in the data set. The scatterplot is not to be confused with the ''correlation plot'' [NIST, 2008] which treats already adopted correlation coefficients in different data groups, while the term ''correlation diagram'' does not seem to be bound.

===Revealed Information===

Perfect linear correlation results in all samples lying on the regression line with positive or negative incline dependent on the sign of the correlation coefficient [MSTE, 1997]. Note, that the nonzero incline of the line is insignificant in this kind of diagram [Wikipedia 3, 2008] since it is dependent on axis scales.

An example of perfect correlation can be seen in the upper left of Figure 1 together with other patterns: strong positive (upper right), weak negative (lower left) and one example of variables without significant correlation.

[[Image:SomeScatterplots.jpg|right|200px|thumb|Figure 1: Some scatterplots.]]

Figure 2 features a regression line to further increase expressiveness. It is the line that passes through the plot as close to the points as possible. The regression function is not necessarily chosen linear as in this example. Any kind of curve may fit a plot (quadratic curves, splines, ...) and must be chosen according to subject matter. Generally, the curve with the smallest sum of squared distances to the plotted points is sought after (''least squares fitting''), [NetMBA, 2008]. For an introduction on linear regression, see [Wikipedia 4, 2008; Wikipedia 5, 2008].

[[Image:WeakNegativeCorrelationLine.jpg|right|200px|thumb|Figure 2: Regression line.]]

Further properties of data sets that are easily discovered are the presence of clusters and outliers as denoted in Figure 3.

[[Image:ClustersOutlyers.jpg|right|200px|thumb|Figure 3: Clusters, outliers.]]

===Scatterplots of Higher Dimensions===

Scatterplots are not restricted to records with only two variables. Higher dimensional data can be displayed by adding the third axis to the plot or by assigning point properties like color, size or shape. Figure 4 shows a plot done with Matlab's command "plot4D", [Lewis, 2008].

[[Image:Plot4D.png|right|200px|thumb|Figure 4: 4D plot.]]

For another example of a threedimensional scatterplot refer to [Wikipedia 1, 2008]. A way of plotting multidimensional data without the use of the third axis can be found in [Demsar, 2008].

===Treating Discrete Data===

For continuously distributed data, scatterplots do well in visualizing density. The problem with discrete data is the possibility of more than one record sharing one point in the diagram (''overplotting''). One solution is to alter the point representation according to density, as is achieved by ''sun flower plots'' in which each point symbol gains radial segments as a consequence, [Wikipedia 2, 2008]. Examples can be found here: [Friendly, 2006; addictedtor, 2005].

===Example of Application===

One prominent example of a scatterplot of two variables is the Hertzsprung-Russell diagramm (HRD for short) in astronomy (Figure 5). It plots absolute magnitude (visual brightness) of stars against their effective temperature and shows a rich set of features such as clusters and a central filament (the ''main sequence''), [Britannica, 2008].

[[Image:hr_diagram_local.png|right|200px|thumb|Figure 5: The HRD of some nearby stars.]]

=References=

*[MSTE, 1997]: Anonymous Carolyn, Carolyn's Unit on Graphing. MSTE, University of Illinois. Retrieved at: November 2, 2008. [http://www.mste.uiuc.edu/courses/ci330ms/youtsey/scatterinfo.html http://www.mste.uiuc.edu/courses/ci330ms/youtsey/scatterinfo.html]
*[NIST, 2008] Carroll Croarkin, Paul Tobias. ''NIST/SEMATECH e-Handbook of Statistical Methods''. Retrieved at November 2, 2008 [http://www.itl.nist.gov/div898/handbook/ http://www.itl.nist.gov/div898/handbook/]
*[NetMBA, 2008] Anonymous, Scatter Plot. NetMBA. Retrieved at: 2 November, 2008. [http://www.netmba.com/statistics/plot/scatter/ http://www.netmba.com/statistics/plot/scatter/]
*[addictedtor, 2005]: Anonymous. Sun Flower Plot. R Graph Gallery. Changed: October 6, 2005 Retrieved at: 2 November, 2008. [http://addictedtor.free.fr/graphiques/graphcode.php?graph=59 http://addictedtor.free.fr/graphiques/graphcode.php?graph=59]
*[Friendly, 2006] Michael Friendly, The sunplot macro. York University. Changed at: November 2, 2006. Retrieved at: November 2, 2008 [http://www.math.yorku.ca/SCS/sasmac/sunplot.html http://www.math.yorku.ca/SCS/sasmac/sunplot.html]
*[Demsar, 2008] Janez Demsar, Simple Visualization Examples. A.I. Lab Ljubljana. Retrieved at: November 2, 2008 [http://www.ailab.si/janez/visualizations.html http://www.ailab.si/janez/visualizations.html]
*[Wikipedia 1, 2008] Scatterplot, Wikipedia. Retrieved at: November 2, 2008. [http://en.wikipedia.org/wiki/Scatterplot http://en.wikipedia.org/wiki/Scatterplot]
*[Wikipedia 2, 2008] Streudiagramm, Wikipedia. Retrieved at: November 2, 2008. [http://de.wikipedia.org/wiki/Streudiagramm http://de.wikipedia.org/wiki/Streudiagramm]
*[Wikipedia 3, 2008] Correlation, Wikipedia. Retrieved at: November 2, 2008. [http://en.wikipedia.org/wiki/Correlation http://en.wikipedia.org/wiki/Correlation]
*[Wikipedia 4, 2008] Linear Regression, Wikipedia. Retrieved at 2 November, 2008. [http://en.wikipedia.org/wiki/Linear_regression http://en.wikipedia.org/wiki/Linear_regression]
*[Wikipedia 5, 2008] Regression Analysis, Wikipedia. Retrieved at 2 November, 2008. [http://en.wikipedia.org/wiki/Regression_analysis http://en.wikipedia.org/wiki/Regression_analysis]
*[Lewis, 2008] Antony Lewis, CosmoMC readme. Retrieved at 16 November, 2008. [http://cosmologist.info/cosmomc/readme.html http://cosmologist.info/cosmomc/readme.html]
*[Britannica, 2008] Hertzsprung–Russell diagram. In Encyclopædia Britannica. Retrieved November 16, 2008, from Encyclopædia Britannica Online: [http://www.britannica.com/EBchecked/topic/263951/Hertzsprung-Russell-diagram http://www.britannica.com/EBchecked/topic/263951/Hertzsprung-Russell-diagram]
=External Links=

*Java Applet: [http://matti.usu.edu/nlvm/nav/frames_asid_144_g_4_t_5.html http://matti.usu.edu/nlvm/nav/frames_asid_144_g_4_t_5.html]

File:Hr diagram local.png

2008-11-16T20:13:01Z

UE-InfoVis0809 9607701: HR diagram of some nearby stars.

== Summary ==
HR diagram of some nearby stars.
== Copyright status ==
Creative Commons License
== Source ==
Copyright © Michael Richmond, http://spiff.rit.edu/classes/phys230/lectures/hr/hr.html

File:Plot4D.png

2008-11-16T17:49:41Z

UE-InfoVis0809 9607701: Example of four-dimensional scatterplot done with Matlab's plot4D.

== Summary ==
Example of four-dimensional scatterplot done with Matlab's plot4D.
== Copyright status ==

== Source ==
Antony Lewis, CosmoMC Readme. [http://cosmologist.info/cosmomc/readme.html]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Scatterplot

2008-11-16T12:51:58Z

UE-InfoVis0809 9607701:

A scatterplot (also called a ''scatter chart'', ''scatter diagram'' or ''scatter graph'' [Wikipedia1, 2008]) is a diagram in which the values of two ''metric'' variables are applied to the horizontal and vertical axes of a cartesian coordinate system. The resulting point in the graph represents one record from a data set. The distribution pattern of points from multiple records reveals, among other qualities, the correlation between the selected variables in the data set. The scatterplot is not to be confused with the ''correlation plot'' [NIST, 2008] which treats already adopted correlation coefficients in different data groups, while the term ''correlation diagram'' does not seem to be bound.

===Revealed Information===

Perfect linear correlation results in all samples lying on the regression line with positive or negative incline dependent on the sign of the correlation coefficient [MSTE, 1997]. Note, that the nonzero incline of the line is insignificant in this kind of diagram [Wikipedia 3, 2008] since it is dependent on axis scales.

An example of perfect correlation can be seen in Figure1 together with other patterns: strong
positive, weak negative and one example of variables without significant correlation.

[[Image:SomeScatterplots.jpg|right|200px|thumb|Figure1: Some scatterplots.]]

Figure2 features a regression line to further increase expressiveness. The regression function is not necessarily chosen linear as in this example. Any kind of curve may fit a plot (quadratic, splines, ...). Generally, the curve with the smallest sum of squared distances to the points is sought after, [NetMBA, 2008]. For an introduction on linear regression, see [Wikipedia 4, 2008; Wikipedia 5, 2008].

[[Image:WeakNegativeCorrelationLine.jpg|right|200px|thumb|Figure2: Regression line.]]

Further properties of data sets that are easily discovered are the presence of clusters and outliers(Figure3).

[[Image:ClustersOutlyers.jpg|right|200px|thumb|Figure3: Clusters, outliers.]]

===Scatterplots of Higher Dimensions===

Scatterplots are not restricted to records with only two variables. Higher dimensional data can be displayed by adding the third axis to the plot or by assigning point properties (color, size, shape).

TODO: add figure with colored 3D plot,

For an example of a threedimensional scatterplot refer to [Wikipedia 1, 2008]. A way of plotting multidimensional data without the use of the third axis can be found in [Demsar, 2008].

===Treating Discrete Data===

For continuously distributed data, scatterplots do well in visualizing density. The problem with discrete data is the possibility of more than one record sharing one point in the diagram (''overplotting''). One solution is to alter the point representation according to density, as is achieved by ''sun flower plots'' in which each point symbol gains radial segments as a consequence, [Wikipedia 2, 2008]. Examples can be found here: [Friendly, 2006; addictedtor, 2005].

=References=

*[MSTE, 1997]: Anonymous Carolyn, Carolyn's Unit on Graphing. MSTE, University of Illinois. Retrieved at: November 2, 2008. [http://www.mste.uiuc.edu/courses/ci330ms/youtsey/scatterinfo.html http://www.mste.uiuc.edu/courses/ci330ms/youtsey/scatterinfo.html]
*[NIST, 2008] Carroll Croarkin, Paul Tobias. ''NIST/SEMATECH e-Handbook of Statistical Methods''. Retrieved at November 2, 2008 [http://www.itl.nist.gov/div898/handbook/ http://www.itl.nist.gov/div898/handbook/]
*[NetMBA, 2008] Anonymous, Scatter Plot. NetMBA. Retrieved at: 2 November, 2008. [http://www.netmba.com/statistics/plot/scatter/ http://www.netmba.com/statistics/plot/scatter/]
*[addictedtor, 2005]: Anonymous. Sun Flower Plot. R Graph Gallery. Changed: October 6, 2005 Retrieved at: 2 November, 2008. [http://addictedtor.free.fr/graphiques/graphcode.php?graph=59 http://addictedtor.free.fr/graphiques/graphcode.php?graph=59]
*[Friendly, 2006] Michael Friendly, The sunplot macro. York University. Changed at: November 2, 2006. Retrieved at: November 2, 2008 [http://www.math.yorku.ca/SCS/sasmac/sunplot.html http://www.math.yorku.ca/SCS/sasmac/sunplot.html]
*[Demsar, 2008] Janez Demsar, Simple Visualization Examples. A.I. Lab Ljubljana. Retrieved at: November 2, 2008 [http://www.ailab.si/janez/visualizations.html http://www.ailab.si/janez/visualizations.html]
*[Wikipedia 1, 2008] Scatterplot, Wikipedia. Retrieved at: November 2, 2008. [http://en.wikipedia.org/wiki/Scatterplot http://en.wikipedia.org/wiki/Scatterplot]
*[Wikipedia 2, 2008] Streudiagramm, Wikipedia. Retrieved at: November 2, 2008. [http://de.wikipedia.org/wiki/Streudiagramm http://de.wikipedia.org/wiki/Streudiagramm]
*[Wikipedia 3, 2008] Correlation, Wikipedia. Retrieved at: November 2, 2008. [http://en.wikipedia.org/wiki/Correlation http://en.wikipedia.org/wiki/Correlation]
*[Wikipedia 4, 2008] Linear Regression, Wikipedia. Retrieved at 2 November, 2008. [http://en.wikipedia.org/wiki/Linear_regression http://en.wikipedia.org/wiki/Linear_regression]
*[Wikipedia 5, 2008] Regression Analysis, Wikipedia. Retrieved at 2 November, 2008. [http://en.wikipedia.org/wiki/Regression_analysis http://en.wikipedia.org/wiki/Regression_analysis]
=External Links=

*Java Applet: [http://matti.usu.edu/nlvm/nav/frames_asid_144_g_4_t_5.html http://matti.usu.edu/nlvm/nav/frames_asid_144_g_4_t_5.html]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Scatterplot

2008-11-06T18:28:06Z

UE-InfoVis0809 9607701: Ok for now...

A scatterplot (also called a ''scatter chart'', ''scatter diagram'' or ''scatter graph'' [Wikipedia1, 2008]) is a diagram in which the values of two ''metric'' variables are applied to the horizontal and vertical axes of a cartesian coordinate system. The resulting point in the graph represents one record from a data set. The distribution pattern of points from multiple records reveals, among other qualities, the correlation between the selected variables in the data set. The scatterplot is not to be confused with the ''correlation plot'' [NIST, 2008] which treats already adopted correlation coefficients in different data groups, while the term ''correlation diagram'' does not seem to be bound.

===Revealed Information===

Perfect linear correlation results in all samples lying on the regression line with positive or negative incline dependent on the sign of the correlation coefficient [MSTE, 1997]. Note, that the nonzero incline of the line is insignificant in this kind of diagram [Wikipedia 3, 2008] since it is dependent on axis scales.

An example of perfect correlation can be seen on the right together with other patterns: strong
positive, weak negative and one example of variables without significant correlation.

[[Image:SomeScatterplots.jpg|right|200px|thumb|Some scatterplots.]]

The plot below features a regression line to further increase expressiveness. The regression function is not necessarily chosen linear as in this example. Any kind of curve may fit a plot (quadratic, splines, ...). Generally, the curve with the smallest sum of squared distances to the points is sought after, [NetMBA, 2008]. For an introduction on linear regression, see [Wikipedia 4, 2008; Wikipedia 5, 2008].

[[Image:WeakNegativeCorrelationLine.jpg|right|200px|thumb|Regression line.]]

Further properties of data sets that are easily discovered are the presence of clusters and outlyers.

[[Image:ClustersOutlyers.jpg|right|200px|thumb|Clusters, outlyers.]]

===Scatterplots of Higher Dimensions===

Scatterplots are not restricted to records with only two variables. Higher dimensional data can be displayed by adding the third axis to the plot or by assigning point properties (color, size, shape).

TODO: add figure with colored 3D plot,

For an example of a threedimensional scatterplot refer to [Wikipedia 1, 2008]. A way of plotting multidimensional data without the use of the third axis can be found in [Demsar, 2008].

===Treating Discrete Data===

For continuously distributed data, scatterplots do well in visualizing density. The problem with discrete data is the possibility of more than one record sharing one point in the diagram (''overplotting''). One solution is to alter the point representation according to density, as is achieved by ''sun flower plots'' in which each point symbol gains radial segments as a consequence, [Wikipedia 2, 2008]. Examples can be found here: [Friendly, 2006; addictedtor, 2005].

=References=

*[MSTE, 1997]: Anonymous Carolyn, Carolyn's Unit on Graphing. MSTE, University of Illinois. Retrieved at: November 2, 2008. [http://www.mste.uiuc.edu/courses/ci330ms/youtsey/scatterinfo.html http://www.mste.uiuc.edu/courses/ci330ms/youtsey/scatterinfo.html]
*[NIST, 2008] Carroll Croarkin, Paul Tobias. ''NIST/SEMATECH e-Handbook of Statistical Methods''. Retrieved at November 2, 2008 [http://www.itl.nist.gov/div898/handbook/ http://www.itl.nist.gov/div898/handbook/]
*[NetMBA, 2008] Anonymous, Scatter Plot. NetMBA. Retrieved at: 2 November, 2008. [http://www.netmba.com/statistics/plot/scatter/ http://www.netmba.com/statistics/plot/scatter/]
*[addictedtor, 2005]: Anonymous. Sun Flower Plot. R Graph Gallery. Changed: October 6, 2005 Retrieved at: 2 November, 2008. [http://addictedtor.free.fr/graphiques/graphcode.php?graph=59 http://addictedtor.free.fr/graphiques/graphcode.php?graph=59]
*[Friendly, 2006] Michael Friendly, The sunplot macro. York University. Changed at: November 2, 2006. Retrieved at: November 2, 2008 [http://www.math.yorku.ca/SCS/sasmac/sunplot.html http://www.math.yorku.ca/SCS/sasmac/sunplot.html]
*[Demsar, 2008] Janez Demsar, Simple Visualization Examples. A.I. Lab Ljubljana. Retrieved at: November 2, 2008 [http://www.ailab.si/janez/visualizations.html http://www.ailab.si/janez/visualizations.html]
*[Wikipedia 1, 2008] Scatterplot, Wikipedia. Retrieved at: November 2, 2008. [http://en.wikipedia.org/wiki/Scatterplot http://en.wikipedia.org/wiki/Scatterplot]
*[Wikipedia 2, 2008] Streudiagramm, Wikipedia. Retrieved at: November 2, 2008. [http://de.wikipedia.org/wiki/Streudiagramm http://de.wikipedia.org/wiki/Streudiagramm]
*[Wikipedia 3, 2008] Correlation, Wikipedia. Retrieved at: November 2, 2008. [http://en.wikipedia.org/wiki/Correlation http://en.wikipedia.org/wiki/Correlation]
*[Wikipedia 4, 2008] Linear Regression, Wikipedia. Retrieved at 2 November, 2008. [http://en.wikipedia.org/wiki/Linear_regression http://en.wikipedia.org/wiki/Linear_regression]
*[Wikipedia 5, 2008] Regression Analysis, Wikipedia. Retrieved at 2 November, 2008. [http://en.wikipedia.org/wiki/Regression_analysis http://en.wikipedia.org/wiki/Regression_analysis]
=External Links=

*Java Applet: [http://matti.usu.edu/nlvm/nav/frames_asid_144_g_4_t_5.html http://matti.usu.edu/nlvm/nav/frames_asid_144_g_4_t_5.html]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Scatterplot

2008-11-06T17:03:18Z

UE-InfoVis0809 9607701:

A scatterplot (also called a ''scatter chart'', ''scatter diagram'' or ''scatter graph'' [Wikipedia]) is a diagram in which the values of two ''metric'' variables are applied to the horizontal and vertical axes of a cartesian coordinate system. The resulting point in the graph represents one record from a data set. The distribution pattern of points from multiple records reveals, among other qualities, the correlation between the selected variables in the data set. The scatterplot is not to be confused with the ''correlation plot'' [Information Technology Lab, NIST #2] which treats already adopted correlation coefficients in different data groups, while the term ''correlation diagram'' does not seem to be bound.

===Revealed Information===

Perfect linear correlation results in all samples lying on the regression line with positive or negative incline dependent on the sign of the correlation coefficient [University of Illinois]. Note, that the nonzero incline of the line is insignificant in this kind of diagram [Wikipedia Correlation, EN] since it is dependent on axis scales.

An example of perfect correlation can be seen on the right together with other patterns: strong
positive, weak negative and one example of variables without significant correlation.

[[Image:SomeScatterplots.jpg|right|200px|thumb|Some scatterplots.]]

The plot below features a regression line to further increase expressiveness. The regression function is not necessarily chosen linear as in this example. Any kind of curve may fit a plot (quadratic, splines, ...). Generally, the curve with the smallest sum of squared distances to the points is sought after, [NetMBA].

[Wikipedia Linear Regression]

[[Image:WeakNegativeCorrelationLine.jpg|right|200px|thumb|Regression line.]]

Generally: refer to regression analysis for further ...

Further properties of data sets that are easily discovered are the presence of clusters and outlyers.

density (-> cluster analysis) & outlyers

[[Image:ClustersOutlyers.jpg|right|200px|thumb|Clusters, outlyers.]]

===Scatterplots of Higher Dimensions===

Scatterplots are not restricted to records with only two variables. Higher dimensional data can be displayed by adding the third axis to the plotspacially or by point properties (color, size, shape)

TODO: add figure with colored 3D plot,

[Wikipedia, EN]

Nice example of plotting multidimensional data: [AI Lab]

===Treating Discrete Data===

For continuously distributed data, scatterplots do well in visualizing density. The problem with discrete data is the possibility of more than one record sharing one point in the diagram (''overplotting''). One solution is to alter the point representation according to density, as is achieved by ''sun flower plots'' in which each point symbol gains radial segments as a consequence, [Wikipedia, DE]. Examples can be found here: [Friendly, 2006], [addictedtor.free.fr].

=References=

*[Wikipedia 1, 2008] Scatterplot, Wikipedia. Retrieved at: November 2, 2008.
[http://en.wikipedia.org/wiki/Scatterplot http://en.wikipedia.org/wiki/Scatterplot]
*[Wikipedia 2, 2008] Streudiagramm, Wikipedia. Retrieved at: November 2, 2008.
[http://de.wikipedia.org/wiki/Streudiagramm http://de.wikipedia.org/wiki/Streudiagramm]
*[Wikipedia 3, 2008] Correlation, Wikipedia. Retrieved at: November 2, 2008.
[http://en.wikipedia.org/wiki/Correlation http://en.wikipedia.org/wiki/Correlation]
*[Wikipedia 4, 2008] Linear Regression, Wikipedia. Retrieved at 2 November, 2008.
[http://en.wikipedia.org/wiki/Linear_regression http://en.wikipedia.org/wiki/Linear_regression]
*[MSTE, 1997]: Anonymous Carolyn, Carolyn's Unit on Graphing. MSTE, University of Illinois. Retrieved at: November 2, 2008.
[http://www.mste.uiuc.edu/courses/ci330ms/youtsey/scatterinfo.html http://www.mste.uiuc.edu/courses/ci330ms/youtsey/scatterinfo.html]
*[NIST , 2008] Carroll Croarkin, Paul Tobias. ''NIST/SEMATECH e-Handbook of Statistical Methods''. Retrieved at November 2, 2008
[http://www.itl.nist.gov/div898/handbook/ http://www.itl.nist.gov/div898/handbook/]

http://www.itl.nist.gov/div898/handbook/eda/section3/eda33q.htm
http://www.itl.nist.gov/div898/handbook/eda/section3/linecorr.htm

*[NetMBA, 2008] Anonymous, Scatter Plot. NetMBA. Retrieved at: 2 November, 2008.
[http://www.netmba.com/statistics/plot/scatter/ http://www.netmba.com/statistics/plot/scatter/]
*[addictedtor.free.fr, 2005]: Anonymous. Sun Flower Plot. R Graph Gallery. Changed at: October 6, 2005 Retrieved at: 2 November, 2008.
[http://addictedtor.free.fr/graphiques/graphcode.php?graph=59 http://addictedtor.free.fr/graphiques/graphcode.php?graph=59]

*[Friendly, 2006] Michael Friendly, The sunplot macro. York University. Changed at: November 2, 2006. Retrieved at: November 2, 2008
[http://www.math.yorku.ca/SCS/sasmac/sunplot.html http://www.math.yorku.ca/SCS/sasmac/sunplot.html]

*[Demsar, ] Janez Demsar, Simple Visualization Examples. A.I. Lab Ljubljana. Retrieved at: November 2, 2008
[http://www.ailab.si/janez/visualizations.html http://www.ailab.si/janez/visualizations.html]

=External Links=
*Java Applet: [http://matti.usu.edu/nlvm/nav/frames_asid_144_g_4_t_5.html http://matti.usu.edu/nlvm/nav/frames_asid_144_g_4_t_5.html]

File:PinPlot543.jpg

2008-11-02T21:49:32Z

UE-InfoVis0809 9607701: Scatterplot with thir axis. Created with Mathematica's PinPlot3D command. Taken from "library.wolfram.com".

== Summary ==
Scatterplot with thir axis. Created with Mathematica's PinPlot3D command. Taken from "library.wolfram.com".
== Copyright status ==

== Source ==

File:ClustersOutlyers.jpg

2008-11-02T21:47:05Z

UE-InfoVis0809 9607701: Scatterplot with two recognizable clusters and outlyers.

== Summary ==
Scatterplot with two recognizable clusters and outlyers.
== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Scatterplot

2008-11-02T21:02:35Z

UE-InfoVis0809 9607701:

A scatterplot (also called a ''scatter chart'', ''scatter diagram'' or ''scatter graph'' [Wikipedia]) is a diagram in which the values of two ''metric'' variables are applied to the horizontal and vertical axes of a cartesian coordinate system. The resulting point in the graph represents one record from a data set. The distribution pattern of points from multiple records reveals, among other qualities, the correlation between the selected variables in the data set. The scatterplot is not to be confused with the ''correlation plot'' [Information Technology Lab, NIST #2] which treats already adopted correlation coefficients in different data groups, while the term ''correlation diagram'' does not seem to be bound.

===Revealed Information===

Perfect linear correlation results in all samples lying on the regression line with positive or negative incline dependent on the sign of the correlation coefficient [University of Illinois]. Note, that the nonzero incline of the line is insignificant in this kind of diagram [Wikipedia Correlation, EN] since it is dependent on axis scales.

An example of perfect correlation can be seen on the right together with other patterns: strong positive, weak negative and one example of variables without significant correlation.

[[Image:SomeScatterplots.jpg|right|200px|thumb|Some scatterplots.]]

The plot below features a regression line to further increase expressiveness. The regression function is not necessarily chosen linear as in this example. Any kind of curve may fit a plot (quadratic, splines, ...). Generally, the curve with the smallest sum of squared distances to the points is sought after, [NetMBA].

[Wikipedia Linear Regression]

[[Image:WeakNegativeCorrelationLine.jpg|right|200px|thumb|Regression line.]]

Generally: refer to regression analysis for further ...

Further properties of data sets that are easily discovered are the presence of clusters and outlyers.

density (-> cluster analysis) & outlyers

*1 image for clusters and outlyers

===Scatterplots of Higher Dimensions===

Scatterplots are not restricted to records with only two variables. Higher dimensional data can be displayed by adding the third axis to the plotspacially or by point properties (color, size, shape)

TODO: add figure with colored 3D plot,

[Wikipedia, EN]

Nice example of plotting multidimensional data: [AI Lab]

===Treating Discrete Data===

For continuously distributed data, scatterplots do well in visualizing density. The problem with discrete data is the possibility of more than one record sharing one point in the diagram (''overplotting''). One solution is to alter the point representation according to density, as is achieved by ''sun flower plots'' in which each point symbol gains radial segments as a consequence, [Wikipedia, DE]. Examples can be found here: [York University], [addictedtor.free.fr].

=References=

*Wikipedia, EN: http://en.wikipedia.org/wiki/Scatterplot
*Wikipedia, DE: http://de.wikipedia.org/wiki/Streudiagramm
*Wikipedia Correlation, EN: http://en.wikipedia.org/wiki/Correlation
*Wikipedia Linear Regression, EN:http://en.wikipedia.org/wiki/Linear_regression
*University of Illinois: http://www.mste.uiuc.edu/courses/ci330ms/youtsey/scatterinfo.html
*Information Technology Lab, NIST #1: http://www.itl.nist.gov/div898/handbook/eda/section3/eda33q.htm
*Information Technology Lab, NIST #2: http://www.itl.nist.gov/div898/handbook/eda/section3/linecorr.htm
*NetMBA: http://www.netmba.com/statistics/plot/scatter/
*ChartItNow: http://www.chartitnow.com/scatter%20diagram.html

*addictedtor.free.fr: http://addictedtor.free.fr/graphiques/graphcode.php?graph=59
*York University: http://www.math.yorku.ca/SCS/sasmac/sunplot.html
*NLVM: http://matti.usu.edu/nlvm/nav/frames_asid_144_g_4_t_5.html

*AI Lab: www.ailab.si/janez/visualizations.html

File:SomeScatterplots.jpg

2008-11-02T11:28:27Z

UE-InfoVis0809 9607701: Some scatterplots: perfect positive, strong positivve, weak negative and one with no corellation among variables.

== Summary ==
Some scatterplots: perfect positive, strong positivve, weak negative and one with no corellation among variables.
== Copyright status ==

== Source ==

File:WeakNegativeCorrelationLine.jpg

2008-11-02T11:17:49Z

UE-InfoVis0809 9607701: Scatterplot of two variables with weak negative correlation, featuring regression line.

== Summary ==
Scatterplot of two variables with weak negative correlation, featuring regression line.
== Copyright status ==

== Source ==

File:WeakNegativeCorrelation.jpg

2008-11-02T11:16:44Z

UE-InfoVis0809 9607701: Scatterplot of two variables with weak negative correlation.

== Summary ==
Scatterplot of two variables with weak negative correlation.
== Copyright status ==

== Source ==

File:StrongPositiveCorrelation.jpg

2008-11-02T11:16:06Z

UE-InfoVis0809 9607701: Scatterplot of two variables with strong positive correlation.

== Summary ==
Scatterplot of two variables with strong positive correlation.
== Copyright status ==

== Source ==

File:PerfectPositiveCorrelation.jpg

2008-11-02T11:15:32Z

UE-InfoVis0809 9607701: Scatterplot of two variables with perfectly positive correlation.

== Summary ==
Scatterplot of two variables with perfectly positive correlation.
== Copyright status ==

== Source ==

File:NoCorrelation.jpg

2008-11-02T11:14:17Z

UE-InfoVis0809 9607701: Scatterplot of uncorrelated variables.

== Summary ==
Scatterplot of uncorrelated variables.
== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Scatterplot

2008-11-01T22:42:40Z

UE-InfoVis0809 9607701:

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Scatterplot

2008-11-01T19:05:07Z

UE-InfoVis0809 9607701:

A scatterplot (also called a ''scatter chart'', ''scatter diagram'' or ''scatter graph'' [Wikipedia]) is a diagram in which the values of two variables are applied to the horizontal and vertical axes of a cartesian coordinate system. The resulting point in the graph represents one record from a data set. The distribution pattern of points from multiple records reveals the correlation among the selected variables in the data set. The scatterplot is not to be confused with the ''correlation plot'' [Information Technology Lab, NIST #2] which treats already adopted correlation coefficients in different data groups, while the term ''correlation diagram'' does not seem to be bound.

=Revealed Information=

==Type of Correlation==
correlation patterns -> type of correlation
(regression function, "scatterplot smoothing" [NetMBA])
sign, strength (TODO: add about figures with: perfect positive, strong tight negative, weak loose positive, no correlation, clusters)
4 figures from [University of Illinois]:
*perfect positive
*high negative
*low positive
*no correlation

1 screen from scatterplot tool [NLVM] with regression line

==Density, Outlyers and Clusters==
density (-> cluster analysis) & outlyers

*1 image for clusters
*1 with outlyer

=Scatterplots of Higher Dimensions=

Not necessarily two variables, higher dimensions displayed spacially or by point properties (color, size, shape)

TODO: add figure with colored 3D plot, sunflower plot [addictedtor.free.fr], [York University], jitter plot

=Treating Discrete Data=

[Wikipedia, DE]

=References=

*Wikipedia, EN: http://en.wikipedia.org/wiki/Scatterplot
*Wikipedia, DE: http://de.wikipedia.org/wiki/Streudiagramm
*University of Illinois: http://www.mste.uiuc.edu/courses/ci330ms/youtsey/scatterinfo.html
*Information Technology Lab, NIST #1: http://www.itl.nist.gov/div898/handbook/eda/section3/eda33q.htm
*Information Technology Lab, NIST #2: http://www.itl.nist.gov/div898/handbook/eda/section3/linecorr.htm
*NetMBA: http://www.netmba.com/statistics/plot/scatter/
*ChartItNow: http://www.chartitnow.com/scatter%20diagram.html
*addictedtor.free.fr: http://addictedtor.free.fr/graphiques/graphcode.php?graph=59
*York University: http://www.math.yorku.ca/SCS/sasmac/sunplot.html
*NLVM: http://matti.usu.edu/nlvm/nav/frames_asid_144_g_4_t_5.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Scatterplot

2008-10-30T16:37:13Z

UE-InfoVis0809 9607701:

A scatterplot (also called a ''scatter chart'', ''scatter diagram'' or ''scatter graph'' [Wikipedia]) is a diagram in which the values of two variables are applied to the horizontal and vertical axes of a cartesian coordinate system. The resulting point in the graph represents one record from a data set. The distribution pattern of points from multiple records reveals the correlation among the selected variables in the data set. The scatterplot is not to be confused with the ''correlation plot'' [Information Technology Lab, NIST #2] which treats already adopted correlation coefficients in different data groups, while the term ''correlation diagram'' does not seem to be bound.

=Revealed Information=

==Type of Correlation==
correlation patterns -> type of correlation
(regression function, "scatterplot smoothing" [NetMBA])
sign, strength (TODO: add about figures with: perfect positive, strong tight negative, weak loose positive, no correlation, clusters)

==Density, Outlyers and Clusters==
density (-> cluster analysis) & outlyers

=Scatterplots of Higher Dimensions=

Not necessarily two variables, higher dimensions displayed spacially or by point properties (color, size, shape)

TODO: add figure with colored 3D plot, sunflower plot [addictedtor.free.fr], [York University], jitter plot

=Treating Discrete Data=

[Wikipedia, DE]

=References=

*Wikipedia, EN: http://en.wikipedia.org/wiki/Scatterplot
*Wikipedia, DE: http://de.wikipedia.org/wiki/Streudiagramm
*University of Illinois: http://www.mste.uiuc.edu/courses/ci330ms/youtsey/scatterinfo.html
*Information Technology Lab, NIST #1: http://www.itl.nist.gov/div898/handbook/eda/section3/eda33q.htm
*Information Technology Lab, NIST #2: http://www.itl.nist.gov/div898/handbook/eda/section3/linecorr.htm
*NetMBA: http://www.netmba.com/statistics/plot/scatter/
*ChartItNow: http://www.chartitnow.com/scatter%20diagram.html
*addictedtor.free.fr: http://addictedtor.free.fr/graphiques/graphcode.php?graph=59
*York University: http://www.math.yorku.ca/SCS/sasmac/sunplot.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Scatterplot

2008-10-30T16:16:20Z

UE-InfoVis0809 9607701:

A scatterplot (also called a ''scatter chart'', ''scatter diagram'' or ''scatter graph'' [Wikipedia]) is a diagram in which the values of two variables are applied to the horizontal and vertical axes of a cartesian coordinate system. The resulting point in the graph represents one record from a data set. The distribution pattern of points from multiple records reveal the correlation between the selected variables in the data set. The scatterplot is not to be confused with the ''correlation plot'' [Information Technology Lab, NIST #2] which treats already adopted correlation coefficients of different data groups, while the term ''correlation diagram'' does not seem to be bound.

=Revealed Information=

correlation patterns -> type of correlation
(regression line, regression "path", "scatterplot smoothing" [NetMBA])
sign, strength (TODO: add about figures with: perfect positive, strong tight negative, weak loose positive, no correlation, clusters)

density (-> cluster analysis) & outlyers

=Scatterplots of higher dimensions=

Not necessarily two variables, higher dimensions displayed spacially or by point properties (color, size, shape)

=Treating Discrete Data=

[Wikipedia, DE]

=References=

*Wikipedia, EN: http://en.wikipedia.org/wiki/Scatterplot
*Wikipedia, DE: http://de.wikipedia.org/wiki/Streudiagramm
*University of Illinois: http://www.mste.uiuc.edu/courses/ci330ms/youtsey/scatterinfo.html
*Information Technology Lab, NIST #1: http://www.itl.nist.gov/div898/handbook/eda/section3/eda33q.htm
*Information Technology Lab, NIST #2: http://www.itl.nist.gov/div898/handbook/eda/section3/linecorr.htm
*NetMBA: http://www.netmba.com/statistics/plot/scatter/
*ChartItNow: http://www.chartitnow.com/scatter%20diagram.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Scatterplot

2008-10-30T16:07:25Z

UE-InfoVis0809 9607701:

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Scatterplot

2008-10-30T15:35:40Z

UE-InfoVis0809 9607701:

=Scatterplot=

A scatterplot (also called a ''scatter chart'', ''scatter diagram'' and ''scatter graph''[Wikipedia]) is a diagram in which the values of two variables are applied to the horizontal and vertical axes of a cartesian coordinate system. The resulting point in the graph represents one record from a data set. The distribution pattern of points from multiple records reveal the correlation between the selected variables in the data set. The scatterplot is not to be confused with the ''correlation plot'' (http://www.itl.nist.gov/div898/handbook/eda/section3/linecorr.htm) or ''correlation diagram'' which treat already determined correlation coefficients.

Revealed Information

outlyers

correlation patterns

regression line

*Wikipedia: http://en.wikipedia.org/wiki/Scatterplot
*University of Illinois: http://www.mste.uiuc.edu/courses/ci330ms/youtsey/scatterinfo.html
*Information Technology Lab, NIST: http://www.itl.nist.gov/div898/handbook/eda/section3/eda33q.htm
*NetMBA: http://www.netmba.com/statistics/plot/scatter/
*ChartItNow: http://www.chartitnow.com/scatter%20diagram.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Scatterplot

2008-10-30T14:22:04Z

UE-InfoVis0809 9607701:

correlation, but NOT "correlation plot" (see: https://www.geoda.uiuc.edu/support/help/explore_brush.html)
outlyers
regression line

Wikipedia: http://en.wikipedia.org/wiki/Scatterplot
University of Illinois: http://www.mste.uiuc.edu/courses/ci330ms/youtsey/scatterinfo.html
Information Technology Lab, NIST: http://www.itl.nist.gov/div898/handbook/eda/section3/eda33q.htm
NetMBA: http://www.netmba.com/statistics/plot/scatter/
ChartItNow: http://www.chartitnow.com/scatter%20diagram.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Scatterplot

2008-10-30T14:17:11Z

UE-InfoVis0809 9607701: New page: correlation outlyers regression line Wikipedia: http://en.wikipedia.org/wiki/Scatterplot University of Illinois: http://www.mste.uiuc.edu/courses/ci330ms/youtsey/scatterinfo.html I...

correlation
outlyers
regression line

Wikipedia: http://en.wikipedia.org/wiki/Scatterplot
University of Illinois: http://www.mste.uiuc.edu/courses/ci330ms/youtsey/scatterinfo.html
Information Technology Lab, NIST: http://www.itl.nist.gov/div898/handbook/eda/section3/eda33q.htm
NetMBA: http://www.netmba.com/statistics/plot/scatter/
ChartItNow: http://www.chartitnow.com/scatter%20diagram.html

User:UE-InfoVis0809 9607701

2008-10-17T15:46:16Z

UE-InfoVis0809 9607701:

'''Axel Goldmann'''

[[Image:Automat05.jpg|right|100px|thumb|Portrait]]
 
*Matrikelnummer: 9607701
*Studienkennzahl: 535
*Mail: e9607701(a)student.tuwien.ac.at

User:UE-InfoVis0809 9607701

2008-10-17T15:42:56Z

UE-InfoVis0809 9607701: New page: =User: ~~~= '''Axel Goldmann''' Portrait *Matrikelnummer: 9607701 *Studienkennzahl: 535 *Mail: e9607701(a)student.tuwien.ac.at

File:Automat05.jpg

2008-10-17T15:28:06Z

UE-InfoVis0809 9607701: New page: == Summary == == Copyright status == == Source ==