InfoVis:Wiki - User contributions [en]

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3

2011-01-17T20:15:14Z

UE-InfoVis1011 0526223:

== Interactive visualization ==

=== Introduction ===
Based on a report [1] of the Unicef, who tries to make a greater access to dinking-water and sanitation for humans all over the world, we made a visualization, which presents the access to it in addiction to the number of the population. The maintenance of the urban population is far better developed, in comparison to the rural population. The main reason therefore is that the rural population has harder and bigger geographic conditions.

884 Million people do not use improved sources of drinking - water.
2.6 Billion people do not use improved sanitation, just 61 percent of human civilization does it.
With this in mind, we tried to show a analysis of the all regions over the world, which we have the information of.

This link leads you to the representation of the dataset.
*[http://keine_Ahnung_was - interactive visualization (LINK !??? )]

=== Analysis of the dataset ===
The dataset[2] is continuous over the time from 1990 to 2008.
In an interval of every 5th year, in each country have been made entries of the (un-) improved sources of drinking water, the sanitation and the people who gained access to it. But not all datasets of all countries are filled in the dataset, so we took just the completed ones.

The application area is the progress on all datasets, which shows us a multidimensional datastructure.

* multivariate
* temporal
* numeric
* hierarchies
* countries < continents
* all data (totals)
** urban
** rural
** water
*** improved (totals)
**** piped
**** other improved
** sanitation
*** unimproved (totals)
**** open defecation
**** shared
**** other

fehlt hier noch etwas? diese auflistung scheint etwas komisch !? 

=== Analysis of the users ===
We declare the experts of preparing the sources of drinking water as the target audience of the visualization.
But users, doctors and inhabitants of the regions are also able to look and understand this graphic.
The shown dataset might be a reference for medical supporters, to know where the derivation of the complaints comes from.
Our diagram might let you find out the coherence between different variables and dependence on time.

=== Analysis of the tasks ===
What do we want to reach with our visualization?
We want to show the community the circumstances of the poor civilization.
Not just a picture of a point, but all information over the globe in a diagram where you can easily read the information from and compare with other countries. You can see on the first sight, which countries need more assistance to reach better results.
Different questions can be saved, like:
Are there any dependencies, which affect different countries?
Which continent has more access?
How much has it been grown in the last few years (max. to 1990)?

=== Visualization design ===
We chose the Job Voyager[3] as a design output, to represent the stacked time series of the reported sampled data.
The data which should be shown can be filtered by population and its attribute.
A characteristic of the area under the drawn line in the chart is, that it shows us the bigger it is the bigger is it's value.
So you are able to see the countries in the chart, which have better prospects.
Furthermore the user has a interaction with the visualization by moving his mouse over the chart.
So the user has the facility to choose the continent bye mouse-over and look at it's countries.
In the left upper corner of the site, the actual name of the country gets shown, by moving the mouse over the area of this country.
The measured values of the different years are not shown just on the y-axis, but also on every point of time in the chart.

[[Image:screenshot_job_voyager.png|thumb|300px|left|Figure 1: visualization]]

advantages
* the ability to extend the chart
* main information on one site
* fluently workflow
* dependencies on different variables
* big dataset, as a smart visualization

disatvantages
* big numbers, hard to represent
* many countries in one chart confuses sometimes
* details hard to read
* sometimes difficult to find a specific country

=== Conclusion ===

The chosen visualization focuses on time oriented data. Main focus was to show the development of the specific attributes over time.
Overall the visualization satisfies this purpose in an easy readable way.

=== References ===
:[1] [Unicef, 2010] Unicef. Progress on Sanitation and drinking-water: 2010 Update. ''JMP report - World Health Organization and UNICEF 2010'', 1–55, 2010.

:[2] [WHO, Unicef, 2003-2010] WHO - UNICEF. Protovis: Joint Monitoring Programme (JMP) for Water Supply and Sanitation. Created at: 1990. Retrieved at: January 15, 2011. http://www.wssinfo.org/data-estimates/table/.

:[3] [Bostock, Heer, 2009] Michael Bostock and Jeffrey Heer. Protovis: A graphical tool for visualization. Created at: March 31, 2009. Retrieved at: January 15, 2011. http://vis.stanford.edu/protovis/ex/jobs.html.

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3

2011-01-17T20:08:53Z

UE-InfoVis1011 0526223:

== Interactive visualization ==

=== Introduction ===
Based on a report [1] of the Unicef, who tries to make a greater access to dinking-water and sanitation for humans all over the world, we made a visualization, which presents the access to it in addiction to the number of the population. The maintenance of the urban population is far better developed, in comparison to the rural population. The main reason therefore is that the rural population has harder and bigger geographic conditions.

884 Million people do not use improved sources of drinking - water.
2.6 Billion people do not use improved sanitation, just 61 percent of human civilization does it.
With this in mind, we tried to show a analysis of the all regions over the world, which we have the information of.

This link leads you to the representation of the dataset.
*[http://keine_Ahnung_was - interactive visualization (LINK !??? )]

=== Analysis of the dataset ===
The dataset[2] is continuous over the time from 1990 to 2008.
In an interval of every 5th year, in each country have been made entries of the (un-) improved sources of drinking water, the sanitation and the people who gained access to it. But not all datasets of all countries are filled in the dataset, so we took just the completed ones.

The application area is the progress on all datasets, which shows us a multidimensional datastructure.

* multivariate
* temporal
* numeric
* hierarchies
* countries < continents
* all data (totals)
** urban
** rural
** water
*** improved (totals)
**** piped
**** other improved
** sanitation
*** unimproved (totals)
**** open defecation
**** shared
**** other

fehlt hier noch etwas? diese auflistung scheint etwas komisch !? 

=== Analysis of the users ===
We declare the experts of preparing the sources of drinking water as the target audience of the visualization.
But users, doctors and inhabitants of the regions are also able to look and understand this graphic.
The shown dataset might be a reference for medical supporters, to know where the derivation of the complaints comes from.
Our diagram might let you find out the coherence between different variables and dependence on time.

=== Analysis of the tasks ===
What do we want to reach with our visualization?
We want to show the community the circumstances of the poor civilization.
Not just a picture of a point, but all information over the globe in a diagram where you can easily read the information from and compare with other countries. You can see on the first sight, which countries need more assistance to reach better results.
Different questions can be saved, like:
Are there any dependencies, which affect different countries?
Which continent has more access?
How much has it been grown in the last few years (max. to 1990)?

=== Visualization design ===
We chose the Job Voyager[3] as a design output, to represent the stacked time series of the reported sampled data.
The data which should be shown can be filtered by population and its attribute.
A characteristic of the area under the drawn line in the chart is, that it shows us the bigger it is the bigger is it's value.
So you are able to see the countries in the chart, which have better prospects.
Furthermore the user has a interaction with the visualization by moving his mouse over the chart.
So the user has the facility to choose the continent bye mouse-over and look at it's countries.
In the left upper corner of the site, the actual name of the country gets shown, by moving the mouse over the area of this country.
The measured values of the different years are not shown just on the y-axis, but also on every point of time in the chart.

[[Image:screenshot_job_voyager.png|thumb|300px|left|Figure 1: visualization]]

advantages
* the ability to extend the chart
* main information on one site
* fluently workflow
* dependencies on different variables
* big dataset, as a smart visualization

disatvantages
* big numbers, hard to represent
* many countries in one chart confuses sometimes
* details hard to read
* sometimes difficult to find a specific country

=== Conclusion ===

The chosen visualization focuses on time oriented data. Main focus was to show the development of the specific attributes over time.
Overall the visualization satisfies this purpose in an easy readable way.

=== References ===
:[1] [Unicef, 2010] Unicef. Progress on Sanitation and drinking-water: 2010 Update. '' World Health Organization and UNICEF 2010'', 1–55, 2010.

:[2] [WHO, Unicef, 2003-2010] WHO - UNICEF. Protovis: Joint Monitoring Programme (JMP) for Water Supply and Sanitation. Created at: 1990. Retrieved at: January 15, 2011. http://www.wssinfo.org/data-estimates/table/.

:[3] [Bostock, Heer, 2009] Michael Bostock and Jeffrey Heer. Protovis: A graphical tool for visualization. Created at: March 31, 2009. Retrieved at: January 15, 2011. http://vis.stanford.edu/protovis/ex/jobs.html.

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3

2011-01-17T18:31:47Z

UE-InfoVis1011 0526223:

== Interactive visualization ==

=== Introduction ===
Based on a report [1] of the Unicef, who tries to make a greater access to dinking-water and sanitation for humans all over the world, we made a visualization, which presents the access to it in addiction to the number of the population. The maintenance of the urban population is far better developed, in comparison to the rural population. The main reason therefore is that the rural population has harder and bigger geographic conditions.

884 Million people do not use improved sources of drinking - water.
2.6 Billion people do not use improved sanitation, just 61 percent of human civilization does it.
With this in mind, we tried to show a analysis of the all regions over the world, which we have the information of.

This link leads you to the representation of the dataset.
*[http://keine_Ahnung_was - interactive visualization (LINK !??? )]

=== Analysis of the dataset ===
The dataset[2] is continuous over the time from 1990 to 2008.
In an interval of every 5th year, in each country have been made entries of the (un-) improved sources of drinking water, the sanitation and the people who gained access to it. But not all datasets of all countries are filled in the dataset, so we took just the completed ones.

The application area is the progress on all datasets, which shows us a multidimensional datastructure.

* multivariate
* temporal
* numeric
* hierarchies
* countries < continents
* all data (totals)
** urban
** rural
** water
*** improved (totals)
**** piped
**** other improved
** sanitation
*** unimproved (totals)
**** open defecation
**** shared
**** other

fehlt hier noch etwas? diese auflistung scheint etwas komisch !? 

=== Analysis of the users ===
We declare the experts of preparing the sources of drinking water as the target audience of the visualization.
But users, doctors and inhabitants of the regions are also able to look and understand this graphic.
The shown dataset might be a reference for medical supporters, to know where the derivation of the complaints comes from.
Our diagram might let you find out the coherence between different variables and dependence on time.

=== Analysis of the tasks ===
What do we want to reach with our visualization?
We want to show the community the circumstances of the poor civilization.
Not just a picture of a point, but all information over the globe in a diagram where you can easily read the information from and compare with other countries. You can see on the first sight, which countries need more assistance to reach better results.
Different questions can be saved, like:
Are there any dependencies, which affect different countries?
Which continent has more access?
How much has it been grown in the last few years (max. to 1990)?

=== Visualization design ===
We chose the Job Voyager[3] as a design output, to represent the stacked time series of the reported sampled data.
The data which should be shown can be filtered by population and its attribute.
A characteristic of the area under the drawn line in the chart is, that it shows us the bigger it is the bigger is it's value.
So you are able to see the countries in the chart, which have better prospects.
Furthermore the user has a interaction with the visualization by moving his mouse over the chart.
So the user has the facility to choose the continent bye mouse-over and look at it's countries.
In the left upper corner of the site, the actual name of the country gets shown, by moving the mouse over the area of this country.
The measured values of the different years are not shown just on the y-axis, but also on every point of time in the chart.

[[Image:screenshot_job_voyager.png|thumb|300px|left|Figure 1: visualization]]

advantages
* the ability to extend the chart
* main information on one site
* fluently workflow
* dependencies on different variables
* big dataset, as a smart visualization

disatvantages
* big numbers, hard to represent
* many countries in one chart confuses sometimes
* details hard to read
* sometimes difficult to find a specific country

=== Conclusion ===

The chosen visualization focuses on time oriented data. Main focus was to show the development of the specific attributes over time.

ANY IDEAS ???
brauchen wir eventuell nicht ???? 

=== References ===
:[1] [Unicef, 2010] Unicef. Progress on Sanitation and drinking-water: 2010 Update. '' World Health Organization and UNICEF 2010'', 1–55, 2010.

:[2] [WHO, Unicef, 2003-2010] WHO - UNICEF. Protovis: Joint Monitoring Programme (JMP) for Water Supply and Sanitation. Created at: 1990. Retrieved at: January 15, 2011. http://www.wssinfo.org/data-estimates/table/.

:[3] [Bostock, Heer, 2009] Michael Bostock and Jeffrey Heer. Protovis: A graphical toll for visualization. Created at: March 31, 2009. Retrieved at: January 15, 2011. http://vis.stanford.edu/protovis/ex/jobs.html.

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3

2011-01-17T18:27:00Z

UE-InfoVis1011 0526223:

== Interactive visualization ==

=== Introduction ===
Based on a report [1] of the Unicef, who tries to make a greater access to dinking-water and sanitation for humans all over the world, we made a visualization, which presents the access to it in addiction to the number of the population. The maintenance of the urban population is far better developed, in comparison to the rural population. The main reason therefore is that the rural population has harder and bigger geographic conditions.

884 Million people do not use improved sources of drinking - water.
2.6 Billion people do not use improved sanitation, just 61 percent of human civilization does it.
With this in mind, we tried to show a analysis of the all regions over the world, which we have the information of.

This link leads you to the representation of the dataset.
*[http://keine_Ahnung_was - interactive visualization (LINK !??? )]

=== Analysis of the dataset ===
The dataset[2] is continuous over the time from 1990 to 2008.
In an interval of every 5th year, in each country have been made entries of the (un-) improved sources of drinking water, the sanitation and the people who gained access to it. But not all datasets of all countries are filled in the dataset, so we took just the completed ones.

The application area is the progress on all datasets, which leaves us to a multidimensional datastructure.

* multivariate
* temporal
* numeric
* hierarchies
* countries < continents
* all data (totals)
** urban
** rural
** water
*** improved (totals)
**** piped
**** other improved
** sanitation
*** unimproved (totals)
**** open defecation
**** shared
**** other

fehlt hier noch etwas? diese auflistung scheint etwas komisch !? 

=== Analysis of the users ===
We declare the experts of preparing the sources of drinking water as the target audience of the visualization.
But users, doctors and inhabitants of the regions are also able to look and understand this graphic.
The shown dataset might be a reference for medical supporters, to know where the derivation of the complaints comes from.
Our diagram might let you find out the coherence between different variables and dependence on time.

=== Analysis of the tasks ===
What do we want to reach with our visualization?
We want to show the community the circumstances of the poor civilization.
Not just a picture of a point, but all information over the globe in a diagram where you can easily read the information from and compare with other countries. You can see on the first sight, which countries need more assistance to reach better results.
Different questions can be saved, like:
Are there any dependencies, which affect different countries?
Which continent has more access?
How much has it been grown in the last few years (max. to 1990)?

=== Visualization design ===
We chose the Job Voyager[3] as a design output, to represent the stacked time series of the reported sampled data.
The data which should be shown can be filtered by population and its attribute.
A characteristic of the area under the drawn line in the chart is, that it shows us the bigger it is the bigger is it's value.
So you are able to see the countries in the chart, which have better prospects.
Furthermore the user has a interaction with the visualization by moving his mouse over the chart.
So the user has the facility to choose the continent bye mouse-over and look at it's countries.
In the left upper corner of the site, the actual name of the country gets shown, by moving the mouse over the area of this country.
The measured values of the different years are not shown just on the y-axis, but also on every point of time in the chart.

[[Image:screenshot_job_voyager.png|thumb|300px|left|Figure 1: visualization]]

advantages
* the ability to extend the chart
* main information on one site
* fluently workflow
* dependencies on different variables
* big dataset, as a smart visualization

disatvantages
* big numbers, hard to represent
* many countries in one chart confuses sometimes
* details hard to read
* sometimes difficult to find a specific country

=== Conclusion ===

The chosen visualization focuses on time oriented data. Main focus was to show the development of the specific attributes over time.

ANY IDEAS ???
brauchen wir eventuell nicht ???? 

=== References ===
:[1] [Unicef, 2010] Unicef. Progress on Sanitation and drinking-water: 2010 Update. '' World Health Organization and UNICEF 2010'', 1–55, 2010.

:[2] [WHO, Unicef, 2003-2010] WHO - UNICEF. Protovis: Joint Monitoring Programme (JMP) for Water Supply and Sanitation. Created at: 1990. Retrieved at: January 15, 2011. http://www.wssinfo.org/data-estimates/table/.

:[3] [Bostock, Heer, 2009] Michael Bostock and Jeffrey Heer. Protovis: A graphical toll for visualization. Created at: March 31, 2009. Retrieved at: January 15, 2011. http://vis.stanford.edu/protovis/ex/jobs.html.

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3

2011-01-17T10:26:57Z

UE-InfoVis1011 0526223:

== Interactive visualization ==

=== Introduction ===
Based on a report [1] of the Unicef, who tries to make a greater access to dinking-water and sanitation for humans all over the world, we made a visualization, which presents the access to it in addiction to the number of the population. The maintenance of the urban population is far better developed, in comparison to the rural population. The main reason therefore is that the rural population has harder and bigger geographic conditions.

884 Million people do not use improved sources of drinking - water.
2.6 Billion people do not use improved sanitation, just 61 percent of human civilization does it.
With this in mind, we tried to show a analysis of the all regions over the world, which we have the information of.

This link leads you to the representation of the dataset.
*[http://keine_Ahnung_was - interactive visualization (LINK !??? )]

=== Analysis of the dataset ===
The dataset is continuous over the time from 1990 to 2008.
In an interval of every 5th year, in each country have been made entries of the (un-) improved sources of drinking water, the sanitation and the people who gained access to it. But not all datasets of all countries are filled in the dataset, so we took just the completed ones.

The application area is the progress on all datasets, which leaves us to a multidimensional datastructure.

* multivariate
* temporal
* numeric
* hierarchies
* countries < continents
* all data (totals)
** urban
** rural
** water
*** improved (totals)
**** piped
**** other improved
** sanitation
*** unimproved (totals)
**** open defecation
**** shared
**** other

fehlt hier noch etwas? diese auflistung scheint etwas komisch !? 

=== Analysis of the users ===
We declare the experts of preparing the sources of drinking water as the target audience of the visualization.
But users, doctors and inhabitants of the regions are also able to look and understand this graphic.
The shown dataset might be a reference for medical supporters, to know where the derivation of the complaints comes from.
Our diagram might let you find out the coherence between different variables and dependence on time.

=== Analysis of the tasks ===
What do we want to reach with our visualization?
We want to show the community the circumstances of the poor civilization.
Not just a picture of a point, but all information over the globe in a diagram where you can easily read the information from and compare with other countries. You can see on the first sight, which countries need more assistance to reach better results.
Different questions can be saved, like:
Are there any dependencies, which affect different countries?
Which continent has more access?
How much has it been grown in the last few years (max. to 1990)?

=== Visualization design ===
We chose the Job Voyager[1] as a design output, to represent the stacked time series of the reported sampled data.
The data which should be shown can be filtered by population and its attribute.
A characteristic of the area under the drawn line in the chart is, that it shows us the bigger it is the bigger is it's value.
So you are able to see the countries in the chart, which have better prospects.
Furthermore the user has a interaction with the visualization by moving his mouse over the chart.
So the user has the facility to choose the continent bye mouse-over and look at it's countries.
In the left upper corner of the site, the actual name of the country gets shown, by moving the mouse over the area of this country.
The measured values of the different years are not shown just on the y-axis, but also on every point of time in the chart.

[[Image:screenshot_job_voyager.png|thumb|300px|left|Figure 1: visualization]]

advantages
* the ability to extend the chart
* main information on one site
* fluently workflow
* dependencies on different variables
* big dataset, as a smart visualization

disatvantages
* big numbers, hard to represent
* many countries in one chart confuses sometimes
* details hard to read
* sometimes difficult to find a specific country

=== Conclusion ===
ANY IDEAS ???
brauchen wir eventuell nicht ???? 

=== References ===
[1] report of unicef
[2] website of protovis

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3

2011-01-17T10:10:46Z

UE-InfoVis1011 0526223:

== Interactive visualization ==

=== Introduction ===
Based on a report [1] of the Unicef, who tries to make a greater access to dinking-water and sanitation for humans all over the world, we made a visualization, which presents the access to it, addiction to the number of the population. The maintenance of the urban population is far better developed, in comparison to the rural population. The main reason therefore is that the rural population has harder and bigger geographic conditions.

884 Million people do not use improved sources of drinking - water.
2.6 Billion people do not use improved sanitation, just 61 percent of human civilization does it.
With this in mind, we tried to show a analysis of the all regions over the world, which we have the information of.

This link leads you to the representation of the dataset.
*[http://keine_Ahnung_was - interactive visualization (LINK !??? )]

=== Analysis of the dataset ===
The dataset is continuous over the time from 1990 to 2008.
In an interval of every 5th year, in each country have been made entries of the (un-) improved sources of drinking water, the sanitation and the people who gained access to it. But not all datasets of all countries filled in, we just take the completed.

The application area is the progress on all datasets, which leaves us to a multidimensional datastructure.

* multivariate
* temporal
* numeric
* hierarchies
* countries < continents
* all data (totals)
** urban
** rural
** water
*** improved (totals)
**** piped
**** other improved
** sanitation
*** unimproved (totals)
**** open defecation
**** shared
**** other

fehlt hier noch etwas? diese auflistung scheint etwas komisch !? 

=== Analysis of the users ===
We declare the experts of preparing the sources of drinking water as the target audience of the visualization.
But users, doctors and inhabitants of the regions are also able to look and understand this graphic.
The shown dataset might be a reference for medical supporters, to know where the derivation of the complaints comes from.
Our diagram might let you find out the coherence between different variables and dependence on time.

=== Analysis of the tasks ===
What do we want to reach with our visualization?
We want to show the community the circumstances of the poor civilization.
Not just a picture of a point, but all information over the globe in a diagram where you can easily read the information of and compare to other countries. You can see on first see, which countries need more assistance to reach better results.
Different questions can be saved, like:
Are there any dependencies, which affect different countries?
Which continent has more access?

=== Visualization design ===
We choose the Job Voyager[1] as a design output to represent the stacked time series of the reported sampled data.
The data which should be shown, can be filtered by two different filters of population and its attribute.
A characteristic of the area under the drawn line in the chart is, that it shows us the bigger it is the bigger is it's value.
So you are able to see in the overview of the countries which have better prospects.
Furthermore the user has a interaction with the visualization by moving his mouse over the chart.
So the user has the facility to choose the continent bye mouse-over and look at it's countries.
In the left upper corner of the site, the actual name of the country gets shown, by moving the mouse over the area of this country in the chart.

[[Image:screenshot_job_voyager.png|thumb|300px|left|Figure 1: visualization]]

advantages
* the ability to extend the chart
* main information on one site
* fluently workflow
* dependencies on different variables
* big dataset, as a smart visualization

disatvantages
* big numbers, hard to represent
* many countries in one chart confuses sometimes
* details hard to read
* sometimes difficult to find a specific country

=== Conclusion ===
ANY IDEAS ???
brauchen wir eventuell nicht ???? 

=== References ===
[1] report of unicef
[2] website of protovis

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3

2011-01-17T10:09:39Z

UE-InfoVis1011 0526223:

== Interactive visualization ==

=== Introduction ===
Based on a report [1] of the Unicef, who tries to make a greater access to dinking-water and sanitation for humans all over the world, we made a visualization, which presents the access to it, addiction to the number of the population. The maintenance of the urban population is far better developed, in comparison to the rural population. The main reason therefore is that the rural population has harder and bigger geographic conditions.

884 Million people do not use improved sources of drinking - water.
2.6 Billion people do not use improved sanitation, just 61 percent of human civilization does it.
With this in mind, we tried to show a analysis of the all regions over the world, which we have the information of.

This link leads you to the representation of the dataset.
*[http://keine_Ahnung_was - interactive visualization (LINK !??? )]

=== Analysis of the dataset ===
The dataset is continuous over the time from 1990 to 2008.
In an interval of every 5th year, in each country have been made entries of the (un-) improved sources of drinking water, the sanitation and the people who gained access to it. But not all datasets of all countries filled in, we just take the completed.

The application area is the progress on all datasets, which leaves us to a multidimensional datastructure.

* multivariate
* temporal
* numeric
* hierarchies
* countries < continents
* all data (totals)
** urban
** rural
** water
*** improved (totals)
**** piped
**** other improved
** sanitation
*** unimproved (totals)
**** open defecation
**** shared
**** other

fehlt hier noch etwas? diese auflistung scheint etwas komisch !? 

=== Analysis of the users ===
We declare the experts of preparing the sources of drinking water as the target audience of the visualization.
But users, doctors and inhabitants of the regions are also able to look and understand this graphic.
The shown dataset might be a reference for medical supporters, to know where the derivation of the complaints comes from.
Our diagram might let you find out the coherence between different variables and dependence on time.

=== Analysis of the tasks ===
What do we want to reach with our visualization?
We want to show the community the circumstances of the poor civilization.
Not just a picture of a point, but all information over the globe in a diagram where you can easily read the information of and compare to other countries. You can see on first see, which countries need more assistance to reach better results.
Different questions can be saved, like:
Are there any dependencies, which affect different countries?
Which continent has more access?

=== Visualization design ===
We choose the Job Voyager[1] as a design output to represent the stacked time series of the reported sampled data.
The data which should be shown, can be filtered by two different filters of population and its attribute.
A characteristic of the area under the drawn line in the chart is, that it shows us the bigger it is the bigger is it's value.
So you are able to see in the overview of the countries which have better prospects.
Furthermore the user has a interaction with the visualization by moving his mouse over the chart.
So the user has the facility to choose the continent bye mouse-over and look at it's countries.
In the left upper corner of the site, the actual name of the country gets shown, by moving the mouse over the area of this country in the chart.

[[Image:screenshot_job_voyager.png|thumb|300px|left|Figure 1: visualization]]

advantages
* the ability to extend the chart
* main information on one site
* fluently workflow
* dependencies on different variables
* big dataset, as a smart visualization

disatvantages
* big numbers, hard to represent
* many countries in one chart confuses sometimes
* details hard to read
* sometimes difficult to find a specific country

=== Conclusion ===
brauchen wir nicht, oder will wer unbedingt was reintun ???? 

=== References ===
[1] report of unicef
[2] website of protovis

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3

2011-01-17T10:07:35Z

UE-InfoVis1011 0526223:

== Interactive visualization ==

=== Introduction ===
Based on a report [1] of the Unicef, who tries to make a greater access to dinking-water and sanitation for humans all over the world, we made a visualization, which presents the access to it, addiction to the number of the population. The maintenance of the urban population is far better developed, in comparison to the rural population. The main reason therefore is that the rural population has harder and bigger geographic conditions.

884 Million people do not use improved sources of drinking - water.
2.6 Billion people do not use improved sanitation, just 61 percent of human civilization does it.
With this in mind, we tried to show a analysis of the all regions over the world, which we have the information of.

This link leads you to the representation of the dataset.
*[http://keine_Ahnung_was - interactive visualization (LINK !??? )]

=== Analysis of the dataset ===
The dataset is continuous over the time from 1990 to 2008.
In an interval of every 5th year, in each country have been made entries of the (un-) improved sources of drinking water, the sanitation and the people who gained access to it. But not all datasets of all countries filled in, we just take the completed.

The application area is the progress on all datasets, which leaves us to a multidimensional datastructure.

* multivariate
* temporal
* numeric
* hierarchies
* countries < continents
* all data (totals)
** urban
** rural
** water
*** improved (totals)
**** piped
**** other improved
** sanitation
*** unimproved (totals)
**** open defecation
**** shared
**** other

fehlt hier noch etwas? diese auflistung scheint etwas komisch !? 

=== Analysis of the users ===
We declare the experts of preparing the sources of drinking water as the target audience of the visualization.
But users, doctors and inhabitants of the regions are also able to look and understand this graphic.
The shown dataset might be a reference for medical supporters, to know where the derivation of the complaints comes from.
Our diagram might let you find out the coherence between different variables and dependence on time.

=== Analysis of the tasks ===
What do we want to reach with our visualization?
We want to show the community the circumstances of the poor civilization.
Not just a picture of a point, but all information over the globe in a diagram where you can easily read the information of and compare to other countries. You can see on first see, which countries need more assistance to reach better results.
Different questions can be saved, like:
Are there any dependencies, which affect different countries?
Which continent has more access?

=== Visualization design ===
We choose the Job Voyager[1] as a design output to represent the stacked time series of the reported sampled data.
The data which should be shown, can be filtered by two different filters of population and its attribute.
A characteristic of the area under the drawn line in the chart is, that it shows us the bigger it is the bigger is it's value.
So you are able to see in the overview of the countries which have better prospects.
Furthermore the user has a interaction with the visualization by moving his mouse over the chart.
So the user has the facility to choose the continent bye mouse-over and look at it's countries.
In the left upper corner of the site, the actual name of the country gets shown, by moving the mouse over the area of this country in the chart.

[[Image:screenshot_job_voyager.png|thumb|300px|left|Figure 1: visualization, see [2]]]

advantages
* the ability to extend the chart
* main information on one site
* fluently workflow
* dependencies on different variables
* big dataset, as a smart visualization

disatvantages
* big numbers, hard to represent
* many countries in one chart confuses sometimes
* details hard to read
* sometimes difficult to find a specific country

=== Conclusion ===
brauchen wir nicht, oder will wer unbedingt was reintun ???? 

=== References ===
[1] report of unicef
[2] website of protovis

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3

2011-01-17T10:06:26Z

UE-InfoVis1011 0526223:

== Interactive visualization ==

=== Introduction ===
Based on a report [1] of the Unicef, who tries to make a greater access to dinking-water and sanitation for humans all over the world, we made a visualization, which presents the access to it, addiction to the number of the population. The maintenance of the urban population is far better developed, in comparison to the rural population. The main reason therefore is that the rural population has harder and bigger geographic conditions.

884 Million people do not use improved sources of drinking - water.
2.6 Billion people do not use improved sanitation, just 61 percent of human civilization does it.
With this in mind, we tried to show a analysis of the all regions over the world, which we have the information of.

This link leads you to the representation of the dataset.
*[http://keine_Ahnung_was - interactive visualization (LINK !??? )]

=== Analysis of the dataset ===
The dataset is continuous over the time from 1990 to 2008.
In an interval of every 5th year, in each country have been made entries of the (un-) improved sources of drinking water, the sanitation and the people who gained access to it. But not all datasets of all countries filled in, we just take the completed.

The application area is the progress on all datasets, which leaves us to a multidimensional datastructure.

* multivariate
* temporal
* numeric
* hierarchies
* countries < continents
* all data (totals)
** urban
** rural
** water
*** improved (totals)
**** piped
**** other improved
** sanitation
*** unimproved (totals)
**** open defecation
**** shared
**** other

fehlt hier noch etwas? diese auflistung scheint etwas komisch !? 

=== Analysis of the users ===
We declare the experts of preparing the sources of drinking water as the target audience of the visualization.
But users, doctors and inhabitants of the regions are also able to look and understand this graphic.
The shown dataset might be a reference for medical supporters, to know where the derivation of the complaints comes from.
Our diagram might let you find out the coherence between different variables and dependence on time.

=== Analysis of the tasks ===
What do we want to reach with our visualization?
We want to show the community the circumstances of the poor civilization.
Not just a picture of a point, but all information over the globe in a diagram where you can easily read the information of and compare to other countries. You can see on first see, which countries need more assistance to reach better results.
Different questions can be saved, like:
Are there any dependencies, which affect different countries?
Which continent has more access?

=== Visualization design ===
We choose the Job Voyager[1] as a design output to represent the stacked time series of the reported sampled data.
The data which should be shown, can be filtered by two different filters of population and its attribute.
A characteristic of the area under the drawn line in the chart is, that it shows us the bigger it is the bigger is it's value.
So you are able to see in the overview of the countries which have better prospects.
Furthermore the user has a interaction with the visualization by moving his mouse over the chart.
So the user has the facility to choose the continent bye mouse-over and look at it's countries.
In the left upper corner of the site, the actual name of the country gets shown, by moving the mouse over the area of this country in the chart.

[[Image:screenshot_job_voyager.png|thumb|300px|left|Figure 1: visualization, see [2]]]

advantages
* the ability to extend the chart
* main information on one site
* fluently workflow
* dependencies on different variables
* big dataset, as a smart visualization

disatvantages
* big numbers, hard to represent
* many countries in one chart confuses sometimes
* details hard to read
* sometimes difficult to find a specific country

=== Conclusion ===
brauchen wir nicht, oder will wer unbedingt was reintun ???? 

=== References ===
[1] report of unicef
[2] website of protovis

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3

2011-01-17T10:05:37Z

UE-InfoVis1011 0526223:

== Interactive visualization ==

=== Introduction ===
Based on a report [1] of the Unicef, who tries to make a greater access to dinking-water and sanitation for humans all over the world, we made a visualization, which presents the access to it, addiction to the number of the population. The maintenance of the urban population is far better developed, in comparison to the rural population. The main reason therefore is that the rural population has harder and bigger geographic conditions.

884 Million people do not use improved sources of drinking - water.
2.6 Billion people do not use improved sanitation, just 61 percent of human civilization does it.
With this in mind, we tried to show a analysis of the all regions over the world, which we have the information of.

This link leads you to the representation of the dataset.
*[http://keine_Ahnung_was - interactive visualization (LINK !??? )]

=== Analysis of the dataset ===
The dataset is continuous over the time from 1990 to 2008.
In an interval of every 5th year, in each country have been made entries of the (un-) improved sources of drinking water, the sanitation and the people who gained access to it. But not all datasets of all countries filled in, we just take the completed.

The application area is the progress on all datasets, which leaves us to a multidimensional datastructure.

* multivariate
* temporal
* numeric
* hierarchies
* countries < continents
* all data (totals)
** urban
** rural
** water
*** improved (totals)
**** piped
**** other improved
** sanitation
*** unimproved (totals)
**** open defecation
**** shared
**** other

fehlt hier noch etwas? diese auflistung scheint etwas komisch !? 

=== Analysis of the users ===
We declare the experts of preparing the sources of drinking water as the target audience of the visualization.
But users, doctors and inhabitants of the regions are also able to look and understand this graphic.
The shown dataset might be a reference for medical supporters, to know where the derivation of the complaints comes from.
Our diagram might let you find out the coherence between different variables and dependence on time.

=== Analysis of the tasks ===
What do we want to reach with our visualization?
We want to show the community the circumstances of the poor civilization.
Not just a picture of a point, but all information over the globe in a diagram where you can easily read the information of and compare to other countries. You can see on first see, which countries need more assistance to reach better results.
Different questions can be saved, like:
Are there any dependencies, which affect different countries?
Which continent has more access?

=== Visualization design ===
We choose the Job Voyager[1] as a design output to represent the stacked time series of the reported sampled data.
The data which should be shown, can be filtered by two different filters of population and its attribute.
A characteristic of the area under the drawn line in the chart is, that it shows us the bigger it is the bigger is it's value.
So you are able to see in the overview of the countries which have better prospects.
Furthermore the user has a interaction with the visualization by moving his mouse over the chart.
So the user has the facility to choose the continent bye mouse-over and look at it's countries.
In the left upper corner of the site, the actual name of the country gets shown, by moving the mouse over the area of this country in the chart.

[[Image:screenshot_job_voyager.png|thumb|300px|left|Figure 1: visualization, see [2]]]

advantages
* the ability to extend the chart
* main information on one site
* fluently workflow
* dependencies on different variables
* big dataset, as a smart visualization

disatvantages
* big numbers, hard to represent
* many countries in one chart confuses sometimes
* details hard to read
* sometimes difficult to find a specific country


=== Conclusion ===
brauchen wir nicht, oder will wer unbedingt was reintun ???? 

=== References ===
[1] report of unicef
[2] website of protovis

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3

2011-01-17T10:04:17Z

UE-InfoVis1011 0526223:

== Interactive visualization ==

=== Introduction ===
Based on a report [1] of the Unicef, who tries to make a greater access to dinking-water and sanitation for humans all over the world, we made a visualization, which presents the access to it, addiction to the number of the population. The maintenance of the urban population is far better developed, in comparison to the rural population. The main reason therefore is that the rural population has harder and bigger geographic conditions.

884 Million people do not use improved sources of drinking - water.
2.6 Billion people do not use improved sanitation, just 61 percent of human civilization does it.
With this in mind, we tried to show a analysis of the all regions over the world, which we have the information of.

This link leads you to the representation of the dataset.
*[http://keine_Ahnung_was - interactive visualization (LINK !??? )]

=== Analysis of the dataset ===
The dataset is continuous over the time from 1990 to 2008.
In an interval of every 5th year, in each country have been made entries of the (un-) improved sources of drinking water, the sanitation and the people who gained access to it. But not all datasets of all countries filled in, we just take the completed.

The application area is the progress on all datasets, which leaves us to a multidimensional datastructure.

* multivariate
* temporal
* numeric
* hierarchies
* countries < continents
* all data (totals)
** urban
** rural
** water
*** improved (totals)
**** piped
**** other improved
** sanitation
*** unimproved (totals)
**** open defecation
**** shared
**** other

fehlt hier noch etwas? diese auflistung scheint etwas komisch !? 

=== Analysis of the users ===
We declare the experts of preparing the sources of drinking water as the target audience of the visualization.
But users, doctors and inhabitants of the regions are also able to look and understand this graphic.
The shown dataset might be a reference for medical supporters, to know where the derivation of the complaints comes from.
Our diagram might let you find out the coherence between different variables and dependence on time.

=== Analysis of the tasks ===
What do we want to reach with our visualization?
We want to show the community the circumstances of the poor civilization.
Not just a picture of a point, but all information over the globe in a diagram where you can easily read the information of and compare to other countries. You can see on first see, which countries need more assistance to reach better results.
Different questions can be saved, like:
Are there any dependencies, which affect different countries?
Which continent has more access?

=== Visualization design ===
We choose the Job Voyager[1] as a design output to represent the stacked time series of the reported sampled data.
The data which should be shown, can be filtered by two different filters of population and its attribute.
A characteristic of the area under the drawn line in the chart is, that it shows us the bigger it is the bigger is it's value.
So you are able to see in the overview of the countries which have better prospects.
Furthermore the user has a interaction with the visualization by moving his mouse over the chart.
So the user has the facility to choose the continent bye mouse-over and look at it's countries.
In the left upper corner of the site, the actual name of the country gets shown, by moving the mouse over the area of this country in the chart.

[[Image:screenshot_job_voyager.png|thumb|400px|left|Figure 1: visualization, see [2]]]

advantages
* the ability to extend the chart
* main information on one site
* fluently workflow
* dependencies on different variables
* big dataset, as a smart visualization

disatvantages
* big numbers, hard to represent
* many countries in one chart confuses sometimes
* details hard to read
* sometimes difficult to find a specific country

=== Conclusion ===
brauchen wir nicht, oder will wer unbedingt was reintun ???? 

=== References ===
[1] report of unicef
[2] website of protovis

File:Screenshot job voyager.png

2011-01-17T10:01:59Z

UE-InfoVis1011 0526223: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3

2011-01-17T10:00:29Z

UE-InfoVis1011 0526223:

== Interactive visualization ==

=== Introduction ===
Based on a report [1] of the Unicef, who tries to make a greater access to dinking-water and sanitation for humans all over the world, we made a visualization, which presents the access to it, addiction to the number of the population. The maintenance of the urban population is far better developed, in comparison to the rural population. The main reason therefore is that the rural population has harder and bigger geographic conditions.

884 Million people do not use improved sources of drinking - water.
2.6 Billion people do not use improved sanitation, just 61 percent of human civilization does it.
With this in mind, we tried to show a analysis of the all regions over the world, which we have the information of.

This link leads you to the representation of the dataset.
*[http://keine_Ahnung_was - interactive visualization]

=== Analysis of the dataset ===
The dataset is continuous over the time from 1990 to 2008.
In an interval of every 5th year, in each country have been made entries of the (un-) improved sources of drinking water, the sanitation and the people who gained access to it. But not all datasets of all countries filled in, we just take the completed.

The application area is the progress on all datasets, which leaves us to a multidimensional datastructure.

* multivariate
* temporal
* numeric
* hierarchies
* countries < continents
* all data (totals)
** urban
** rural
** water
*** improved (totals)
**** piped
**** other improved
** sanitation
*** unimproved (totals)
**** open defecation
**** shared
**** other

fehlt hier noch etwas? diese auflistung scheint etwas komisch !? 

=== Analysis of the users ===
We declare the experts of preparing the sources of drinking water as the target audience of the visualization.
But users, doctors and inhabitants of the regions are also able to look and understand this graphic.
The shown dataset might be a reference for medical supporters, to know where the derivation of the complaints comes from.
Our diagram might let you find out the coherence between different variables and dependence on time.

=== Analysis of the tasks ===
What do we want to reach with our visualization?
We want to show the community the circumstances of the poor civilization.
Not just a picture of a point, but all information over the globe in a diagram where you can easily read the information of and compare to other countries. You can see on first see, which countries need more assistance to reach better results.
Different questions can be saved, like:
Are there any dependencies, which affect different countries?
Which continent has more access?

=== Visualization design ===
We choose the Job Voyager[1] as a design output to represent the stacked time series of the reported sampled data.
The data which should be shown, can be filtered by two different filters of population and its attribute.
A characteristic of the area under the drawn line in the chart is, that it shows us the bigger it is the bigger is it's value.
So you are able to see in the overview of the countries which have better prospects.
Furthermore the user has a interaction with the visualization by moving his mouse over the chart.
So the user has the facility to choose the continent bye mouse-over and look at it's countries.
In the left upper corner of the site, the actual name of the country gets shown, by moving the mouse over the area of this country in the chart.

[[Image:screenshot_job_voyager.png|thumb|300px|left|Figure 1: visualization, see [2]]]

[[Image:harakiriscreenshot.jpg|200px]]

advantages
* the ability to extend the chart
* main information on one site
* fluently workflow
* dependencies on different variables
* big dataset, as a smart visualization

disatvantages
* big numbers, hard to represent
* many countries in one chart confuses sometimes
* details hard to read
* sometimes difficult to find a specific country

=== Conclusion ===
brauchen wir nicht oder will wer unbedingt was reintun ???? 

=== References ===
[1] report of unicef
[2] website

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3

2011-01-17T09:57:46Z

UE-InfoVis1011 0526223:

== Interactive visualization ==

=== Introduction ===
Based on a report [1] of the Unicef, who tries to make a greater access to dinking-water and sanitation for humans all over the world, we made a visualization, which presents the access to it, addiction to the number of the population. The maintenance of the urban population is far better developed, in comparison to the rural population. The main reason therefore is that the rural population has harder and bigger geographic conditions.

884 Million people do not use improved sources of drinking - water.
2.6 Billion people do not use improved sanitation, just 61 percent of human civilization does it.
With this in mind, we tried to show a analysis of the all regions over the world, which we have the information of.

This link leads you to the representation of the dataset.
*[http://keine_Ahnung_was - interactive visualization]

=== Analysis of the dataset ===
The dataset is continuous over the time from 1990 to 2008.
In an interval of every 5th year, in each country have been made entries of the (un-) improved sources of drinking water, the sanitation and the people who gained access to it. But not all datasets of all countries filled in, we just take the completed.

The application area is the progress on all datasets, which leaves us to a multidimensional datastructure.

* multivariate
* temporal
* numeric
* hierarchies
* countries < continents
* all data (totals)
** urban
** rural
** water
*** improved (totals)
**** piped
**** other improved
** sanitation
*** unimproved (totals)
**** open defecation
**** shared
**** other

fehlt hier noch etwas? diese auflistung scheint etwas komisch !? 

=== Analysis of the users ===
We declare the experts of preparing the sources of drinking water as the target audience of the visualization.
But users, doctors and inhabitants of the regions are also able to look and understand this graphic.
The shown dataset might be a reference for medical supporters, to know where the derivation of the complaints comes from.
Our diagram might let you find out the coherence between different variables and dependence on time.

=== Analysis of the tasks ===
What do we want to reach with our visualization?
We want to show the community the circumstances of the poor civilization.
Not just a picture of a point, but all information over the globe in a diagram where you can easily read the information of and compare to other countries. You can see on first see, which countries need more assistance to reach better results.
Different questions can be saved, like:
Are there any dependencies, which affect different countries?
Which continent has more access?

=== Visualization design ===
We choose the Job Voyager[1] as a design output to represent the stacked time series of the reported sampled data.
The data which should be shown, can be filtered by two different filters of population and its attribute.
A characteristic of the area under the drawn line in the chart is, that it shows us the bigger it is the bigger is it's value.
So you are able to see in the overview of the countries which have better prospects.
Furthermore the user has a interaction with the visualization by moving his mouse over the chart.
So the user has the facility to choose the continent bye mouse-over and look at it's countries.
In the left upper corner of the site, the actual name of the country gets shown, by moving the mouse over the area of this country in the chart.

[[Image:screenshot_job_voyager.png|thumb|300px|left|Figure 1: visualization, see [2]]]

advantages
* the ability to extend the chart
* main information on one site
* fluently workflow
* dependencies on different variables
* big dataset, as a smart visualization

disatvantages
* big numbers, hard to represent
* many countries in one chart confuses sometimes
* details hard to read
* sometimes difficult to find a specific country

=== Conclusion ===
brauchen wir nicht oder will wer unbedingt was reintun ???? 

=== References ===
[1] report of unicef
[2] website

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3

2011-01-17T09:45:15Z

UE-InfoVis1011 0526223:

== Interactive visualization ==

=== Introduction ===
Based on a report [1] of the Unicef, who tries to make a greater access to dinking-water and sanitation for humans all over the world, we made a visualization, which presents the access to it, addiction to the number of the population. The maintenance of the urban population is far better developed, in comparison to the rural population. The main reason therefore is that the rural population has harder and bigger geographic conditions.

884 Million people do not use improved sources of drinking - water.
2.6 Billion people do not use improved sanitation, just 61 percent of human civilization does it.
With this in mind, we tried to show a analysis of the all regions over the world, which we have the information of.

This link leads you to the representation of the dataset.

[http://webspace...............?]

=== Analysis of the dataset ===
The dataset is continuous over the time from 1990 to 2008.
In an interval of every 5th year, in each country have been made entries of the (un-) improved sources of drinking water, the sanitation and the people who gained access to it. But not all datasets of all countries filled in, we just take the completed.

The application area is the progress on all datasets, which leaves us to a multidimensional datastructure.

* multivariate
* temporal
* numeric
* hierarchies
* countries < continents
* all data (totals)
** urban
** rural
** water
*** improved (totals)
**** piped
**** other improved
** sanitation
*** unimproved (totals)
**** open defecation
**** shared
**** other

fehlt hier noch etwas (?????????) diese auflistung scheint etwas komisch !?

=== Analysis of the users ===
We declare the experts of preparing the sources of drinking water as the target audience of the visualization.
But users, doctors and inhabitants of the regions are also able to look and understand this graphic.
The shown dataset might be a reference for medical supporters, to know where the derivation of the complaints comes from.
Our diagram might let you find out the coherence between different variables and dependence on time.

=== Analysis of the tasks ===
What do we want to reach with our visualization?
We want to show the community the circumstances of the poor civilization.
Not just a picture of a point, but all information over the globe in a diagram where you can easily read the information of and compare to other countries. You can see on first see, which countries need more assistance to reach better results.
Different questions can be saved, like:
Are there any dependencies, which affect different countries?
Which continent has more access?

=== Visualization design ===
We choose the Job Voyager[1] as a design output to represent the stacked time series of the reported sampled data.
The data which should be shown, can be filtered by two different filters of population and its attribute.
A characteristic of the area under the drawn line in the chart is, that it shows us the bigger it is the bigger is it's value.
So you are able to see in the overview of the countries which have better prospects.
Furthermore the user has a interaction with the visualization by moving his mouse over the chart.
So the user has the facility to choose the continent bye mouse-over and look at it's countries.
In the left upper corner of the site, the actual name of the country gets shown, by moving the mouse over the area of this country in the chart.

[[Image:screenshot.png|thumb|300px|left|Figure 1: visualization, see [2]]]

advantages
* the ability to extend the chart
* main information on one site
* fluently workflow
* dependencies on different variables
* big dataset, as a smart visualization

disatvantages
* big numbers, hard to represent
* many countries in one chart confuses sometimes
* details hard to read
* sometimes difficult to find a specific country

=== Conclusion ===
brauchen wir nicht oder will wer unbedingt was reintun ???? 

=== References ===
[1] report of unicef
[2] website

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3

2011-01-17T09:41:33Z

UE-InfoVis1011 0526223:

== Interactive visualization ==

=== Introduction ===
Based on a report [1] of the Unicef, who tries to make a greater access to dinking-water and sanitation for humans all over the world, we made a visualization, which presents the access to it, addiction to the number of the population. The maintenance of the urban population is far better developed, in comparison to the rural population. The main reason therefore is that the rural population has harder and bigger geographic conditions.

884 Million people do not use improved sources of drinking - water.
2.6 Billion people do not use improved sanitation, just 61 percent of human civilization does it.
With this in mind, we tried to show a analysis of the all regions over the world, which we have the information of.

This link leads you to the representation of the dataset.
[http://webspace...............?]

=== Analysis of the dataset ===
The dataset is continuous over the time from 1990 to 2008.
In an interval of every 5th year, in each country have been made entries of the (un-) improved sources of drinking water, the sanitation and the people who gained access to it. But not all datasets of all countries filled in, we just take the completed.

The application area is the progress on all datasets, which leaves us to a multidimensional datastructure.

* multivariate
* temporal
* numeric
* hierarchies
* countries < continents
* all data (totals)
** urban
** rural
** water
*** improved (totals)
**** piped
**** other improved
** sanitation
*** unimproved (totals)
**** open defecation
**** shared
**** other

fehlt hier noch etwas (?????????) diese auflistung scheint etwas komisch !?

=== Analysis of the users ===
We declare the experts of preparing the sources of drinking water as the target audience of the visualization.
But users, doctors and inhabitants of the regions are also able to look and understand this graphic.
The shown dataset might be a reference for medical supporters, to know where the derivation of the complaints comes from.
Our diagram might let you find out the coherence between different variables and dependence on time.

=== Analysis of the tasks ===
What do we want to reach with our visualization?
We want to show the community the circumstances of the poor civilization.
Not just a picture of a point, but all information over the globe in a diagram where you can easily read the information of and compare to other countries. You can see on first see, which countries need more assistance to reach better results.
Different questions can be saved, like:
Are there any dependencies, which affect different countries?
Which continent has more access?

=== Visualization design ===
We choose the Job Voyager[1] as a design output to represent the stacked time series of the reported sampled data.
The data which should be shown, can be filtered by two different filters of population and its attribute.
A characteristic of the area under the drawn line in the chart is, that it shows us the bigger it is the bigger is it's value.
So you are able to see in the overview of the countries which have better prospects.
Furthermore the user has a interaction with the visualization by moving his mouse over the chart.
So the user has the facility to choose the continent bye mouse-over and look at it's countries.
In the left upper corner of the site, the actual name of the country gets shown, by moving the mouse over the area of this country in the chart.

[[Image:screenshot.png|thumb|300px|left|Figure 1: visualization, see [2] ]]

advantages
* the ability to extend the chart
* main information on one site
* fluently workflow
* dependencies on different variables
* big dataset, as a smart visualization

disatvantages
* big numbers, hard to represent
* many countries in one chart confuses sometimes
* details hard to read
* sometimes difficult to find a specific country
° haha
° haha

=== Conclusion ===
brauchen wir nicht oder ???

=== References ===
[]

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3

2011-01-17T01:18:20Z

UE-InfoVis1011 0526223:

== interactive visualization ==

... whatever ...

=== Introduction ===
Based on a report [1] of the Unicef, who tries to make a greater access to dinking-water and sanitation for humans all over the world, we made a visualization, which presents the access to it, addiction to the number of the population. The maintenance of the urban population is far better developed, in comparison to the rural population. The main reason therefore is that the rural population has harder and bigger geographic conditions.

884 Million people do not use improved sources of drinking - water.
2.6 Billion people do not use improved sanitation, just 61 percent of human civilization does it.
With this in mind, we tried to show a analysis of the all regions over the world, which we have the information of.

=== dataset analysis ===
The dataset is continuous over the time from 1990 to 2008.
In an interval of every 5th year, in each country have been made entries of the (un-) improved sources of drinking water, the sanitation and the people who gained access to it. But not all datasets of all countries filled in, we just take the completed.

The application area is the progress on all datasets, which leaves us to a multidimensional datastructure.

* multivariate
* temporal
* numeric
* hierarchies
* countries < continents
* all data (totals)
** urban
** rural
** water
*** improved (totals)
**** piped
**** other improved
** sanitation
*** unimproved (totals)
**** open defecation
**** shared
**** other

=== users ===
We declare the doctors and experts of preparing the sources of drinking water as the target audience of the visualization.
But users and inhabitants of the regions are also able to look and understand this graphic.
The shown dataset might be a reference for medical supporters, to know where the derivation of the complaints comes from.
Our diagram might let you find out the coherence between different variables and dependence on time.

=== tasks ===
...

=== visualization design ===
...

=== Conclusion ===
...

=== References ===
...

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3

2011-01-06T22:07:54Z

UE-InfoVis1011 0526223: New page: == interactive visualization == ... whatever ... === Introduction === ... === data === ... === users === ... === tasks === ... === visualization design === ... === Conclusion === ...

== interactive visualization ==

... whatever ...

=== Introduction ===

...

=== data ===
...

=== users ===
...

=== tasks ===
...

=== visualization design ===
...

=== Conclusion ===
...

=== References ===
...

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 2

2010-11-17T22:47:44Z

UE-InfoVis1011 0526223:

== Pargnostics: Screen-Space Metrics for Parallel Coordinates ==

=== Introduction ===

Visualization still takes place in a space with a limited number of discrete pixels.
The result of this often is over-plotting, clutter or other things.
Most of the time this visual structures are avoided.
But sometimes this artifacts can be useful, because they might point out interesting structures in the data.
Good visualizations have to show the relevant information at the first glance and therefore show the information in a clear structure.
Until now not a lot of attention is paid to the way a visualzation is presented on the screen.
For the case of parallel coordinates so called Pargnostics (Parallel coordinates diagnostics) should act as a bridge between the created visualization and the perceptual system of the user.
Based on metrics it's possible to provide an optimization which maximize or minimize certain visual artifacts. [Dasgupta and Kosara, 2010]

=== Metrics ===

[[Image:Dasgupta+Kosara Pixel-Space Histograms.png|thumb|300px|right|Figure 1: Pixel-space histograms, see [Dasgupta and Kosara, 2010] page 1018.]]
To automatically enhance the analytical tasks of users Dasgupta and Kosara [2010] proposed several metrics.
The metrics are used to measure the properties of parallel coordinates.
First ''pixel-space histograms'' are calculated to optimize the calculation of these metrics.
Pixel-space histograms discretize the lines drawn in parallel coordinates into bins - each bin being one pixel (for a total of ''h'' pixels in an axis):
* ''One-Dimensional Axis Histogram'': A vector ''b'' containing the number of lines that start or end at this pixel - see columns A and B in figure 1.
* ''One-Dimensional Distance Histogram'': A vector ''d'' where each component measures the slope of lines.
* ''Two-Dimensional Axis Pair Histogram'': A matrix where each cell ''xi,j'' states that ''n'' lines are going from pixel ''i'' to pixel ''j'' - see matrix in figure 1.

The metrics proposed can be used for measuring different data properties: ''correlation'' (number of line crossings, angles of crossing), ''aggregation'' (parallelism), ''many-to-one/one-to-many relationships'' (convergence, divergence), ''quality'' (over-plotting), ''information density'' (pixel-based entropy).

One of the most useful optimization is the line crossing and the parallelism metrics. Angels of crossing helps out, where line crossing gets difficult to read.
The convergence-divergence metric works very well for categorical axes and the pixel based entropy optimizes the alpha value, which is useful on larger datasets.

;Number of Line Crossings
:This metric intuitively just does what the name implies, count how many line crossings there are between two axes of the parallel coordinates. The count is efficiently calculated by using intervals as proposed by [Allen, 1983; Rit 1986] with a complexity of ''O(h4)''. This count is then normalized by the maximum number of possible crossings in order to compare the metric with different axis combinations. [Dasgupta and Kosara, 2010]

;Angles of Crossing
:Lines crossing at flat angles tend to create clutter [Dasgupta and Kosara, 2010]. To calculate this metric, first the crossing angles between every pair of lines which are crossing get calculated. From the results of these calculations the median crossing angle is obtained to be used as the resulting metric value. See the right histograms in figure 2. [[Image:Dasgupta+Kosara_Distance+angle-histograms.png|thumb|300px|Figure 2: "Distance histograms (left half of each cell below the parallel coordinates) and angles of crossings (right half) histograms for different dimensions of the cars data." See [Dasgupta and Kosara, 2010] page 1021.]]

;Parallelism
:A pair of lines which is not crossing is parallel to each other. Parallelism is often prefered as a metric compared to the number of crossings because it tends to produce less clutter [Dasgupta and Kosara, 2010]. Parallelism is calculated by taking the inverse of the absolute interquartile range of line distances.
:Narrowly distributed histograms have high parallelism. See the left histograms figure 2. Between horsepower and weight there is high parallelism. Between the other axes not.

;Mutual Information
:Mutual Information measures the statistical dependence of the drawn data. Pargnostics treats the data dimensions as random variables and uses the two-dimensional axis histogram to denote the joint probability of random variables. This metric's value should be maximized.

;Convergence, Divergence
:Lines from the left axis joining in single points on the right axis are converging. Divergence is the inverse of this - i.e. lines from the right joining on the left axis.

;Over-plotting
:Over-plotting measures how many lines are aggregated on a single pixel when the parallel coordinates are drawn. This metric is directly dependent on the number of bins (pixels) used for an axis [Dasgupta and Kosara, 2010]. The value obtained by this metric should be minimized to increase the quality of the visualization.

;Pixel-based Entropy
:This metric shows the degree of randomness in any segment of a visualization. A high pixel-based entropy normally leads to busy but very readable displays of data. [Dasgupta and Kosara, 2010]

=== Dimension Order Optimization ===

Using the metrics from above helps to find an optimization of the visualization display.
This is an NP-complete problem, but by using a binned data model and by considering the special properties of parallel coordinates it's possible to reduce the amount of time, which is needed to find optimal solutions [Dasgupta and Kosara, 2010].
Using a branch-and-bound algorithm also can reduce the time necessary.

The purpose of the branch-and-bound algorithm is to find the optimal order of axes.
To do that a matrix of all axis pairs and their associated costs gets constructed.
The costs are a combination of several metrics.

The construction of the matrix has to be done only once at the beginning of the algorithm.

==== Axis Inversions ====

While constructing the matrix of axis pairs both the inverted and noninverted situation for every axis pair is taken into account.
The state - inverted or not inverted - with the lower cost is used in the matrix and the algorithm keeps track, which one it was.
This happens locally so inverting one axis pair, doesn't have an immediate effect on other axis pairs.

==== Branch-and-Bound Optimization ====

The branch-and-bound algorithm uses a priority queue and best-first search.
For that kind of implementations it's very important to make precise estimates, which subtrees can be culled and which can't.
Since these estimates are based on the full cost matrix, which is constructed at the beginning of the algorithm, they are indeed very precise.

Based on case studies by Dasgupta and Kosara [2010] the branch-and-bound algorithm finds the optimal solution really quick.
The main reason for this is that the metrics are constructed for every axis pair on their own and not for every possible combination of the axis pairs in the entire visualization.

=== Conclusion ===

{{Quotation|In this sense, Pargnostics fills a gap in the existing literature on parallel coordinates. Being able to analyze what ends up on the screen makes it possible to provide better visualization setups that take the specific properties of the visualization technique into account.|Dasgupta and Kosara, 2010}}

The metrics and the optimization explained above are an important step towards better visualiztions.
These metrics not only describe the image which is rendered to the screen, but also the different visual structures which can be seen in this image.

=== References ===

*[Allen, 1983] J. Allen. Maintaining knowledge about temporal intervals. ''Communications of the ACM'', 26:832-843, 1983.
*[Dasgupta and [[Kosara, Robert|Kosara]], 2010] Aritra Dasgupta and [[Kosara, Robert|Robert Kosara]]. Pargnostics: Screen-Space Metrics for Parallel Coordinates. ''IEEE Transactions on Visualization and Computer Graphics'', 16(6):1017-1026, November/December 2010.
*[Rit, 1986] J.-F. Rit. Propagating temporal constraints for scheduling. In ''Proceedings of the Fifth National Conference on Artificial Intelligence'', pages 383-388, 1986

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 2

2010-11-17T22:42:13Z

UE-InfoVis1011 0526223:

== Pargnostics: Screen-Space Metrics for Parallel Coordinates ==

=== Introduction ===

Visualization still takes place in a space with a limited number of discrete pixels.
The result of this often is over-plotting, clutter or other things.
Most of the time this visual structures are avoided.
But sometimes this artifacts can be useful, because they might point out interesting structures in the data.
Good visualizations have to show the relevant information at the first glance and therefore show the information in a clear structure.
Until now not a lot of attention is paid to the way a visualzation is presented on the screen.
For the case of parallel coordinates so called Pargnostics (Parallel coordinates diagnostics) should act as a bridge between the created visualization and the perceptual system of the user.
Based on metrics it's possible to provide an optimization which maximize or minimize certain visual artifacts. [Dasgupta and Kosara, 2010]

=== Metrics ===

[[Image:Dasgupta+Kosara Pixel-Space Histograms.png|thumb|300px|right|Figure 1: Pixel-space histograms, see [Dasgupta and Kosara, 2010] page 1018.]]
To automatically enhance the analytical tasks of users Dasgupta and Kosara [2010] proposed several metrics.
The metrics are used to measure the properties of parallel coordinates.
First ''pixel-space histograms'' are calculated to optimize the calculation of these metrics.
Pixel-space histograms discretize the lines drawn in parallel coordinates into bins - each bin being one pixel (for a total of ''h'' pixels in an axis):
* ''One-Dimensional Axis Histogram'': A vector ''b'' containing the number of lines that start or end at this pixel - see columns A and B in figure 1.
* ''One-Dimensional Distance Histogram'': A vector ''d'' where each component measures the slope of lines.
* ''Two-Dimensional Axis Pair Histogram'': A matrix where each cell ''xi,j'' states that ''n'' lines are going from pixel ''i'' to pixel ''j'' - see matrix in figure 1.

The metrics proposed can be used for measuring different data properties: ''correlation'' (number of line crossings, angles of crossing), ''aggregation'' (parallelism), ''many-to-one/one-to-many relationships'' (convergence, divergence), ''quality'' (over-plotting), ''information density'' (pixel-based entropy).

One of the most useful optimization is the line crossing and the parallelism metrics. Angels of crossing helps out, where line crossing gets difficult to read.
The convergence-divergence metric works very well for categorical axes and the pixel based entropy optimizes the alpha value, which is useful on larger datasets.

;Number of Line Crossings
:This metric intuitively just does what the name implies, count how many line crossings there are between two axes of the parallel coordinates. The count is efficiently calculated by using intervals as proposed by [Allen, 1983; Rit 1986] with a complexity of ''O(h4)''. This count is then normalized by the maximum number of possible crossings in order to compare the metric with different axis combinations. [Dasgupta and Kosara, 2010]

;Angles of Crossing
:Lines crossing at flat angles tend to create clutter [Dasgupta and Kosara, 2010]. To calculate this metric, first the crossing angles between every pair of lines which are crossing get calculated. From the results of these calculations the median crossing angle is obtained to be used as the resulting metric value. See the right histograms in figure 2. [[Image:Dasgupta+Kosara_Distance+angle-histograms.png|thumb|300px|Figure 2: "Distance histograms (left half of each cell below the parallel coordinates) and angles of crossings (right half) histograms for different dimensions of the cars data." See [Dasgupta and Kosara, 2010] page 1021.]]

;Parallelism
:A pair of lines which is not crossing is parallel to each other. Parallelism is often prefered as a metric compared to the number of crossings because it tends to produce less clutter [Dasgupta and Kosara, 2010]. Parallelism is calculated by taking the inverse of the absolute interquartile range of line distances.
:Narrowly distributed histograms have high parallelism. See the left histograms figure 2. Between horsepower and weight there is high parallelism. Between the other axes not.

;Mutual Information
:Mutual Information measures the statistical dependence of the drawn data. Pargnostics treats the data dimensions as random variables and uses the two-dimensional axis histogram to denote the joint probability of random variables. This metric's value should be maximized.

;Convergence, Divergence
:Lines from the left axis joining in single points on the right axis are converging. Divergence is the inverse of this - i.e. lines from the right joining on the left axis.

;Over-plotting
:Over-plotting measures how many lines are aggregated on a single pixel when the parallel coordinates are drawn. This metric is directly dependent on the number of bins (pixels) used for an axis [Dasgupta and Kosara, 2010]. The value obtained by this metric should be minimized to increase the quality of the visualization.

;Pixel-based Entropy
:This metric shows the degree of randomness in any segment of a visualization. A high pixel-based entropy normally leads to busy but very readable displays of data. [Dasgupta and Kosara, 2010]

=== Dimension Order Optimization ===

Using the metrics from above helps to find an optimization of the visualization display.
This is an NP-complete problem, but by using a binned data model and by considering the special properties of parallel coordinates it's possible to reduce the amount of time which is needed to find optimal solutions [Dasgupta and Kosara, 2010].
Using a branch-and-bound algorithm also can reduce the time necessary.

The purpose of the branch-and-bound algorithm is to find the optimal order of axes.
To do that a matrix of all axis pairs and their associated costs gets constructed.
The costs are a combination of several metrics.

The construction of the matrix has to be done only once at the beginning of the algorithm.

==== Axis Inversions ====

While constructing the matrix of axis pairs both the inverted and noninverted situation for every axis pair is taken into account.
The state - inverted or not inverted - with the lower cost is used in the matrix and the algorithm keeps track which one it was.
This happens locally so inverting one axis pair doesn't have an immediate effect on other axis pairs.

==== Branch-and-Bound Optimization ====

The branch-and-bound algorithm uses a priority queue and best-first search.
For that kind of implementations it's very important to make precise estimates, which subtrees can be culled and which can't.
Since these estimates are based on the full cost matrix, which is constructed at the beginning of the algorithm, they are indeed very precise.

Based on case studies by Dasgupta and Kosara [2010] the branch-and-bound algorithm finds the optimal solution really quick.
The main reason for this is that the metrics are constructed for every axis pair on their own and not for every possible combination of the axis pairs in the entire visualization.

=== Conclusion ===

{{Quotation|In this sense, Pargnostics fills a gap in the existing literature on parallel coordinates. Being able to analyze what ends up on the screen makes it possible to provide better visualization setups that take the specific properties of the visualization technique into account.|Dasgupta and Kosara, 2010}}

The metrics and the optimization explained above are an important step towards better visualiztions.
This metrics not only describe the image which is rendered to screen, but also the different visual structures which can be seen in this image.

=== References ===

*[Allen, 1983] J. Allen. Maintaining knowledge about temporal intervals. ''Communications of the ACM'', 26:832-843, 1983.
*[Dasgupta and [[Kosara, Robert|Kosara]], 2010] Aritra Dasgupta and [[Kosara, Robert|Robert Kosara]]. Pargnostics: Screen-Space Metrics for Parallel Coordinates. ''IEEE Transactions on Visualization and Computer Graphics'', 16(6):1017-1026, November/December 2010.
*[Rit, 1986] J.-F. Rit. Propagating temporal constraints for scheduling. In ''Proceedings of the Fifth National Conference on Artificial Intelligence'', pages 383-388, 1986

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 2

2010-11-17T22:39:45Z

UE-InfoVis1011 0526223:

== Pargnostics: Screen-Space Metrics for Parallel Coordinates ==

=== Introduction ===

Visualization still takes place in a space with a limited number of discrete pixels.
The result of this often is over-plotting, clutter or other things.
Most of the time this visual structures are avoided.
But sometimes this artifacts can be useful, because they might point out interesting structures in the data.
Good visualizations have to show the relevant information at the first glance and therefore show the information in a clear structure.
Until now not a lot of attention is paid to the way a visualzation is presented on the screen.
For the case of parallel coordinates so called Pargnostics (Parallel coordinates diagnostics) should act as a bridge between the created visualization and the perceptual system of the user.
Based on metrics it's possible to provide an optimization which maximize or minimize certain visual artifacts. [Dasgupta and Kosara, 2010]

=== Metrics ===

[[Image:Dasgupta+Kosara Pixel-Space Histograms.png|thumb|300px|right|Figure 1: Pixel-space histograms, see [Dasgupta and Kosara, 2010] page 1018.]]
To automatically enhance the analytical tasks of users Dasgupta and Kosara [2010] proposed several metrics.
The metrics are used to measure the properties of parallel coordinates.
First ''pixel-space histograms'' are calculated to optimize the calculation of these metrics.
Pixel-space histograms discretize the lines drawn in parallel coordinates into bins - each bin being one pixel (for a total of ''h'' pixels in an axis):
* ''One-Dimensional Axis Histogram'': A vector ''b'' containing the number of lines that start or end at this pixel - see columns A and B in figure 1.
* ''One-Dimensional Distance Histogram'': A vector ''d'' where each component measures the slope of lines.
* ''Two-Dimensional Axis Pair Histogram'': A matrix where each cell ''xi,j'' states that ''n'' lines are going from pixel ''i'' to pixel ''j'' - see matrix in figure 1.

The metrics proposed can be used for measuring different data properties: ''correlation'' (number of line crossings, angles of crossing), ''aggregation'' (parallelism), ''many-to-one/one-to-many relationships'' (convergence, divergence), ''quality'' (over-plotting), ''information density'' (pixel-based entropy).

One of the most useful optimization is the line crossing and the parallelism metrics. Angels of crossing helps out, where line crossing gets difficult to read.
The convergence-divergence metric works very well for categorical axes and the pixel based entropy optimizes the alpha value, which is useful on larger datasets.

;Number of Line Crossings
:This metric intuitively just does what the name implies, count how many line crossings there are between two axes of the parallel coordinates. The count is efficiently calculated by using intervals as proposed by [Allen, 1983; Rit 1986] with a complexity of ''O(h4)''. This count is then normalized by the maximum number of possible crossings in order to compare the metric with different axis combinations. [Dasgupta and Kosara, 2010]

;Angles of Crossing
:Lines crossing at flat angles tend to create clutter [Dasgupta and Kosara, 2010]. To calculate this metric, first the crossing angles between every pair of lines which are crossing get calculated. From the results of these calculations the median crossing angle is obtained to be used as the resulting metric value. See the right histograms in figure 2. [[Image:Dasgupta+Kosara_Distance+angle-histograms.png|thumb|300px|Figure 2: "Distance histograms (left half of each cell below the parallel coordinates) and angles of crossings (right half) histograms for different dimensions of the cars data." See [Dasgupta and Kosara, 2010] page 1021.]]

;Parallelism
:A pair of lines which is not crossing is parallel to each other. Parallelism is often prefered as a metric compared to the number of crossings because it tends to produce less clutter [Dasgupta and Kosara, 2010]. Parallelism is calculated by taking the inverse of the absolute interquartile range of line distances.
:Narrowly distributed histograms have high parallelism. See the left histograms figure 2. Between horsepower and weight there is high parallelism. Between the other axes not.

;Mutual Information
:Mutual Information measures the statistical dependence of the drawn data. Pargnostics treats the data dimensions as random variables and uses the two-dimensional axis histogram to denote the joint probability of random variables. This metric's value should be maximized.

;Convergence, Divergence
:Lines from the left axis joining in single points on the right axis are converging. Divergence is the inverse of this - i.e. lines from the right joining on the left axis.

;Over-plotting
:Over-plotting measures how many lines are aggregated on a single pixel when the parallel coordinates are drawn. This metric is directly dependent on the number of bins (pixels) used for an axis [Dasgupta and Kosara, 2010]. The value obtained by this metric should be minimized to increase the quality of the visualization.

;Pixel-based Entropy
:This metric shows the degree of randomness in any segment of a visualization. A high pixel-based entropy based entropy normally leads to busy but very readable displays of data. [Dasgupta and Kosara, 2010]

=== Dimension Order Optimization ===

Using the metrics from above helps to find an optimization of the visualization display.
This is an NP-complete problem, but by using a binned data model and by considering the special properties of parallel coordinates it's possible to reduce the amount of time which is needed to find optimal solutions [Dasgupta and Kosara, 2010].
Using a branch-and-bound algorithm also can reduce the time necessary.

The purpose of the branch-and-bound algorithm is to find the optimal order of axes.
To do that a matrix of all axis pairs and their associated costs gets constructed.
The costs are a combination of several metrics.

The construction of the matrix has to be done only once at the beginning of the algorithm.

==== Axis Inversions ====

While constructing the matrix of axis pairs both the inverted and noninverted situation for every axis pair is taken into account.
The state - inverted or not inverted - with the lower cost is used in the matrix and the algorithm keeps track which one it was.
This happens locally so inverting one axis pair doesn't have an immediate effect on other axis pairs.

==== Branch-and-Bound Optimization ====

The branch-and-bound algorithm uses a priority queue and best-first search.
For that kind of implementations it's very important to make precise estimates, which subtrees can be culled and which can't.
Since these estimates are based on the full cost matrix, which is constructed at the beginning of the algorithm, they are indeed very precise.

Based on case studies by Dasgupta and Kosara [2010] the branch-and-bound algorithm finds the optimal solution really quick.
The main reason for this is that the metrics are constructed for every axis pair on their own and not for every possible combination of the axis pairs in the entire visualization.

=== Conclusion ===

{{Quotation|In this sense, Pargnostics fills a gap in the existing literature on parallel coordinates. Being able to analyze what ends up on the screen makes it possible to provide better visualization setups that take the specific properties of the visualization technique into account.|Dasgupta and Kosara, 2010}}

The metrics and the optimization explained above are an important step towards better visualiztions.
This metrics not only describe the image which is rendered to screen, but also the different visual structures which can be seen in this image.

=== References ===

*[Allen, 1983] J. Allen. Maintaining knowledge about temporal intervals. ''Communications of the ACM'', 26:832-843, 1983.
*[Dasgupta and [[Kosara, Robert|Kosara]], 2010] Aritra Dasgupta and [[Kosara, Robert|Robert Kosara]]. Pargnostics: Screen-Space Metrics for Parallel Coordinates. ''IEEE Transactions on Visualization and Computer Graphics'', 16(6):1017-1026, November/December 2010.
*[Rit, 1986] J.-F. Rit. Propagating temporal constraints for scheduling. In ''Proceedings of the Fifth National Conference on Artificial Intelligence'', pages 383-388, 1986

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 2

2010-11-17T22:13:42Z

UE-InfoVis1011 0526223:

== Pargnostics: Screen-Space Metrics for Parallel Coordinates ==

=== Introduction ===

Visualization still takes place in a space with a limited number of discrete pixels.
The result of this often is over-plotting, clutter or other things.
Most of the time this visual structures are avoided.
But sometimes this artifacts can be useful, because they might point out interesting structures in the data.
Good visualizations have to show the relevant information at the first glance and therefore show the information in a clear structure.
Until now not a lot of attention is paid to the way a visualzation is presented on the screen.
For the case of parallel coordinates so called Pargnostics (Parallel coordinates diagnostics) should act as a bridge between the created visualization and the perceptual system of the user.
Based on metrics it's possible to provide an optimization which maximize or minimize certain visual artifacts. [Dasgupta and Kosara, 2010]

=== Metrics ===

[[Image:Dasgupta+Kosara Pixel-Space Histograms.png|thumb|300px|right|Figure 1: Pixel-space histograms, see [Dasgupta and Kosara, 2010] page 1018.]]
To automatically enhance the analytical tasks of users Dasgupta and Kosara [2010] proposed several metrics.
The metrics are used to measure the properties of parallel coordinates.
First ''pixel-space histograms'' are calculated to optimize the calculation of these metrics.
Pixel-space histograms discretize the lines drawn in parallel coordinates into bins - each bin being one pixel (for a total of ''h'' pixels in an axis):
* ''One-Dimensional Axis Histogram'': A vector ''b'' containing the number of lines that start or end at this pixel - see columns A and B in figure 1.
* ''One-Dimensional Distance Histogram'': A vector ''d'' where each component measures the slope of lines.
* ''Two-Dimensional Axis Pair Histogram'': A matrix where each cell ''xi,j'' states that ''n'' lines are going from pixel ''i'' to pixel ''j'' - see matrix in figure 1.

The metrics proposed can be used for measuring different data properties: ''correlation'' (number of line crossings, angles of crossing), ''aggregation'' (parallelism), ''many-to-one/one-to-many relationships'' (convergence, divergence), ''quality'' (over-plotting), ''information density'' (pixel-based entropy).

One of the most useful optimization is the line crossing and the parallelism metrics. Angels of crossing helps out, where line crossing gets difficult to read.
The convergence-divergence metric works very well for categorical axes and the pixel based entropy optimizes the alpha value, which is useful on larger datasets.

;Number of Line Crossings
:This metric intuitively just does what the name implies, count how many line crossings there are between two axes of the parallel coordinates. The count is efficiently calculated by using intervals as proposed by [Allen, 1983; Rit 1986] with a complexity of ''O(h4)''. This count is then normalized by the maximum number of possible crossings in order to compare the metric with different axis combinations. [Dasgupta and Kosara, 2010]

;Angles of Crossing
:Lines crossing at flat angles tend to create clutter [Dasgupta and Kosara, 2010]. To calculate this metric, first the crossing angles between every pair of lines which are crossing get calculated. From the results of these calculations the median crossing angle is obtained to be used as the resulting metric value. See the right histograms in figure 2. [[Image:Dasgupta+Kosara_Distance+angle-histograms.png|thumb|300px|Figure 2: "Distance histograms (left half of each cell below the parallel coordinates) and angles of crossings (right half) histograms for different dimensions of the cars data." See [Dasgupta and Kosara, 2010] page 1021.]]

;Parallelism
:A pair of lines which is not crossing is parallel to each other. Parallelism is often prefered as a metric compared to the number of crossings because it tends to produce less clutter [Dasgupta and Kosara, 2010]. Parallelism is calculated by taking the inverse of the absolute interquartile range of line distances.
:Narrowly distributed histograms have high parallelism. See the left histograms figure 2. Between horsepower and weight there is high parallelism. Between the other axes not.

;Mutual Information
:The information we are handling with is task oriented and difficult to model. There exists a dependency between variables and mutual information, which tries to measure this relationship.
:If the Value is zero the variables are conditionally independent. But it doesn't imply independence, because you could miss some dependencies.
:Therefor Pargnostics treats the data dimensions as random variables and in this case it uses the two-dimensional axis histogram to denote the joint probability of random variables.

;Convergence, Divergence
:Lines from the left axis joining in single points on the right axis are converging. Divergence is the inverse of this - i.e. lines from the right joining on the left.

;Over-plotting
:Over-plotting measures how many lines are aggregated on a single pixel when the parallel coordinates are drawn. This metric is directly dependent on the number of bins (pixels) used for an axis [Dasgupta and Kosara, 2010]. The value obtained by this metric should be minimized to increase the quality of the visualization.

;Pixel-based Entropy
:TBD
:TBD

=== Dimension Order Optimization ===

Using the metrics from above helps to find an optimization of the visualization display.
This is an NP-complete problem, but by using a binned data model and by considering the special properties of parallel coordinates it's possible to reduce the amount of time which is needed to find optimal solutions [Dasgupta and Kosara, 2010].
Using a branch-and-bound algorithm also can reduce the time necessary.

The purpose of the branch-and-bound algorithm is to find the optimal order of axes.
To do that a matrix of all axis pairs and their associated costs gets constructed.
The costs are a combination of several metrics.

The construction of the matrix has to be done only once at the beginning of the algorithm.

==== Axis Inversions ====

While constructing the matrix of axis pairs both the inverted and noninverted situation for every axis pair is taken into account.
The state - inverted or not inverted - with the lower cost is used in the matrix and the algorithm keeps track which one it was.
This happens locally so inverting one axis pair doesn't have an immediate effect on other axis pairs.

==== Branch-and-Bound Optimization ====

The branch-and-bound algorithm uses a priority queue and best-first search.
For that kind of implementations it's very important to make precise estimates, which subtrees can be culled and which can't.
Since these estimates are based on the full cost matrix, which is constructed at the beginning of the algorithm, they are indeed very precise.

Based on case studies by Dasgupta and Kosara [2010] the branch-and-bound algorithm finds the optimal solution really quick.
The main reason for this is that the metrics are constructed for every axis pair on their own and not for every possible combination of the axis pairs in the entire visualization.

=== Conclusion ===

{{Quotation|In this sense, Pargnostics fills a gap in the existing literature on parallel coordinates. Being able to analyze what ends up on the screen makes it possible to provide better visualization setups that take the specific properties of the visualization technique into account.|Dasgupta and Kosara, 2010}}

The metrics and the optimization explained above are an important step towards better visualiztions.
This metrics not only describe the image which is rendered to screen, but also the different visual structures which can be seen in this image.

=== References ===

*[Allen, 1983] J. Allen. Maintaining knowledge about temporal intervals. ''Communications of the ACM'', 26:832-843, 1983.
*[Dasgupta and [[Kosara, Robert|Kosara]], 2010] Aritra Dasgupta and [[Kosara, Robert|Robert Kosara]]. Pargnostics: Screen-Space Metrics for Parallel Coordinates. ''IEEE Transactions on Visualization and Computer Graphics'', 16(6):1017-1026, November/December 2010.
*[Rit, 1986] J.-F. Rit. Propagating temporal constraints for scheduling. In ''Proceedings of the Fifth National Conference on Artificial Intelligence'', pages 383-388, 1986

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 2

2010-11-17T22:11:53Z

UE-InfoVis1011 0526223:

== Pargnostics: Screen-Space Metrics for Parallel Coordinates ==

=== Introduction ===

Visualization still takes place in a space with a limited number of discrete pixels.
The result of this often is over-plotting, clutter or other things.
Most of the time this visual structures are avoided.
But sometimes this artifacts can be useful, because they might point out interesting structures in the data.
Good visualizations have to show the relevant information at the first glance and therefore show the information in a clear structure.
Until now not a lot of attention is paid to the way a visualzation is presented on the screen.
For the case of parallel coordinates so called Pargnostics (Parallel coordinates diagnostics) should act as a bridge between the created visualization and the perceptual system of the user.
Based on metrics it's possible to provide an optimization which maximize or minimize certain visual artifacts. [Dasgupta and Kosara, 2010]

=== Metrics ===

[[Image:Dasgupta+Kosara Pixel-Space Histograms.png|thumb|300px|right|Figure 1: Pixel-space histograms, see [Dasgupta and Kosara, 2010] page 1018.]]
To automatically enhance the analytical tasks of users Dasgupta and Kosara [2010] proposed several metrics.
The metrics are used to measure the properties of parallel coordinates.
First ''pixel-space histograms'' are calculated to optimize the calculation of these metrics.
Pixel-space histograms discretize the lines drawn in parallel coordinates into bins - each bin being one pixel (for a total of ''h'' pixels in an axis):
* ''One-Dimensional Axis Histogram'': A vector ''b'' containing the number of lines that start or end at this pixel - see columns A and B in figure 1.
* ''One-Dimensional Distance Histogram'': A vector ''d'' where each component measures the slope of lines.
* ''Two-Dimensional Axis Pair Histogram'': A matrix where each cell ''xi,j'' states that ''n'' lines are going from pixel ''i'' to pixel ''j'' - see matrix in figure 1.

The metrics proposed can be used for measuring different data properties: ''correlation'' (number of line crossings, angles of crossing), ''aggregation'' (parallelism), ''many-to-one/one-to-many relationships'' (convergence, divergence), ''quality'' (over-plotting), ''information density'' (pixel-based entropy).

One of the most useful optimization is the line crossing and the parallelism metrics. Angels of crossing helps out, where line crossing gets difficult to read.
The convergence-divergence metric works very well for categorical axes and the pixel based entropy optimizes the alpha value, which is useful on larger datasets.

;Number of Line Crossings
:This metric intuitively just does what the name implies, count how many line crossings there are between two axes of the parallel coordinates. The count is efficiently calculated by using intervals as proposed by [Allen, 1983; Rit 1986] with a complexity of ''O(h4)''. This count is then normalized by the maximum number of possible crossings in order to compare the metric with different axis combinations. [Dasgupta and Kosara, 2010]

;Angles of Crossing
:Lines crossing at flat angles tend to create clutter [Dasgupta and Kosara, 2010]. To calculate this metric, first the crossing angles between every pair of lines which are crossing get calculated. From the results of these calculations the median crossing angle is obtained to be used as the resulting metric value. See the right histograms in figure 2. [[Image:Dasgupta+Kosara_Distance+angle-histograms.png|thumb|300px|Figure 2: "Distance histograms (left half of each cell below the parallel coordinates) and angles of crossings (right half) histograms for different dimensions of the cars data." See [Dasgupta and Kosara, 2010] page 1021.]]

;Parallelism
:A pair of lines which is not crossing is parallel to each other. Parallelism is often prefered as a metric compared to the number of crossings because it tends to produce less clutter [Dasgupta and Kosara, 2010]. Parallelism is calculated by taking the inverse of the absolute interquartile range of line distances.
:Narrowly distributed histograms have high parallelism. See the left histograms figure 2. Between horsepower and weight there is high parallelism. Between the other axes not.

;Mutual Information
:The information we are handling with is task oriented and difficult to model. There exists a dependency between variables and mutual information, which tries to measure this relationship.
:If the Value is zero the variables are conditionally independent. But it doesn't imply independence, because you could miss some dependencies.
:Therefor Pargnostics treats the data dimensions as random variables and in this case it uses the two-dimensional axis histogram to visualize them.

;Convergence, Divergence
:Lines from the left axis joining in single points on the right axis are converging. Divergence is the inverse of this - i.e. lines from the right joining on the left.

;Over-plotting
:Over-plotting measures how many lines are aggregated on a single pixel when the parallel coordinates are drawn. This metric is directly dependent on the number of bins (pixels) used for an axis [Dasgupta and Kosara, 2010]. The value obtained by this metric should be minimized to increase the quality of the visualization.

;Pixel-based Entropy
:TBD
:TBD

=== Dimension Order Optimization ===

Using the metrics from above helps to find an optimization of the visualization display.
This is an NP-complete problem, but by using a binned data model and by considering the special properties of parallel coordinates it's possible to reduce the amount of time which is needed to find optimal solutions [Dasgupta and Kosara, 2010].
Using a branch-and-bound algorithm also can reduce the time necessary.

The purpose of the branch-and-bound algorithm is to find the optimal order of axes.
To do that a matrix of all axis pairs and their associated costs gets constructed.
The costs are a combination of several metrics.

The construction of the matrix has to be done only once at the beginning of the algorithm.

==== Axis Inversions ====

While constructing the matrix of axis pairs both the inverted and noninverted situation for every axis pair is taken into account.
The state - inverted or not inverted - with the lower cost is used in the matrix and the algorithm keeps track which one it was.
This happens locally so inverting one axis pair doesn't have an immediate effect on other axis pairs.

==== Branch-and-Bound Optimization ====

The branch-and-bound algorithm uses a priority queue and best-first search.
For that kind of implementations it's very important to make precise estimates, which subtrees can be culled and which can't.
Since these estimates are based on the full cost matrix, which is constructed at the beginning of the algorithm, they are indeed very precise.

Based on case studies by Dasgupta and Kosara [2010] the branch-and-bound algorithm finds the optimal solution really quick.
The main reason for this is that the metrics are constructed for every axis pair on their own and not for every possible combination of the axis pairs in the entire visualization.

=== Conclusion ===

{{Quotation|In this sense, Pargnostics fills a gap in the existing literature on parallel coordinates. Being able to analyze what ends up on the screen makes it possible to provide better visualization setups that take the specific properties of the visualization technique into account.|Dasgupta and Kosara, 2010}}

The metrics and the optimization explained above are an important step towards better visualiztions.
This metrics not only describe the image which is rendered to screen, but also the different visual structures which can be seen in this image.

=== References ===

*[Allen, 1983] J. Allen. Maintaining knowledge about temporal intervals. ''Communications of the ACM'', 26:832-843, 1983.
*[Dasgupta and [[Kosara, Robert|Kosara]], 2010] Aritra Dasgupta and [[Kosara, Robert|Robert Kosara]]. Pargnostics: Screen-Space Metrics for Parallel Coordinates. ''IEEE Transactions on Visualization and Computer Graphics'', 16(6):1017-1026, November/December 2010.
*[Rit, 1986] J.-F. Rit. Propagating temporal constraints for scheduling. In ''Proceedings of the Fifth National Conference on Artificial Intelligence'', pages 383-388, 1986

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 2

2010-11-17T22:04:35Z

UE-InfoVis1011 0526223:

== Pargnostics: Screen-Space Metrics for Parallel Coordinates ==

=== Introduction ===

Visualization still takes place in a space with a limited number of discrete pixels.
The result of this often is over-plotting, clutter or other things.
Most of the time this visual structures are avoided.
But sometimes this artifacts can be useful, because they might point out interesting structures in the data.
Good visualizations have to show the relevant information at the first glance and therefore show the information in a clear structure.
Until now not a lot of attention is paid to the way a visualzation is presented on the screen.
For the case of parallel coordinates so called Pargnostics (Parallel coordinates diagnostics) should act as a bridge between the created visualization and the perceptual system of the user.
Based on metrics it's possible to provide an optimization which maximize or minimize certain visual artifacts. [Dasgupta and Kosara, 2010]

=== Metrics ===

[[Image:Dasgupta+Kosara Pixel-Space Histograms.png|thumb|300px|right|Figure 1: Pixel-space histograms, see [Dasgupta and Kosara, 2010] page 1018.]]
To automatically enhance the analytical tasks of users Dasgupta and Kosara [2010] proposed several metrics.
The metrics are used to measure the properties of parallel coordinates.
First ''pixel-space histograms'' are calculated to optimize the calculation of these metrics.
Pixel-space histograms discretize the lines drawn in parallel coordinates into bins - each bin being one pixel (for a total of ''h'' pixels in an axis):
* ''One-Dimensional Axis Histogram'': A vector ''b'' containing the number of lines that start or end at this pixel - see columns A and B in figure 1.
* ''One-Dimensional Distance Histogram'': A vector ''d'' where each component measures the slope of lines.
* ''Two-Dimensional Axis Pair Histogram'': A matrix where each cell ''xi,j'' states that ''n'' lines are going from pixel ''i'' to pixel ''j'' - see matrix in figure 1.

The metrics proposed can be used for measuring different data properties: ''correlation'' (number of line crossings, angles of crossing), ''aggregation'' (parallelism), ''many-to-one/one-to-many relationships'' (convergence, divergence), ''quality'' (over-plotting), ''information density'' (pixel-based entropy).

One of the most useful optimization is the line crossing and the parallelism metrics. Angels of crossing helps out, where line crossing gets difficult to read.
The convergence-divergence metric works very well for categorical axes and the pixel based entropy optimizes the alpha value, which is useful on larger datasets.

;Number of Line Crossings
:This metric intuitively just does what the name implies, count how many line crossings there are between two axes of the parallel coordinates. The count is efficiently calculated by using intervals as proposed by [Allen, 1983; Rit 1986] with a complexity of ''O(h4)''. This count is then normalized by the maximum number of possible crossings in order to compare the metric with different axis combinations. [Dasgupta and Kosara, 2010]

;Angles of Crossing
:Lines crossing at flat angles tend to create clutter [Dasgupta and Kosara, 2010]. To calculate this metric, first the crossing angles between every pair of lines which are crossing get calculated. From the results of these calculations the median crossing angle is obtained to be used as the resulting metric value. See the right histograms in figure 2. [[Image:Dasgupta+Kosara_Distance+angle-histograms.png|thumb|300px|Figure 2: "Distance histograms (left half of each cell below the parallel coordinates) and angles of crossings (right half) histograms for different dimensions of the cars data." See [Dasgupta and Kosara, 2010] page 1021.]]

;Parallelism
:A pair of lines which is not crossing is parallel to each other. Parallelism is often prefered as a metric compared to the number of crossings because it tends to produce less clutter [Dasgupta and Kosara, 2010]. Parallelism is calculated by taking the inverse of the absolute interquartile range of line distances.
:Narrowly distributed histograms have high parallelism. See the left histograms figure 2. Between horsepower and weight there is high parallelism. Between the other axes not.

;Mutual Information
The information we are handling with is task oriented and difficult to model. There exists a dependency between variables and mutual information, which tries to measure this relationship.
If the Value is zero the variables are conditionally independent. But it doesn't imply independence, because you could miss some dependencies.
Therefor Pargnostics treats the data dimensions as random variables and in this case it uses the two-dimensional axis histogram to visualize them.

;Convergence, Divergence
:Lines from the left axis joining in single points on the right axis are converging. Divergence is the inverse of this - i.e. lines from the right joining on the left.

;Over-plotting
:Over-plotting measures how many lines are aggregated on a single pixel when the parallel coordinates are drawn. This metric is directly dependent on the number of bins (pixels) used for an axis [Dasgupta and Kosara, 2010]. The value obtained by this metric should be minimized to increase the quality of the visualization.

;Pixel-based Entropy
:TBD
:TBD

=== Dimension Order Optimization ===

Using the metrics from above helps to find an optimization of the visualization display.
This is an NP-complete problem, but by using a binned data model and by considering the special properties of parallel coordinates it's possible to reduce the amount of time which is needed to find optimal solutions [Dasgupta and Kosara, 2010].
Using a branch-and-bound algorithm also can reduce the time necessary.

The purpose of the branch-and-bound algorithm is to find the optimal order of axes.
To do that a matrix of all axis pairs and their associated costs gets constructed.
The costs are a combination of several metrics.

The construction of the matrix has to be done only once at the beginning of the algorithm.

==== Axis Inversions ====

While constructing the matrix of axis pairs both the inverted and noninverted situation for every axis pair is taken into account.
The state - inverted or not inverted - with the lower cost is used in the matrix and the algorithm keeps track which one it was.
This happens locally so inverting one axis pair doesn't have an immediate effect on other axis pairs.

==== Branch-and-Bound Optimization ====

The branch-and-bound algorithm uses a priority queue and best-first search.
For that kind of implementations it's very important to make precise estimates which subtrees can be culled and which can't.
Since these estimates are based on the full cost matrix which is constructed at the beginning of the algorithm they are indeed very precise.

Based on case studies by Dasgupta and Kosara [2010] the branch-and-bound algorithm finds the optimal solution really quick.
The main reason for this is that the metrics are constructed for every axis pair on their own and not for every possible combination of the axis pairs in the entire visualization.

=== Conclusion ===

{{Quotation|In this sense, Pargnostics fills a gap in the existing literature on parallel coordinates. Being able to analyze what ends up on the screen makes it possible to provide better visualization setups that take the specific properties of the visualization technique into account.|Dasgupta and Kosara, 2010}}

The metrics and the optimization explained above are an important step towards better visualiztions.
This metrics not only describe the image which is rendered to screen, but also the different visual structures which can be seen in this image.

=== References ===

*[Allen, 1983] J. Allen. Maintaining knowledge about temporal intervals. ''Communications of the ACM'', 26:832-843, 1983.
*[Dasgupta and [[Kosara, Robert|Kosara]], 2010] Aritra Dasgupta and [[Kosara, Robert|Robert Kosara]]. Pargnostics: Screen-Space Metrics for Parallel Coordinates. ''IEEE Transactions on Visualization and Computer Graphics'', 16(6):1017-1026, November/December 2010.
*[Rit, 1986] J.-F. Rit. Propagating temporal constraints for scheduling. In ''Proceedings of the Fifth National Conference on Artificial Intelligence'', pages 383-388, 1986

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 2

2010-11-17T21:33:39Z

UE-InfoVis1011 0526223:

== Pargnostics: Screen-Space Metrics for Parallel Coordinates ==

=== Introduction ===

Visualization still takes place in a space with a limited number of discrete pixels.
The result of this often is over-plotting, clutter or other things.
Most of the time this visual structures are avoided.
But sometimes this artifacts can be useful, because they might point out interesting structures in the data.
Good visualizations have to show the relevant information at the first glance and therefore show the information in a clear structure.
Until now not a lot of attention is paid to the way a visualzation is presented on the screen.
For the case of parallel coordinates so called Pargnostics (Parallel coordinates diagnostics) should act as a bridge between the created visualization and the perceptual system of the user.
Based on metrics it's possible to provide an optimization which maximize or minimize certain visual artifacts. [Dasgupta and Kosara, 2010]

=== Metrics ===

[[Image:Dasgupta+Kosara Pixel-Space Histograms.png|thumb|300px|right|Figure 1: Pixel-space histograms, see [Dasgupta and Kosara, 2010] p1018]]
To automatically enhance the analytical tasks of users Dasgupta and Kosara [2010] proposed several metrics.
The metrics are used to measure the properties of parallel coordinates.
For optimize calculation of these metrics, first ''pixel-space histograms'' are calculated.
Pixel-space histograms discretize the lines drawn in parallel coordinates into bins - each bin being one pixel (for a total of ''h'' pixels in an axis):
* ''One-Dimensional Axis Histogram'': A vector ''b'' containing the number of lines that start or end at this pixel - see columns A and B in figure 1.
* ''One-Dimensional Distance Histogram'': A vector ''d'' where each component measures the slope of lines.
* ''Two-Dimensional Axis Pair Histogram'': A matrix where each cell ''xi,j'' means that ''n'' lines are going from pixel ''i'' to pixel ''j'' - see matrix in figure 1.

The metrics proposed can be used for measuring different data properties: ''correlation'' (number of line crossings, angles of crossing), ''aggregation'' (parallelism), ''many-to-one/one-to-many relationships'' (convergence, divergence), ''quality'' (over-plotting), ''information density'' (pixel-based entropy).

One of the most useful optimization is the line crossing and the parallelism metrics. Angels of crossing helps out, where line crossing gets difficult to read.
The convergence-divergence metric works for categorical axes very well and the pixel based entropy optimizes the alpha value, which is useful on larger datasets.

;Number of Line Crossings
:This metric intuitively just does what the name implies, count how many line crossings there are between two axes of the prallel coordinates. The count is efficiently calculated by using intervals as proposed by [Allen, 1983; Rit 1986] with a complexity of ''O(h4)''. This count is then normalized by the maximum number of possible crossings in order compare the metric between different axis combinations. [Dasgupta and Kosara, 2010]

;Angles of Crossing
:Lines crossing at flat angles tend to create clutter [Dasgupta and Kosara, 2010]. To calculate this metric, first the crossing angles between every pair of lines which are crossing get calculated. From the results of these calculations the median crossing angle is obtained to be used as the resulting metric value.

;Parallelism
:A pair of lines which is not crossing is parallel to each other. Parallelism is often prefered as a metric compared to the number of crossings because it tends to produce less clutter [Dasgupta and Kosara, 2010]. Parallelism is calculated by taking the inverse of the absolute interquartile range of line distances.

;Mutual Information
:TBD
:TBD

;Convergence, Divergence
:Lines from the left axis joining in single points on the right axis are converging. Divergence is the inverse of this - i.e. lines from the right joining on the left.

;Over-plotting
:Over-plotting measures how many lines are aggregated on a single pixel when the parallel coordinates are drawn. This metric is directly dependent on the number of bins (pixels) used for an axis [Dasgupta and Kosara, 2010]. The value obtained by this metric should be minimized to increase the quality of the visualization.

;Pixel-based Entropy
:TBD
:TBD

=== Dimension Order Optimization ===

Using the metrics from above helps to find an optimization of the visualization display.
This is an NP-complete problem, but by using a binned data model and by considering the special properties of parallel coordinates it's possible to reduce the amount of time which is needed to find optimal solutions [Dasgupta and Kosara, 2010].
Using a branch-and-bound algorithm also can reduce the time necessary.

The purpose of the branch-and-bound algorithm is to find the optimal order of axes.
To do that a matrix of all axis pairs and their associated costs gets constructed.
The costs are a combination of several metrics.

The construction of the matrix has to be done only once at the beginning of the algorithm.

=== Axis Inversions ===

While constructing the matrix of axis pairs both the inverted and noninverted situation for every axis pair is taking into account.
The situation(inverted or noninverted) with the lower costs gets used in the matrix and the algorithm keeps track which one that was.
This happens locally. So inverting one axis pair doesn't have an immediate effect on other axis pairs.

=== Branch-and-Bound Optimization ===

The Branch-and-Bound algorithm uses a priority queue and best-first search. For that kind of implemantions it's very important to make precise estimates which subtrees can be culled and which can't. Since these estimates are based on the full cost matrix constructed at the beginning of the algorithm they are indeed very precise.

Based on case studies by Dasgupta and Kosara [2010] the branch-and-bound algorithm finds the optimal solution really quick.
The main reason for this is that the metrics are constructed for every axis pair on their own and not for every possible combination of the axis pairs in the entire visualization.

=== Conclusion ===

{{Quotation|In this sense, Pargnostics fills a gap in the existing literature on parallel coordinates. Being able to analyze what ends up on the screen makes it possible to provide better visualization setups that take the specific properties of the visualization technique into account.|Dasgupta and Kosara, 2010}}

The metrics and the optimization explained above are a really important step towards better visualiztions.
This metrics not only describe the image which is rendered to screen, but also the different visual structures which can be seen in this image.

=== References ===

*[Allen, 1983] J. Allen. Maintaining knowledge about temporal intervals. ''Communications of the ACM'', 26:832-843, 1983.
*[Dasgupta and [[Kosara, Robert|Kosara]], 2010] Aritra Dasgupta and [[Kosara, Robert|Robert Kosara]]. Pargnostics: Screen-Space Metrics for Parallel Coordinates. ''IEEE Transactions on Visualization and Computer Graphics'', 16(6):1017-1026, November/December 2010.
*[Rit, 1986] J.-F. Rit. Propagating temporal constraints for scheduling. In ''Proceedings of the Fifth National Conference on Artificial Intelligence'', pages 383-388, 1986

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 2

2010-11-17T20:33:25Z

UE-InfoVis1011 0526223:

== Pargnostics: Screen-Space Metrics for Parallel Coordinates ==

=== Introduction ===

Visualization still takes place in a space with a limited number of discrete
pixels. The result of this often is over-plotting, clutter or other things.
Most of the time this structures are avoided. But sometimes this artifacts can be useful, because they might point out interesting structures in the data.
Good Visuals have to show the relevant information at the first glance and therefor they have to show the information in a clear structure.
Until know not a lot of attention is paid to the way visualzation is presented on the screen.
For the case of parallel coordinates so called Pargnostics (Parallel coordinates diagnostics) should act as a bridge between the created visualization and the perceptual system of the user.
Based on metrics it's possible to provide an optimization which maximize or minimize certain visual artifacts.

=== Metrics ===

[[Image:Dasgupta+Kosara Pixel-Space Histograms.png|thumb|300px|right|Figure 1: Pixel-space histograms, see [Dasgupta and Kosara, 2010] p1018]]
To automatically enhance the analytical tasks of users Dasgupta and Kosara [2010] proposed several metrics.
The metrics are used to measure the properties of parallel coordinates.
For calculating these metrics, first ''pixel-space histograms'' need to be calculated.
Pixel-space histograms discretize the lines drawn in parallel coordinates into bins - each bin being one pixel (for a total of ''h'' pixels in an axis):
* ''One-Dimensional Axis Histogram'': A vector ''b'' containing the number of lines that start or end at this pixel - see columns A and B in figure 1.
* ''One-Dimensional Distance Histogram'': A vector ''d'' where each component measures the slope of lines.
* ''Two-Dimensional Axis Pair Histogram'': A matrix where each cell ''xi,j'' means that ''n'' lines are going from pixel ''i'' to pixel ''j'' - see matrix in figure 1.

The metrics proposed can be used for measuring different data properties: ''correlation'' (number of line crossings, angles of crossing), ''aggregation'' (parallelism), ''many-to-one/one-to-many relationships'' (convergence, divergence), ''quality'' (over-plotting), ''information density'' (pixel-based entropy).

==== Number of Line Crossings ====

The first metric proposed by Dasgupta and Kosara [2010] is ''number of line crossings''.
This intuitively just does what the name implies, count how many line crossings there are between two axes of the prallel coordinates.
The count is efficiently calculated by using intervals as proposed by [Allen, 1983; Rit 1986] with a complexity of ''O(h4)''.
This count is then normalized by the maximum number of possible crossings in order compare the metric between different axis combinations.

==== Angles of Crossing ====

The number of line crossings is one one of the most important metrics.
But according to Dasgupta and Kosara [2010] the angles of the line crossings are an equally important metric.
Lines crossing at flat angles tend to create clutter.
But how does this metric gets calculated?
First the crossing angles between every pair of lines which are crossing get calculated.
Afterwards the median crossing angle gets calculated. This median can be used for optimizations.

==== Parallelism ====

Another metric is Parallelism. A pair of lines which is not crossing is parallel to each other.
Parallelism can for example show clusters within a subset of the data.
Parallelism is often prefered as a metric compared to the number of crossings because it tends to produce less clutter.

==== Mutual Information ====

==== Convergence, Divergence ====

==== Over-plotting ====

==== Pixel-based Entropy ====

=== Dimension Order Optimization ===

Using the metrics from above helps to find an optimization of the visualization display.
Normally this would be a NP-complete problem but by using a binned data model and by considering the special properties of parallel coordinates it's possible to reduce the amount of time which is needed to find optimal solutions. Using a branch-and-bound algorithm also can reduce the necessary time.

The porpuse of the branch-and-bound algorithm is to find the optimal order of axes.
To do that a matrix of all axis pairs and their associated costs gets constructed.
The costs are a combination of several metrics.

The construction of the matrix has to be done only once at the beginning of the algorithm.

=== Axis Inversions ===

While constructing the matrix of axis pairs both the inverted and noninverted situation for every axis pair is taking into account.
The situation(inverted or noninverted) with the lower costs gets used in the matrix and the algorithm keeps track which one that was.
This happens locally. So inverting one axis pair doesn't have an immediate effect on other axis pairs.

=== Branch-and-Bound Optimization ===

The Branch-and-Bound algorithm uses a priority queue and best-first search. For that kind of implemantions it's very important to make precise estimates which subtrees can be culled and which can't. Since these estimates are based on the full cost matrix constructed at the beginning of the algorithm they are indeed very precise.

Based on case studies by Dasgupta and Kosara [2010] the branch-and-bound algorithm finds the optimal solution really quick.
The main reason for this is that the metrics are constructed for every axis pair on their own and not for every possible combination of the axis pairs in the entire visualization.

=== Conclusion ===

<blockquote>"In this sense, Pargnostics fills a gap in the existing literature
on parallel coordinates. Being able to analyze what ends up on
the screen makes it possible to provide better visualization setups that
take the specific properties of the visualization technique into account."</blockquote>
<center>[Dasgupta and Kosara, 2010]</center>

The metrics and the optimization explained above are a really importan step towards better visualiztions.
This metrics not only describe the image which is rendered to screen, but also the different visual structures which can be seen in this image.

=== References ===

*[Allen, 1983] J. Allen. Maintaining knowledge about temporal intervals. ''Communications of the ACM'', 26:832-843, 1983.
*[Dasgupta and [[Kosara, Robert|Kosara]], 2010] Aritra Dasgupta and [[Kosara, Robert|Robert Kosara]]. Pargnostics: Screen-Space Metrics for Parallel Coordinates. ''IEEE Transactions on Visualization and Computer Graphics'', 16(6):1017-1026, November/December 2010.
*[Rit, 1986] J.-F. Rit. Propagating temporal constraints for scheduling. In ''Proceedings of the Fifth National COnference on Artificial Intelligence'', pages 383-388, 1986

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 2

2010-11-16T23:22:11Z

UE-InfoVis1011 0526223:

User:UE-InfoVis1011 0526223

2010-10-19T20:34:36Z

UE-InfoVis1011 0526223:

<big>'''Jasin Alili'''</big>, ... 

[[Image:jasin.jpg|200px]]

=== InfoVis ===
*[[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 | Gruppe 05 (Alili, Marschik, Bachhuber)]]

=== TU Vienna ===
[http://www.tuwien.ac.at Vienna University of Technology] 

=== External links ===
*[http://wu.ac.at/io - WU Zentrum für Auslandsstudien] 

[[Category:Persons]]

User:UE-InfoVis1011 0526223

2010-10-19T20:33:58Z

UE-InfoVis1011 0526223:

<big>'''Jasin Alili'''</big>, ... 

[[Image:jasin.jpg|200px]]

=== InfoVis ===
*[[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 | Gruppe 05 (Alili, Marschik, Bachhuber)]]

=== TU Vienna ===
[http://www.tuwien.ac.at Vienna University of Technology] 
[http://wu.ac.at/io ZAS - Zentrum für Auslandsstudien ] 

=== External links ===
*[http://wu.ac.at/io - WU Zentrum für Auslandsstudien] 

[[Category:Persons]]

User:UE-InfoVis1011 0526223

2010-10-19T20:32:47Z

UE-InfoVis1011 0526223:

<big>'''Jasin Alili'''</big>, ... 

[[Image:jasin.jpg|200px]]

=== InfoVis ===
[[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 | Gruppe 05 (Alili, Marschik, Bachhuber)]]

=== TU Vienna ===
[http://www.tuwien.ac.at Vienna University of Technology] 
[http://wu.ac.at/io ZAS - Zentrum für Auslandsstudien ] 

== External links ==
*[http://wu.ac.at/io - WU Zentrum für Auslandsstudien] 

[[Category:Persons]]

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05

2010-10-19T20:27:51Z

UE-InfoVis1011 0526223:

Gruppe 05 für die [[Teaching:TUW_-_UE_InfoVis_WS_2010/11|UE InfoVis WS 2010/11]]

=Gruppenmitglieder=
* [[User:UE-InfoVis1011_0625039|Marschik, Patrick]]
* [[User:UE-InfoVis1011_0526223|Alili, Jasin]]
* Bachhuber, Ben

=Aufgabe=
* [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 1|Aufgabe 1]] (siehe [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe1.html])
* [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 2|Aufgabe 2]] (siehe [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe2.html])
* [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 3|Aufgabe 3]] (siehe [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe3.html])

User:UE-InfoVis1011 0526223

2010-10-18T14:40:37Z

UE-InfoVis1011 0526223:

<big>'''Jasin Alili'''</big>, ... 

[[Image:jasin.jpg|200px]]

=== InfoVis ===
[[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 (Jasin, Patrik, ???)|Gruppe 05]]

=== TU Vienna ===
[http://www.tuwien.ac.at Vienna University of Technology] 
[http://wu.ac.at/io ZAS - Zentrum für Auslandsstudien ] 

== External links ==
*[http://wu.ac.at/io - WU Zentrum für Auslandsstudien] 

[[Category:Persons]]

User:UE-InfoVis1011 0526223

2010-10-18T14:36:47Z

UE-InfoVis1011 0526223:

<big>'''Jasin Alili'''</big>, ... 

[[Image:jasin.jpg|200px]]

== Affiliation ==

=== InfoVis ===
[[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 (Jasin, Patrik, ???)|Gruppe 05]]

=== TU Vienna ===
[http://www.tuwien.ac.at Vienna University of Technology] 
[http://wu.ac.at/io ZAS - Zentrum für Auslandsstudien ] 

=== Current Project(s) ===

== Contact ==

== External links ==
*[http://wu.ac.at/io - WU Zentrum für Auslandsstudien] 

[[Category:Persons]]

User:UE-InfoVis1011 0526223

2010-10-18T14:35:45Z

UE-InfoVis1011 0526223:

File:Jasin.jpg

2010-10-18T14:27:23Z

UE-InfoVis1011 0526223: it's mee

== Summary ==
it's mee
== Copyright status ==

== Source ==

User:UE-InfoVis1011 0526223

2010-10-18T14:26:54Z

UE-InfoVis1011 0526223:

<big>'''Jasin Alili'''</big>, zukünftiger DI hoffentlich 

[[Image:jasin.jpg|200px]]

== Affiliation ==

=== InfoVis ===
[[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 (Jasin, Patrik, ???)|Gruppe 05]]

=== TU Vienna ===
[http://www.tuwien.ac.at Vienna University of Technology] 
[http://wu.ac.at/io ZAS - Zentrum für Auslandsstudien ] 

=== Current Project(s) ===

== Contact ==

== External links ==
*[http://wu.ac.at/io]

[[Category:Persons]]

User:UE-InfoVis1011 0526223

2010-10-18T14:24:10Z

UE-InfoVis1011 0526223: New page: <big>'''Jasin Alili'''</big>, zukünftiger DI hoffentlich 200px == Affiliation == === InfoVis === [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 (Jas...

<big>'''Jasin Alili'''</big>, zukünftiger DI hoffentlich 

[[Image:SEmrich.jpg|200px]]

== Affiliation ==

=== InfoVis ===
[[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 (Jasin, Patrik, ???)|Gruppe 05]]

=== TU Vienna ===
[http://www.tuwien.ac.at Vienna University of Technology] 
[http://wu.ac.at/io ZAS - Zentrum für Auslandsstudien ] 

=== Current Project(s) ===

== Contact ==

== External links ==
*[http://wu.ac.at/io]

[[Category:Persons]]