InfoVis:Wiki - User contributions [en]

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 3

2011-01-17T20:02:08Z

InfoVis1011 9925916:

= Information Visualization, Task 3 =
This article is the result of [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01|group 1]] for [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe3.html task 3] of the course [http://www.ifs.tuwien.ac.at/~silvia/wien/vu-infovis/ information visualisation] at [http://www.tuwien.ac.at Vienna University of Technology] in the winter semester 2010/2011.

= Data =
The data for this task is provided by the [http://www.wssinfo.org Joint Monitoring Program for Water Supply and Sanitation (JMP)] of the [http://www.who.int/en/ World Health Organization (WHO)] and the [http://www.unicef.org/ UN Children’s Fund (UNICEF)]. It contains information about access to (drinking) water and sanitary standards in countries all over the globe, intended to monitor the process towards the [http://mdgs.un.org/unsd/mdg/Default.aspx Millennium Development Goals (MDG)].

== Area of Application ==
This being a fairly ambitious program requires close analysis of the data. This being especially necessary as it is clear (and also stated so by the JMP) that the acquisition of such data is hindered by infrastructural (numerous measurement standards, difficulty to reach places for a significant sample, etc.) and financial shortcomings. Expectedly the samples are not complete. Several countries lack one or more variables either completely or partially (over sample period 1990-2008). To overcome these problems, solutions had to be found for the individual visualizations.

== Analysis of Dataset ==
The dataset provided is structured temporally (time periods in which the samples were taken) as well as ordinally (for measurement of water quality so called drinking-water and sanitation ladders have been defined [JMP, 2010]: piped water sources are better than other improved which again are better than unimproved sources) and it contains quantitative samples. Nominal values tell about the region a sample is related to. The samples themselves are absolute and/or relative figures. Thus the dataset at hand is multidimensional and temporally structured.

The given information was extended by the (nominal) information to which continent a country belongs. This implicit structure adds a hierarchy, as the countries can be summed up to continents – a fact that is necessary or at least favorable for some visualization techniques.

== Potential Users ==
By the nature of the data processed in this task, the potential audience is an extremly wide one. In order to serve a large part of it a multi-step approach was chosed for the presentation of the data. These steps target not only different interrest groups but also help to gain an overview as well as an understanding and feel for the details of the data and their implications for countries development towards the millenium goals.

Since not even academic audience usually has the same skill in reading and understanding graphs and data (e.g. a doctor and a statistician) this has been considered when creating the visualisation applications. This also explains why there is no ''best order'' in which to explore those apps. For example might a statistician first choose to view the scatterplot and by this gain an overview of the distribution of data and correlations within the sample. Development workers on the other hand will likely be interrested in absolute numbers of people that lack access to improved water to decide where to deploy missions for improvement. And a politian or decision maker could want to have relative figures to base her or his decission for further support programms on them.

For this reason a webpage has been developed that gives acces to the three developed applications (including a brief description) and as such makes it possible for a broad audience to view the data they are (personally or professionally) interrested in.

= Objectives =
According to [JMP, 2010] a main goal is to identify disparities in development of different regions, so well-directed actions can be taken. The visualization shall support to examine:
* Which regions are left behind?
* Differences between urban and rural regions
* Differences between provided services (water/sanitation)
* Performance of different regions (with respect to different baselines where they started from)

= Visualization =

In the visualization solutions supplied, several different techniques are utilized to present the data:
* Bar chart
* Line chart
* Stacked time series
* Scatterplots

Based upon examples of the Protovis toolkit, interactive charts have been enhanced, adopted or interlinked in order to become more powerful. The driving idea behind this was Ben Shneidermans information seeking matra [Shneiderman, 1996]: "Overview first, zoom and filter, then details-on-deman"

== Development of Drinking Water Supply ==

=== Visual Mapping ===

==== Bar Chart ====
[[Image:Watervisbarchart.png | 350px | thumb | '''Figure 1''' : ''Bar chart'']]
In bar charts the length of the respective bar expresses the corresponding value of the variable. The advantage of this technique is the ability to feature individual values and support comparison of one value to another [Few, 2007, p. 3]. In the given example in figure 1 a ranking of the improvement/decline of improved drinking water access in the time period between 1990 and 2008 has been made.

In addition a bar can easily be used to express a second variable by color. This complies with Tufte´s design principle of multifunctional graphical elements [Tufte, 1983, p. 138]: “Mobilize every graphical element, perhaps several times over, to show the data”. Color is used to show the increase/decrease of population in the corresponding country. This technique makes it possible for the user to see correlations between variables (in this case improvement of drinking water and demographics) as well as differences between different groups (e.g. urban and rural).

==== Line Chart ====
Line chart are suitable to show development, and subsequently change, over time. When using bar charts, the overall shape of change gets lost in a forest of bars [Few, 2007, p.3]. Thus line charts are used to display evolution over the years.

=== Interaction ===

Following interaction techniques have been applied for this purpose:
* Overview and details on demand
* Filter

===== Overview and Detail on Demand =====
Overview and detail shows the data at more than one level, but they also show where the finer grain display fits into the larger grain display. Their main disadvantage is that they require coordination of two visual domains [Card, 1996]. In this visualizations overview is used to provide the big picture, displaying all countries. When pointing with the mouse at an element, the corresponding detail data is shown in a different view.

===== Filter =====
With filter functions the user can select different subsets of the whole dataset. By allowing users to control the content of the display, users can quickly focus on their interests by eliminating unwanted items [Shneiderman, 1996]. In the visualization the user can filter regions and parts of the population (urban/rural), by doing this interesting aspects of the dataset can be found (for example improvements in rural South America).

== Proportion of Regions with Access to Unimproved Water Sources ==
[[Image:Watervis-stackedtimeseries.jpg | 350px | thumb | '''Figure 2''' : ''Stacked time series'']]
As pointed out above, the focus was set upon development of regions over time. In this application visualization was restricted to utilization of unimproved water sources and its development. Of course this can be easily extended onto other variables. Important aspects are: how do different regions develop, and which regions are concerned most?

=== Visual Mapping ===

==== Hierarchical Stacked Time Series ====

According to [Few, 2006] lines can be used for time series to emphasize overall patterns, with time being placed on the horizontal axis.

The vertical axis is used to show how regions contribute to the total number of people with access to unimproved water sources. Instead of lines filled areas are used to depict the numbers, by this multiple countries can be stacked (adding up to the total number of each time period). The region taking up the most part of the chart is the one with the highest impact.

Color is used to encode the type of the region (urban, rural). And a text-label is used to identify the region.
As suggested in [Ward et al. 2010], redundant mapping is used to emphasize important data: regions with higher impact. For this labels are scaled proportionally, and color-saturation expresses the influence of the country/region on the total number.

==== Bar Chart ====
So far this chart does not show if the number of a region actually decreases or increases, as the numbers are compared to the total number per year. Thus it is linked to a bar chart. This chart again places Time on the horizontal axis, and quantity on the vertical –although absolute figures. The bars are used to display the figures of the country/region the mouse hovers over. For comparison reasons each year’s global total is also displayed.

=== Interaction ===
At any point the user can filter the shown data in the following ways:
* Choose if rural, urban, or total data of the countries/regions is shown
* Filter the countries/regions by their name, via search query

The first view shows the development of continents. The user can inspect how countries contribute to a continents’ development by clicking on the continent (zooming in). This brings up the name of the selected continent in the top left corner, providing basic contextual information.

When filtering and zooming, the scale is adjusted to the amount of regions presented in order to make regions with small impact visible as well. Moving the mouse over a region selects it for comparison to the total number in the bar chart on the right. This also clarifies the impact of this region to the total number.

== Scatterplot ==
[[Image:ScatterplotKeyvariables.png | 350px | thumb | '''Figure 3''' : ''Scatterplot'']]
=== Data Mapping ===
According to [Mazza, 2009, Table 3.2, p.40] there are three suitable preattentive attributes for the visualization of quantitative data: length and numerosity (form) as well as a spatial position in 2D-space.

Scatterplots, the third representation chosen for the given quantitative data, are based on this last attribute – 2D spatial positioning. Further this “standard scatterplot” is enhanced by utilization of color intensity. This attribute, suitable for (preattentive) visualization of ordinal data [Mazza, 2009, Table 3.2, p.40] is used to visualize the density of samples at a certain position, which translates into an ordinal relation. This additional feature uncovers what would otherwise remain hidden within the amount of data: aggregations of same or similar samples. This also "reduces" the space needed per plot which is one of the shortcomings of scatterplots – especially when plotting multiple variables.

=== Interaction Features ===
According to [Stuart Card, Informaiton Visualisation, p.525] scatterplots are a sensible visualization technique for problems with one or more variables. In the latter case they are usually combine to scatterplot matrices. First the user has to choose the data to be displayed by the scatterplots, which can be either a full-variable display of the water markers or sanitary markers. The third set consists of the total figures of water and sanitation. Each of these three sub-sets also contains the development of the countries’ overall populations and the development of urbanization.

Via brushing with the mouse the user can then select areas/countries within any of the scatterplots. The chosen selection is then highlighted in all the other scatterplots as well. With this a general overview of the data is possible and eventual correlations between variables can be uncovered.

== External Links ==
* [http://www.wssinfo.org/data-estimates/table/ JMP-download of raw data]
* [http://vis.stanford.edu/protovis/ Protovis homepage]

== References ==
* [Card, 1996] Card, Stuart: "Information visualization and information foraging", Proceedings of the workshop on Advanced visual interfaces - AVI ’96 (1996), 12. URL: [http://portal.acm.org/citation.cfm?doid=948449.948451 http://portal.acm.org/citation.cfm?doid=948449.948451], last access on January 17, 2011.

* [Card, 2008] Card, Stuart: “Information Visualization”, in The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications, Sears, A. and Jacko, J., A. (eds.), Lawrence Erlbaum Assoc Inc, 2008

* [Few, 2006] Few, Stephen: "Table and Graph Design at a Glance". URL: [http://ieg.ifs.tuwien.ac.at/%7Egschwand/teaching/infovis_ue_ws10/download/Table-and-Graph-Design-at-a-Glance.pdf http://ieg.ifs.tuwien.ac.at/%7Egschwand/teaching/infovis_ue_ws10/download/Table-and-Graph-Design-at-a-Glance.pdf], last access on January 17, 2011.

* [Few, 2007] Few, Stephen: "Visualizing Change", Visual Business Intelligence Newsletter (2007), p.1-15. URL: [http://www.perceptualedge.com/articles/visual_business_intelligence/visualizing_change.pdf http://www.perceptualedge.com/articles/visual_business_intelligence/visualizing_change.pdf], last access on January 17, 2011.

* [JMP, 2010] Joint Monitoring Programme: "Progress on sanitation and drinking water – 2010 Update", (2010) WHO Library Cataloguing-in-Publication Data. URL: [http://www.wssinfo.org/fileadmin/user_upload/resources/1278061137-JMP_report_2010_en.pdf http://www.wssinfo.org/fileadmin/user_upload/resources/1278061137-JMP_report_2010_en.pdf], last access on January 17, 2011.

* [Mazza, 2009] Mazza, Riccardo: "Introduction to Information Visualization", Springer-Verlag, London, 2009

* [Shneiderman, 1996] Shneiderman, B: "The eyes have it: a task by data type taxonomy for information visualizations", Proceedings 1996 IEEE Symposium on Visual Languages (1996), p.336-343. URL: [http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=545307 http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=545307], last access on January 17, 2011.

* [Tufte, 1983] Tufte, E.R.: "The Visual Display of Quantitative Information", Cheshire, Connecticut, Graphics Press, 1983.

* [Ward et al., 2010] Ward, M., Grindstein, G. and Keim, D.: "Interactive Data Visualization: Foundations, Techniques, and Application", A K Peters, 2010.

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 3

2011-01-17T19:57:15Z

InfoVis1011 9925916:

= Information Visualization, Task 3 =
This article is the result of [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01|group 1]] for [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe3.html task 3] of the course [http://www.ifs.tuwien.ac.at/~silvia/wien/vu-infovis/ information visualisation] at [http://www.tuwien.ac.at Vienna University of Technology] in the winter semester 2010/2011.

= Data =
The data for this task is provided by the [http://www.wssinfo.org Joint Monitoring Program for Water Supply and Sanitation (JMP)] of the [http://www.who.int/en/ World Health Organization (WHO)] and the [http://www.unicef.org/ UN Children’s Fund (UNICEF)]. It contains information about access to (drinking) water and sanitary standards in countries all over the globe, intended to monitor the process towards the [http://mdgs.un.org/unsd/mdg/Default.aspx Millennium Development Goals (MDG)].

== Area of Application ==
This being a fairly ambitious program requires close analysis of the data. This being especially necessary as it is clear (and also stated so by the JMP) that the acquisition of such data is hindered by infrastructural (numerous measurement standards, difficulty to reach places for a significant sample, etc.) and financial shortcomings. Expectedly the samples are not complete. Several countries lack one or more variables either completely or partially (over sample period 1990-2008). To overcome these problems, solutions had to be found for the individual visualizations.

== Analysis of Dataset ==
The dataset provided is structured temporally (time periods in which the samples were taken) as well as ordinally (for measurement of water quality so called drinking-water and sanitation ladders have been defined [JMP, 2010]: piped water sources are better than other improved which again are better than unimproved sources) and it contains quantitative samples. Nominal values tell about the region a sample is related to. The samples themselves are absolute and/or relative figures. Thus the dataset at hand is multidimensional and temporally structured.

The given information was extended by the (nominal) information to which continent a country belongs. This implicit structure adds a hierarchy, as the countries can be summed up to continents – a fact that is necessary or at least favorable for some visualization techniques.

== Potential Users ==
By the nature of the data processed in this task, the potential audience is an extremly wide one. In order to serve a large part of it a multi-step approach was chosed for the presentation of the data. These steps target not only different interrest groups but also help to gain an overview as well as an understanding and feel for the details of the data and their implications for countries development towards the millenium goals.

Since not even academic audience usually has the same skill in reading and understanding graphs and data (e.g. a doctor and a statistician) this has been considered when creating the visualisation applications. This also explains why there is no ''best order'' in which to explore those apps. For example might a statistician first choose to view the scatterplot and by this gain an overview of the distribution of data and correlations within the sample. Development workers on the other hand will likely be interrested in absolute numbers of people that lack access to improved water to decide where to deploy missions for improvement. And a politian or decision maker could want to have relative figures to base her or his decission for further support programms on them.

For this reason a webpage has been developed that gives acces to the three developed applications (including a brief description) and as such makes it possible for a broad audience to view the data they are (personally or professionally) interrested in.

= Objectives =
According to [JMP, 2010] a main goal is to identify disparities in development of different regions, so well-directed actions can be taken. The visualization shall support to examine:
* Which regions are left behind?
* Differences between urban and rural regions
* Differences between provided services (water/sanitation)
* Performance of different regions (with respect to different baselines where they started from)

= Visualization =

In the visualization solutions supplied, several different techniques are utilized to present the data:
* Bar chart
* Line chart
* Stacked time series
* Scatterplots

Based upon examples of the Protovis toolkit, interactive charts have been enhanced, adopted or interlinked in order to become more powerful. The driving idea behind this was Ben Shneidermans information seeking matra [Shneiderman, 1996]: "Overview first, zoom and filter, then details-on-deman"

== Development of Drinking Water Supply ==

=== Visual Mapping ===

==== Bar Chart ====
[[Image:Watervisbarchart.png | 350px | thumb | '''Figure 1''' : ''Bar chart'']]
In bar charts the length of the respective bar expresses the corresponding value of the variable. The advantage of this technique is the ability to feature individual values and support comparison of one value to another [Few, 2007, p. 3]. In the given example in figure 1 a ranking of the improvement/decline of improved drinking water access in the time period between 1990 and 2008 has been made.

In addition a bar can easily be used to express a second variable by color. This complies with Tufte´s design principle of multifunctional graphical elements [Tufte, 1983, p. 138]: “Mobilize every graphical element, perhaps several times over, to show the data”. Color is used to show the increase/decrease of population in the corresponding country. This technique makes it possible for the user to see correlations between variables (in this case improvement of drinking water and demographics) as well as differences between different groups (e.g. urban and rural).

==== Line Chart ====
Line chart are suitable to show development, and subsequently change, over time. When using bar charts, the overall shape of change gets lost in a forest of bars [Few, 2007, p.3]. Thus line charts are used to display evolution over the years.

=== Interaction ===

Following interaction techniques have been applied for this purpose:
* Overview and details on demand
* Filter

===== Overview and Detail on Demand =====
Overview and detail shows the data at more than one level, but they also show where the finer grain display fits into the larger grain display. Their main disadvantage is that they require coordination of two visual domains [Card, 1996]. In this visualizations overview is used to provide the big picture, displaying all countries. When pointing with the mouse at an element, the corresponding detail data is shown in a different view.

===== Filter =====
With filter functions the user can select different subsets of the whole dataset. By allowing users to control the content of the display, users can quickly focus on their interests by eliminating unwanted items [Shneiderman, 1996]. In the visualization the user can filter regions and parts of the population (urban/rural), by doing this interesting aspects of the dataset can be found (for example improvements in rural South America).

== Proportion of Access to Unimproved Water Sources Over Time ==
[[Image:Watervis-stackedtimeseries.jpg | 350px | thumb | '''Figure 2''' : ''Stacked time series'']]
As pointed out above, the focus was set upon development of regions over time. In this application visualization was restricted to utilization of unimproved water sources and its development. Of course this can be easily extended onto other variables. Important aspects are: how do different regions develop, and which regions are concerned most?

=== Visual Mapping ===

==== Hierarchical Stacked Time Series ====

According to [Few, 2006] lines can be used for time series to emphasize overall patterns, with time being placed on the horizontal axis.

The vertical axis is used to show how regions contribute to the total number of people with access to unimproved water sources. Instead of lines filled areas are used to depict the numbers, by this multiple countries can be stacked (adding up to the total number of each time period). The region taking up the most part of the chart is the one with the highest impact.

Color is used to encode the type of the region (urban, rural). And a text-label is used to identify the region.
As suggested in [Ward et al. 2010], redundant mapping is used to emphasize important data: regions with higher impact. For this labels are scaled proportionally, and color-saturation expresses the influence of the country/region on the total number.

==== Bar Chart ====
So far this chart does not show if the number of a region actually decreases or increases, as the numbers are compared to the total number per year. Thus it is linked to a bar chart. This chart again places Time on the horizontal axis, and quantity on the vertical –although absolute figures. The bars are used to display the figures of the country/region the mouse hovers over. For comparison reasons each year’s global total is also displayed.

=== Interaction ===
At any point the user can filter the shown data in the following ways:
* Choose if rural, urban, or total data of the countries/regions is shown
* Filter the countries/regions by their name, via search query

The first view shows the development of continents. The user can inspect how countries contribute to a continents’ development by clicking on the continent (zooming in). This brings up the name of the selected continent in the top left corner, providing basic contextual information.

When filtering and zooming, the scale is adjusted to the amount of regions presented in order to make regions with small impact visible as well. Moving the mouse over a region selects it for comparison to the total number in the bar chart on the right. This also clarifies the impact of this region to the total number.

== Scatterplot ==
[[Image:ScatterplotKeyvariables.png | 350px | thumb | '''Figure 3''' : ''Scatterplot'']]
=== Data Mapping ===
According to [Mazza, 2009, Table 3.2, p.40] there are three suitable preattentive attributes for the visualization of quantitative data: length and numerosity (form) as well as a spatial position in 2D-space.

Scatterplots, the third representation chosen for the given quantitative data, are based on this last attribute – 2D spatial positioning. Further this “standard scatterplot” is enhanced by utilization of color intensity. This attribute, suitable for (preattentive) visualization of ordinal data [Mazza, 2009, Table 3.2, p.40] is used to visualize the density of samples at a certain position, which translates into an ordinal relation. This additional feature uncovers what would otherwise remain hidden within the amount of data: aggregations of same or similar samples. This also "reduces" the space needed per plot which is one of the shortcomings of scatterplots – especially when plotting multiple variables.

=== Interaction Features ===
According to [Stuart Card, Informaiton Visualisation, p.525] scatterplots are a sensible visualization technique for problems with one or more variables. In the latter case they are usually combine to scatterplot matrices. First the user has to choose the data to be displayed by the scatterplots, which can be either a full-variable display of the water markers or sanitary markers. The third set consists of the total figures of water and sanitation. Each of these three sub-sets also contains the development of the countries’ overall populations and the development of urbanization.

Via brushing with the mouse the user can then select areas/countries within any of the scatterplots. The chosen selection is then highlighted in all the other scatterplots as well. With this a general overview of the data is possible and eventual correlations between variables can be uncovered.

== External Links ==
* [http://www.wssinfo.org/data-estimates/table/ JMP-download of raw data]
* [http://vis.stanford.edu/protovis/ Protovis homepage]

== References ==
* [Card, 1996] Card, Stuart: "Information visualization and information foraging", Proceedings of the workshop on Advanced visual interfaces - AVI ’96 (1996), 12. URL: [http://portal.acm.org/citation.cfm?doid=948449.948451 http://portal.acm.org/citation.cfm?doid=948449.948451], last access on January 17, 2011.

* [Card, 2008] Card, Stuart: “Information Visualization”, in The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications, Sears, A. and Jacko, J., A. (eds.), Lawrence Erlbaum Assoc Inc, 2008

* [Few, 2006] Few, Stephen: "Table and Graph Design at a Glance". URL: [http://ieg.ifs.tuwien.ac.at/%7Egschwand/teaching/infovis_ue_ws10/download/Table-and-Graph-Design-at-a-Glance.pdf http://ieg.ifs.tuwien.ac.at/%7Egschwand/teaching/infovis_ue_ws10/download/Table-and-Graph-Design-at-a-Glance.pdf], last access on January 17, 2011.

* [Few, 2007] Few, Stephen: "Visualizing Change", Visual Business Intelligence Newsletter (2007), p.1-15. URL: [http://www.perceptualedge.com/articles/visual_business_intelligence/visualizing_change.pdf http://www.perceptualedge.com/articles/visual_business_intelligence/visualizing_change.pdf], last access on January 17, 2011.

* [JMP, 2010] Joint Monitoring Programme: "Progress on sanitation and drinking water – 2010 Update", (2010) WHO Library Cataloguing-in-Publication Data. URL: [http://www.wssinfo.org/fileadmin/user_upload/resources/1278061137-JMP_report_2010_en.pdf http://www.wssinfo.org/fileadmin/user_upload/resources/1278061137-JMP_report_2010_en.pdf], last access on January 17, 2011.

* [Mazza, 2009] Mazza, Riccardo: "Introduction to Information Visualization", Springer-Verlag, London, 2009

* [Shneiderman, 1996] Shneiderman, B: "The eyes have it: a task by data type taxonomy for information visualizations", Proceedings 1996 IEEE Symposium on Visual Languages (1996), p.336-343. URL: [http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=545307 http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=545307], last access on January 17, 2011.

* [Tufte, 1983] Tufte, E.R.: "The Visual Display of Quantitative Information", Cheshire, Connecticut, Graphics Press, 1983.

* [Ward et al., 2010] Ward, M., Grindstein, G. and Keim, D.: "Interactive Data Visualization: Foundations, Techniques, and Application", A K Peters, 2010.

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 3

2011-01-17T19:19:34Z

InfoVis1011 9925916:

= Information Visualization, Task 3 =
This article is the result of [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01|group 1]] for [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe3.html task 3] of the course [http://www.ifs.tuwien.ac.at/~silvia/wien/vu-infovis/ information visualisation] at [http://www.tuwien.ac.at Vienna University of Technology] in the winter semester 2010/2011.

= Data =
The data for this task is provided by the [http://www.wssinfo.org Joint Monitoring Program for Water Supply and Sanitation (JMP)] of the [http://www.who.int/en/ World Health Organization (WHO)] and the [http://www.unicef.org/ UN Children’s Fund (UNICEF)]. It contains information about access to (drinking) water and sanitary standards in countries all over the globe, intended to monitor the process towards the [http://mdgs.un.org/unsd/mdg/Default.aspx Millennium Development Goals (MDG)].

== Area of application ==
This being a fairly ambitious program requires close analysis of the data. This being especially necessary as it is clear (and also stated so by the JMP) that the acquisition of such data is hindered by infrastructural (numerous measurement standards, difficulty to reach places for a significant sample, etc.) and financial shortcomings. Expectedly the samples are not complete. Several countries lack one or more variables either completely or partially (over sample period 1990-2008). To overcome these problems, solutions had to be found for the individual visualizations.

== Analysis of dataset ==
The dataset provided is structured temporally (time periods in which the samples were taken) as well as ordinally (for measurement of water quality so called drinking-water and sanitation ladders have been defined [JMP, 2010]: piped water sources are better than other improved which again are better than unimproved sources) and it contains quantitative samples. Nominal values tell about the region a sample is related to. The samples themselves are absolute and/or relative figures. Thus the dataset at hand is multidimensional and temporally structured.

The given information was extended by the (nominal) information to which continent a country belongs. This implicit structure adds a hierarchy, as the countries can be summed up to continents – a fact that is necessary or at least favorable for some visualization techniques.

== Potential users ==
By the nature of the data processed in this task, the potential audience is an extremly wide one. In order to serve a large part of it a multi-step approach was chosed for the presentation of the data. These steps target not only different interrest groups but also help to gain an overview as well as an understanding and feel for the details of the data and their implications for countries development towards the millenium goals.

Since not even academic audience usually has the same skill in reading and understanding graphs and data (e.g. a doctor and a statistician) this has been considered when creating the visualisation applications. This also explains why there is no ''best order'' in which to explore those apps. For example might a statistician first choose to view the scatterplot and by this gain an overview of the distribution of data and correlations within the sample. Development workers on the other hand will likely be interrested in absolute numbers of people that lack access to improved water to decide where to deploy missions for improvement. And a politian or decision maker could want to have relative figures to base her or his decission for further support programms on them.

For this reason a webpage has been developed that gives acces to the three developed applications (including a brief description) and as such makes it possible for a broad audience to view the data they are (personally or professionally) interrested in.

= Objectives =
According to [JMP, 2010] a main goal is to identify disparities in development of different regions, so well-directed actions can be taken. The visualization shall support to examine:
* Which regions are left behind?
* Differences between urban and rural regions
* Differences between provided services (water/sanitation)
* Performance of different regions (with respect to different baselines where they started from)

= Visualization =

In the visualization solutions supplied, several different techniques are utilized to present the data:
* Bar chart
* Line chart
* Stacked time series
* Scatterplots

Based upon examples of the Protovis toolkit, interactive charts have been enhanced, adopted or interlinked in order to become more powerful. The driving idea behind this was Ben Shneidermans information seeking matra [Shneiderman, 1996]: "Overview first, zoom and filter, then details-on-deman"

== Development of drinking water supply ==

=== Visual Mapping ===

==== Bar chart ====
[[Image:Watervisbarchart.png | 350px | thumb | '''Figure 1''' : ''Bar chart'']]
In bar charts the length of the respective bar expresses the corresponding value of the variable. The advantage of this technique is the ability to feature individual values and support comparison of one value to another [Few, 2007, p. 3]. In the given example in figure 1 a ranking of the improvement/decline of improved drinking water access in the time period between 1990 and 2008 has been made.

In addition a bar can easily be used to express a second variable by color. This complies with Tufte´s design principle of multifunctional graphical elements [Tufte, 1983, p. 138]: “Mobilize every graphical element, perhaps several times over, to show the data”. Color is used to show the increase/decrease of population in the corresponding country. This technique makes it possible for the user to see correlations between variables (in this case improvement of drinking water and demographics) as well as differences between different groups (e.g. urban and rural).

==== Line chart ====
Line chart are suitable to show development, and subsequently change, over time. When using bar charts, the overall shape of change gets lost in a forest of bars [Few, 2007, p.3]. Thus line charts are used to display evolution over the years.

=== Interaction ===

Following interaction techniques have been applied for this purpose:
* Overview and details on demand
* Filter

===== Overview and detail on demand =====
Overview and detail shows the data at more than one level, but they also show where the finer grain display fits into the larger grain display. Their main disadvantage is that they require coordination of two visual domains [Card, 1996]. In two visualizations '''(vis1 & vis2)''' overview is used to provide the big picture, displaying all countries. When pointing with the mouse at an element, the corresponding detail data is shown in a different view.

===== Filter =====
With filter functions the user can select different subsets of the whole dataset. By allowing users to control the content of the display, users can quickly focus on their interests by eliminating unwanted items [Shneiderman, 1996]. In the visualization the user can filter regions and parts of the population (urban/rural), by doing this interesting aspects of the dataset can be found (for example improvements in rural South America).

== Stacked time series and bar chart ==
[[Image:Watervis-stackedtimeseries.jpg | 350px | thumb | '''Figure 2''' : ''Stacked time series'']]
As pointed out above, the focus was set upon development of regions over time. In this application visualization was restricted to utilization of unimproved water sources and its development. Of course this can be easily extended onto other variables. Important aspects are: how do different regions develop, and which regions are concerned most?

=== Visual mapping ===
According to [Few, 2006] lines can be used for time series to emphasize overall patterns, with time being placed on the horizontal axis.

The vertical axis is used to show how regions contribute to the total number of people with access to unimproved water sources. Instead of lines filled areas are used to depict the numbers, by this multiple countries can be stacked (adding up to the total number of each time period). The region taking up the most part of the chart is the one with the highest impact.

Color is used to encode the type of the region (urban, rural). And a text-label is used to identify the region.
As suggested in [Ward et al. 2010], redundant mapping is used to emphasize important data: regions with higher impact. For this labels are scaled proportionally, and color-saturation expresses the influence of the country/region on the total number.

So far this chart does not show if the number of a region actually decreases or increases, as the numbers are compared to the total number per year. Thus it is linked to a bar chart. This chart again places Time on the horizontal axis, and quantity on the vertical –although absolute figures. The bars are used to display the figures of the country/region the mouse hovers over. For comparison reasons each year’s global total is also displayed.

=== Interaction ===
At any point the user can filter the shown data in the following ways:
* Choose if rural, urban, or total data of the countries/regions is shown
* Filter the countries/regions by their name, via search query

The first view shows the development of continents. The user can inspect how countries contribute to a continents’ development by clicking on the continent (zooming in). This brings up the name of the selected continent in the top left corner, providing basic contextual information.

When filtering and zooming, the scale is adjusted to the amount of regions presented in order to make regions with small impact visible as well. Moving the mouse over a region selects it for comparison to the total number in the bar chart on the right. This also clarifies the impact of this region to the total number.

== Scatterplot ==
[[Image:ScatterplotKeyvariables.png | 350px | thumb | '''Figure 3''' : ''Scatterplot'']]
=== Data Mapping ===
According to [Mazza, 2009, Table 3.2, p.40] there are three suitable preattentive attributes for the visualization of quantitative data: length and numerosity (form) as well as a spatial position in 2D-space.

Scatterplots, the third representation chosen for the given quantitative data, are based on this last attribute – 2D spatial positioning. Further this “standard scatterplot” is enhanced by utilization of color intensity. This attribute, suitable for (preattentive) visualization of ordinal data [Mazza, 2009, Table 3.2, p.40] is used to visualize the density of samples at a certain position, which translates into an ordinal relation. This additional feature uncovers what would otherwise remain hidden within the amount of data: aggregations of same or similar samples. This also "reduces" the space needed per plot which is one of the shortcomings of scatterplots – especially when plotting multiple variables.

=== Interaction Features ===
According to [Stuart Card, Informaiton Visualisation, p.525] scatterplots are a sensible visualization technique for problems with one or more variables. In the latter case they are usually combine to scatterplot matrices. First the user has to choose the data to be displayed by the scatterplots, which can be either a full-variable display of the water markers or sanitary markers. The third set consists of the total figures of water and sanitation. Each of these three sub-sets also contains the development of the countries’ overall populations and the development of urbanization.

Via brushing with the mouse the user can then select areas/countries within any of the scatterplots. The chosen selection is then highlighted in all the other scatterplots as well. With this a general overview of the data is possible and eventual correlations between variables can be uncovered.

== External Links ==
* [http://www.wssinfo.org/data-estimates/table/ JMP-download of raw data]
* [http://vis.stanford.edu/protovis/ Protovis homepage]

== References ==
* [Card, 1996] Card, Stuart: "Information visualization and information foraging", Proceedings of the workshop on Advanced visual interfaces - AVI ’96 (1996), 12. URL: [http://portal.acm.org/citation.cfm?doid=948449.948451 http://portal.acm.org/citation.cfm?doid=948449.948451], last access on January 17, 2011.

* [Card, 2008] Card, Stuart: “Information Visualization”, in The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications, Sears, A. and Jacko, J., A. (eds.), Lawrence Erlbaum Assoc Inc, 2008

* [Few, 2006] Few, Stephen: "Table and Graph Design at a Glance". URL: [http://ieg.ifs.tuwien.ac.at/%7Egschwand/teaching/infovis_ue_ws10/download/Table-and-Graph-Design-at-a-Glance.pdf http://ieg.ifs.tuwien.ac.at/%7Egschwand/teaching/infovis_ue_ws10/download/Table-and-Graph-Design-at-a-Glance.pdf], last access on January 17, 2011.

* [Few, 2007] Few, Stephen: "Visualizing Change", Visual Business Intelligence Newsletter (2007), p.1-15. URL: [http://www.perceptualedge.com/articles/visual_business_intelligence/visualizing_change.pdf http://www.perceptualedge.com/articles/visual_business_intelligence/visualizing_change.pdf], last access on January 17, 2011.

* [JMP, 2010] Joint Monitoring Programme: "Progress on sanitation and drinking water – 2010 Update", (2010) WHO Library Cataloguing-in-Publication Data. URL: [http://www.wssinfo.org/fileadmin/user_upload/resources/1278061137-JMP_report_2010_en.pdf http://www.wssinfo.org/fileadmin/user_upload/resources/1278061137-JMP_report_2010_en.pdf], last access on January 17, 2011.

* [Mazza, 2009] Mazza, Riccardo: "Introduction to Information Visualization", Springer-Verlag, London, 2009

* [Shneiderman, 1996] Shneiderman, B: "The eyes have it: a task by data type taxonomy for information visualizations", Proceedings 1996 IEEE Symposium on Visual Languages (1996), p.336-343. URL: [http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=545307 http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=545307], last access on January 17, 2011.

* [Tufte, 1983] Tufte, E.R.: "The Visual Display of Quantitative Information", Cheshire, Connecticut, Graphics Press, 1983.

* [Ward et al., 2010] Ward, M., Grindstein, G. and Keim, D.: "Interactive Data Visualization: Foundations, Techniques, and Application", A K Peters, 2010.

Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 3

2011-01-17T18:47:59Z

InfoVis1011 9925916:

* Objectives gehört auf die gleiche Ebene wie, und vor Visualization (Ziel -> Umsetzung)
* Screenshot für stacked time series ist nun auch drin
* ich hätte die Theorie eher als Begründung für die jeweilige Umsetzung gesehen.
* jööö
[[User:InfoVis1011 9925916|Michael Kraxner]] 19:47, 17 January 2011 (CET) Michael Kraxner

* Ich habe screenshots für den Bar chart und den Scatterplot eingefügt.
* Der Aufbau je App macht meiner Meinung nach Sinn, da sie ja nicht direkt zusammenhängend sind

[[User:UE-InfoVis1011 0326062|Thomas Schneider]] 18:06, 17 January 2011 (CET)

So, der erste Teil ist online. 
Meiner Meinung nach jetzt noch:
* die Startpage für die Apps (mach ich)
* Screenshots und Illustrationsmaterial für den wiki-Artikel
* Die Gliederung von Visualisation gefällt mir nicht. Sollten wir noch überarbeiten. Spontaner Einfall: entweder wir fassen nach den einzelnen Apps zusammen, oder wir teilen zwischen Theorie und Praxis. Habt ihr bessere Vorschläge?
* Schlage vor, dass wir die Apps benennen und durchgängig so bezeichnen. Einmal hab ich im Text schon '''(vis1 & vis2)''' geschrieben
-- [[User:UE-InfoVis1011 0026203|Štefan EMRICH]] 17:15, 17 Jänner 2010 (CEST)

User:InfoVis1011 9925916

2011-01-17T18:42:41Z

InfoVis1011 9925916:

<big>'''Michael Kraxner'''</big>

[[Image:MKraxner.JPG|200px | thumb |alt=Michael Kraxner|Michael Kraxner]]
== Affiliation ==
* [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 (Emrich, ???, ???)|Gruppe 01]]
* [http://www.tuwien.ac.at Vienna University of Technology] 
* [http://ifs.tuwien.ac.at Information & Software Engineering Group]

[[CATEGORY: Persons]]

Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 3

2011-01-17T18:38:39Z

InfoVis1011 9925916:

* Objectives gehört auf die gleiche Ebene wie, und vor Visualization (Ziel -> Umsetzung)
* Screenshot für stacked time series ist nun auch drin
* ich hätte die Theorie eher als Begründung für die jeweilige Umsetzung gesehen.
[[User:InfoVis1011 9925916|InfoVis1011 9925916]] 19:38, 17 January 2011 (CET)

* Ich habe screenshots für den Bar chart und den Scatterplot eingefügt.
* Der Aufbau je App macht meiner Meinung nach Sinn, da sie ja nicht direkt zusammenhängend sind

[[User:UE-InfoVis1011 0326062|Thomas Schneider]] 18:06, 17 January 2011 (CET)

So, der erste Teil ist online. 
Meiner Meinung nach jetzt noch:
* die Startpage für die Apps (mach ich)
* Screenshots und Illustrationsmaterial für den wiki-Artikel
* Die Gliederung von Visualisation gefällt mir nicht. Sollten wir noch überarbeiten. Spontaner Einfall: entweder wir fassen nach den einzelnen Apps zusammen, oder wir teilen zwischen Theorie und Praxis. Habt ihr bessere Vorschläge?
* Schlage vor, dass wir die Apps benennen und durchgängig so bezeichnen. Einmal hab ich im Text schon '''(vis1 & vis2)''' geschrieben
-- [[User:UE-InfoVis1011 0026203|Štefan EMRICH]] 17:15, 17 Jänner 2010 (CEST)

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 3

2011-01-17T18:07:02Z

InfoVis1011 9925916:

= Information Visualization, Task 3 =
This article is the result of [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01|group 1]] for [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe3.html task 3] of the course [http://www.ifs.tuwien.ac.at/~silvia/wien/vu-infovis/ information visualisation] at [http://www.tuwien.ac.at Vienna University of Technology] in the winter semester 2010/2011.

= Data =
The data for this task is provided by the [http://www.wssinfo.org Joint Monitoring Program for Water Supply and Sanitation (JMP)] of the [http://www.who.int/en/ World Health Organization (WHO)] and the [http://www.unicef.org/ UN Children’s Fund (UNICEF)]. It contains information about access to (drinking) water and sanitary standards in countries all over the globe, intended to monitor the process towards the [http://mdgs.un.org/unsd/mdg/Default.aspx Millennium Development Goals (MDG)].

== Area of application ==
This being a fairly ambitious program requires close analysis of the data. This being especially necessary as it is clear (and also stated so by the JMP) that the acquisition of such data is hindered by infrastructural (numerous measurement standards, difficulty to reach places for a significant sample, etc.) and financial shortcomings. Expectedly the samples are not complete. Several countries lack one or more variables either completely or partially (over sample period 1990-2008). To overcome these problems, solutions had to be found for the individual visualizations.

== Analysis of dataset ==
The dataset provided is structured temporally (time periods in which the samples were taken) as well as ordinally (for measurement of water quality so called drinking-water and sanitation ladders have been defined [JMP, 2010]: piped water sources are better than other improved which again are better than unimproved sources) and it contains quantitative samples. Nominal values tell about the region a sample is related to. The samples themselves are absolute and/or relative figures. Thus the dataset at hand is multidimensional and temporally structured.

The given information was extended by the (nominal) information to which continent a country belongs. This implicit structure adds a hierarchy, as the countries can be summed up to continents – a fact that is necessary or at least favorable for some visualization techniques.

== Potential users ==
By the nature of the data processed in this task, the potential audience is an extremly wide one. In order to serve a large part of it a multi-step approach was chosed for the presentation of the data. These steps target not only different interrest groups but also help to gain an overview as well as an understanding and feel for the details of the data and their implications for countries development towards the millenium goals.

Since not even academic audience usually has the same skill in reading and understanding graphs and data (e.g. a doctor and a statistician) this has been considered when creating the visualisation applications. This also explains why there is no ''best order'' in which to explore those apps. For example might a statistician first choose to view the scatterplot and by this gain an overview of the distribution of data and correlations within the sample. Development workers on the other hand will likely be interrested in absolute numbers of people that lack access to improved water to decide where to deploy missions for improvement. And a politian or decision maker could want to have relative figures to base her or his decission for further support programms on them.

For this reason a webpage has been developed that gives acces to the three developed applications (including a brief description) and as such makes it possible for a broad audience to view the data they are (personally or professionally) interrested in.

= Visualization =
== Objectives ==
According to [JMP, 2010] a main goal is to identify disparities in development of different regions, so well-directed actions can be taken. The visualization shall support to examine:
* Which regions are left behind?
* Differences between urban and rural regions
* Differences between provided services (water/sanitation)
* Performance of different regions (with respect to different baselines where they started from)

=== Visualization design ===
In the visualization solutions supplied, several different techniques are utilized to present the data:
* Bar chart
* Line chart
* Stacked time series
* Scatterplots

Based upon examples of the Protovis toolkit, interactive charts have been enhanced, adopted or interlinked in order to become more powerful. The driving idea behind this was Ben Shneidermans information seeking matra [Shneiderman, 1996]: "Overview first, zoom and filter, then details-on-deman"

Following interaction techniques have been applied for this purpose:
* Overview and details on demand
* Filter

==== Overview and detail on demand ====
Overview and detail shows the data at more than one level, but they also show where the finer grain display fits into the larger grain display. Their main disadvantage is that they require coordination of two visual domains [Card, 1996]. In two visualizations '''(vis1 & vis2)''' overview is used to provide the big picture, displaying all countries. When pointing with the mouse at an element, the corresponding detail data is shown in a different view.

==== Filter ====
With filter functions the user can select different subsets of the whole dataset. By allowing users to control the content of the display, users can quickly focus on their interests by eliminating unwanted items [Shneiderman, 1996]. In the visualization the user can filter regions and parts of the population (urban/rural), by doing this interesting aspects of the dataset can be found (for example improvements in rural South America).

== Bar chart ==
[[Image:Watervisbarchart.png | 350px | thumb | '''Figure 1''' : ''Bar chart'']]
In bar charts the length of the respective bar expresses the corresponding value of the variable. The advantage of this technique is the ability to feature individual values and support comparison of one value to another [Few, 2007, p. 3]. In the given example in figure 1 a ranking of the improvement/decline of improved drinking water access in the time period between 1990 and 2008 has been made.

In addition a bar can easily be used to express a second variable by color. This complies with Tufte´s design principle of multifunctional graphical elements [Tufte, 1983, p. 138]: “Mobilize every graphical element, perhaps several times over, to show the data”. Color is used to show the increase/decrease of population in the corresponding country. This technique makes it possible for the user to see correlations between variables (in this case improvement of drinking water and demographics) as well as differences between different groups (e.g. urban and rural).

== Line chart ==
Line chart are suitable to show development, and subsequently change, over time. When using bar charts, the overall shape of change gets lost in a forest of bars [Few, 2007, p.3]. Thus line charts are used to display evolution over the years.

== Stacked time series and bar chart ==
[[Image:Watervis-stackedtimeseries.jpg | 350px | thumb | '''Figure 2''' : ''Stacked time series'']]
As pointed out above, the focus was set upon development of regions over time. In this application visualization was restricted to utilization of unimproved water sources and its development. Of course this can be easily extended onto other variables. Important aspects are: how do different regions develop, and which regions are concerned most?

=== Visual mapping ===
According to [Few, 2006] lines can be used for time series to emphasize overall patterns, with time being placed on the horizontal axis.

The vertical axis is used to show how regions contribute to the total number of people with access to unimproved water sources. Instead of lines filled areas are used to depict the numbers, by this multiple countries can be stacked (adding up to the total number of each time period). The region taking up the most part of the chart is the one with the highest impact.

Color is used to encode the type of the region (urban, rural). And a text-label is used to identify the region.
As suggested in [Ward et al. 2010], redundant mapping is used to emphasize important data: regions with higher impact. For this labels are scaled proportionally, and color-saturation expresses the influence of the country/region on the total number.

So far this chart does not show if the number of a region actually decreases or increases, as the numbers are compared to the total number per year. Thus it is linked to a bar chart. This chart again places Time on the horizontal axis, and quantity on the vertical –although absolute figures. The bars are used to display the figures of the country/region the mouse hovers over. For comparison reasons each year’s global total is also displayed.

=== Interaction ===
At any point the user can filter the shown data in the following ways:
* Choose if rural, urban, or total data of the countries/regions is shown
* Filter the countries/regions by their name, via search query

The first view shows the development of continents. The user can inspect how countries contribute to a continents’ development by clicking on the continent (zooming in). This brings up the name of the selected continent in the top left corner, providing basic contextual information.

When filtering and zooming, the scale is adjusted to the amount of regions presented in order to make regions with small impact visible as well. Moving the mouse over a region selects it for comparison to the total number in the bar chart on the right. This also clarifies the impact of this region to the total number.

== Scatterplot ==
[[Image:ScatterplotKeyvariables.png | 350px | thumb | '''Figure 3''' : ''Scatterplot'']]
=== Data Mapping ===
According to [Mazza, 2009, Table 3.2, p.40] there are three suitable preattentive attributes for the visualization of quantitative data: length and numerosity (form) as well as a spatial position in 2D-space.

Scatterplots, the third representation chosen for the given quantitative data, are based on this last attribute – 2D spatial positioning. Further this “standard scatterplot” is enhanced by utilization of color intensity. This attribute, suitable for (preattentive) visualization of ordinal data [Mazza, 2009, Table 3.2, p.40] is used to visualize the density of samples at a certain position, which translates into an ordinal relation. This additional feature uncovers what would otherwise remain hidden within the amount of data: aggregations of same or similar samples. This also "reduces" the space needed per plot which is one of the shortcomings of scatterplots – especially when plotting multiple variables.

=== Interaction Features ===
According to [Stuart Card, Informaiton Visualisation, p.525] scatterplots are a sensible visualization technique for problems with one or more variables. In the latter case they are usually combine to scatterplot matrices. First the user has to choose the data to be displayed by the scatterplots, which can be either a full-variable display of the water markers or sanitary markers. The third set consists of the total figures of water and sanitation. Each of these three sub-sets also contains the development of the countries’ overall populations and the development of urbanization.

Via brushing with the mouse the user can then select areas/countries within any of the scatterplots. The chosen selection is then highlighted in all the other scatterplots as well. With this a general overview of the data is possible and eventual correlations between variables can be uncovered.

== External Links ==
* [http://www.wssinfo.org/data-estimates/table/ JMP-download of raw data]
* [http://vis.stanford.edu/protovis/ Protovis homepage]

== References ==
* [Card, 1996] Card, Stuart: "Information visualization and information foraging", Proceedings of the workshop on Advanced visual interfaces - AVI ’96 (1996), 12. URL: [http://portal.acm.org/citation.cfm?doid=948449.948451 http://portal.acm.org/citation.cfm?doid=948449.948451], last access on January 17, 2011.

* [Card, 2008] Card, Stuart: “Information Visualization”, in The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications, Sears, A. and Jacko, J., A. (eds.), Lawrence Erlbaum Assoc Inc, 2008

* [Few, 2006] Few, Stephen: "Table and Graph Design at a Glance". URL: [http://ieg.ifs.tuwien.ac.at/%7Egschwand/teaching/infovis_ue_ws10/download/Table-and-Graph-Design-at-a-Glance.pdf http://ieg.ifs.tuwien.ac.at/%7Egschwand/teaching/infovis_ue_ws10/download/Table-and-Graph-Design-at-a-Glance.pdf], last access on January 17, 2011.

* [Few, 2007] Few, Stephen: "Visualizing Change", Visual Business Intelligence Newsletter (2007), p.1-15. URL: [http://www.perceptualedge.com/articles/visual_business_intelligence/visualizing_change.pdf http://www.perceptualedge.com/articles/visual_business_intelligence/visualizing_change.pdf], last access on January 17, 2011.

* [JMP, 2010] Joint Monitoring Programme: "Progress on sanitation and drinking water – 2010 Update", (2010) WHO Library Cataloguing-in-Publication Data. URL: [http://www.wssinfo.org/fileadmin/user_upload/resources/1278061137-JMP_report_2010_en.pdf http://www.wssinfo.org/fileadmin/user_upload/resources/1278061137-JMP_report_2010_en.pdf], last access on January 17, 2011.

* [Mazza, 2009] Mazza, Riccardo: "Introduction to Information Visualization", Springer-Verlag, London, 2009

* [Shneiderman, 1996] Shneiderman, B: "The eyes have it: a task by data type taxonomy for information visualizations", Proceedings 1996 IEEE Symposium on Visual Languages (1996), p.336-343. URL: [http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=545307 http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=545307], last access on January 17, 2011.

* [Tufte, 1983] Tufte, E.R.: "The Visual Display of Quantitative Information", Cheshire, Connecticut, Graphics Press, 1983.

* [Ward et al., 2010] Ward, M., Grindstein, G. and Keim, D.: "Interactive Data Visualization: Foundations, Techniques, and Application", A K Peters, 2010.

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 3

2011-01-17T18:06:28Z

InfoVis1011 9925916:

= Information Visualization, Task 3 =
This article is the result of [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01|group 1]] for [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe3.html task 3] of the course [http://www.ifs.tuwien.ac.at/~silvia/wien/vu-infovis/ information visualisation] at [http://www.tuwien.ac.at Vienna University of Technology] in the winter semester 2010/2011.

= Data =
The data for this task is provided by the [http://www.wssinfo.org Joint Monitoring Program for Water Supply and Sanitation (JMP)] of the [http://www.who.int/en/ World Health Organization (WHO)] and the [http://www.unicef.org/ UN Children’s Fund (UNICEF)]. It contains information about access to (drinking) water and sanitary standards in countries all over the globe, intended to monitor the process towards the [http://mdgs.un.org/unsd/mdg/Default.aspx Millennium Development Goals (MDG)].

== Area of application ==
This being a fairly ambitious program requires close analysis of the data. This being especially necessary as it is clear (and also stated so by the JMP) that the acquisition of such data is hindered by infrastructural (numerous measurement standards, difficulty to reach places for a significant sample, etc.) and financial shortcomings. Expectedly the samples are not complete. Several countries lack one or more variables either completely or partially (over sample period 1990-2008). To overcome these problems, solutions had to be found for the individual visualizations.

== Analysis of dataset ==
The dataset provided is structured temporally (time periods in which the samples were taken) as well as ordinally (for measurement of water quality so called drinking-water and sanitation ladders have been defined [JMP, 2010]: piped water sources are better than other improved which again are better than unimproved sources) and it contains quantitative samples. Nominal values tell about the region a sample is related to. The samples themselves are absolute and/or relative figures. Thus the dataset at hand is multidimensional and temporally structured.

The given information was extended by the (nominal) information to which continent a country belongs. This implicit structure adds a hierarchy, as the countries can be summed up to continents – a fact that is necessary or at least favorable for some visualization techniques.

== Potential users ==
By the nature of the data processed in this task, the potential audience is an extremly wide one. In order to serve a large part of it a multi-step approach was chosed for the presentation of the data. These steps target not only different interrest groups but also help to gain an overview as well as an understanding and feel for the details of the data and their implications for countries development towards the millenium goals.

Since not even academic audience usually has the same skill in reading and understanding graphs and data (e.g. a doctor and a statistician) this has been considered when creating the visualisation applications. This also explains why there is no ''best order'' in which to explore those apps. For example might a statistician first choose to view the scatterplot and by this gain an overview of the distribution of data and correlations within the sample. Development workers on the other hand will likely be interrested in absolute numbers of people that lack access to improved water to decide where to deploy missions for improvement. And a politian or decision maker could want to have relative figures to base her or his decission for further support programms on them.

For this reason a webpage has been developed that gives acces to the three developed applications (including a brief description) and as such makes it possible for a broad audience to view the data they are (personally or professionally) interrested in.

= Visualization =
== Objectives ==
According to [JMP, 2010] a main goal is to identify disparities in development of different regions, so well-directed actions can be taken. The visualization shall support to examine:
* Which regions are left behind?
* Differences between urban and rural regions
* Differences between provided services (water/sanitation)
* Performance of different regions (with respect to different baselines where they started from)

=== Visualization design ===
In the visualization solutions supplied, several different techniques are utilized to present the data:
* Bar chart
* Line chart
* Stacked time series
* Scatterplots

Based upon examples of the Protovis toolkit, interactive charts have been enhanced, adopted or interlinked in order to become more powerful. The driving idea behind this was Ben Shneidermans information seeking matra [Shneiderman, 1996]: "Overview first, zoom and filter, then details-on-deman"

Following interaction techniques have been applied for this purpose:
* Overview and details on demand
* Filter

==== Overview and detail on demand ====
Overview and detail shows the data at more than one level, but they also show where the finer grain display fits into the larger grain display. Their main disadvantage is that they require coordination of two visual domains [Card, 1996]. In two visualizations '''(vis1 & vis2)''' overview is used to provide the big picture, displaying all countries. When pointing with the mouse at an element, the corresponding detail data is shown in a different view.

==== Filter ====
With filter functions the user can select different subsets of the whole dataset. By allowing users to control the content of the display, users can quickly focus on their interests by eliminating unwanted items [Shneiderman, 1996]. In the visualization the user can filter regions and parts of the population (urban/rural), by doing this interesting aspects of the dataset can be found (for example improvements in rural South America).

== Bar chart ==
[[Image:Watervisbarchart.png | 350px | thumb | '''Figure 1''' : ''Bar chart'']]
In bar charts the length of the respective bar expresses the corresponding value of the variable. The advantage of this technique is the ability to feature individual values and support comparison of one value to another [Few, 2007, p. 3]. In the given example in figure 1 a ranking of the improvement/decline of improved drinking water access in the time period between 1990 and 2008 has been made.

In addition a bar can easily be used to express a second variable by color. This complies with Tufte´s design principle of multifunctional graphical elements [Tufte, 1983, p. 138]: “Mobilize every graphical element, perhaps several times over, to show the data”. Color is used to show the increase/decrease of population in the corresponding country. This technique makes it possible for the user to see correlations between variables (in this case improvement of drinking water and demographics) as well as differences between different groups (e.g. urban and rural).

== Line chart ==
Line chart are suitable to show development, and subsequently change, over time. When using bar charts, the overall shape of change gets lost in a forest of bars [Few, 2007, p.3]. Thus line charts are used to display evolution over the years.

== Stacked time series and bar chart ==
[[Image:Watervis-stackedtimeseries.jpg | 350px | thumb | '''Figure 3''' : ''Stacked time series'']]
As pointed out above, the focus was set upon development of regions over time. In this application visualization was restricted to utilization of unimproved water sources and its development. Of course this can be easily extended onto other variables. Important aspects are: how do different regions develop, and which regions are concerned most?

=== Visual mapping ===
According to [Few, 2006] lines can be used for time series to emphasize overall patterns, with time being placed on the horizontal axis.

The vertical axis is used to show how regions contribute to the total number of people with access to unimproved water sources. Instead of lines filled areas are used to depict the numbers, by this multiple countries can be stacked (adding up to the total number of each time period). The region taking up the most part of the chart is the one with the highest impact.

Color is used to encode the type of the region (urban, rural). And a text-label is used to identify the region.
As suggested in [Ward et al. 2010], redundant mapping is used to emphasize important data: regions with higher impact. For this labels are scaled proportionally, and color-saturation expresses the influence of the country/region on the total number.

So far this chart does not show if the number of a region actually decreases or increases, as the numbers are compared to the total number per year. Thus it is linked to a bar chart. This chart again places Time on the horizontal axis, and quantity on the vertical –although absolute figures. The bars are used to display the figures of the country/region the mouse hovers over. For comparison reasons each year’s global total is also displayed.

=== Interaction ===
At any point the user can filter the shown data in the following ways:
* Choose if rural, urban, or total data of the countries/regions is shown
* Filter the countries/regions by their name, via search query

The first view shows the development of continents. The user can inspect how countries contribute to a continents’ development by clicking on the continent (zooming in). This brings up the name of the selected continent in the top left corner, providing basic contextual information.

When filtering and zooming, the scale is adjusted to the amount of regions presented in order to make regions with small impact visible as well. Moving the mouse over a region selects it for comparison to the total number in the bar chart on the right. This also clarifies the impact of this region to the total number.

== Scatterplot ==
[[Image:ScatterplotKeyvariables.png | 350px | thumb | '''Figure 3''' : ''Scatterplot'']]
=== Data Mapping ===
According to [Mazza, 2009, Table 3.2, p.40] there are three suitable preattentive attributes for the visualization of quantitative data: length and numerosity (form) as well as a spatial position in 2D-space.

Scatterplots, the third representation chosen for the given quantitative data, are based on this last attribute – 2D spatial positioning. Further this “standard scatterplot” is enhanced by utilization of color intensity. This attribute, suitable for (preattentive) visualization of ordinal data [Mazza, 2009, Table 3.2, p.40] is used to visualize the density of samples at a certain position, which translates into an ordinal relation. This additional feature uncovers what would otherwise remain hidden within the amount of data: aggregations of same or similar samples. This also "reduces" the space needed per plot which is one of the shortcomings of scatterplots – especially when plotting multiple variables.

=== Interaction Features ===
According to [Stuart Card, Informaiton Visualisation, p.525] scatterplots are a sensible visualization technique for problems with one or more variables. In the latter case they are usually combine to scatterplot matrices. First the user has to choose the data to be displayed by the scatterplots, which can be either a full-variable display of the water markers or sanitary markers. The third set consists of the total figures of water and sanitation. Each of these three sub-sets also contains the development of the countries’ overall populations and the development of urbanization.

Via brushing with the mouse the user can then select areas/countries within any of the scatterplots. The chosen selection is then highlighted in all the other scatterplots as well. With this a general overview of the data is possible and eventual correlations between variables can be uncovered.

== External Links ==
* [http://www.wssinfo.org/data-estimates/table/ JMP-download of raw data]
* [http://vis.stanford.edu/protovis/ Protovis homepage]

== References ==
* [Card, 1996] Card, Stuart: "Information visualization and information foraging", Proceedings of the workshop on Advanced visual interfaces - AVI ’96 (1996), 12. URL: [http://portal.acm.org/citation.cfm?doid=948449.948451 http://portal.acm.org/citation.cfm?doid=948449.948451], last access on January 17, 2011.

* [Card, 2008] Card, Stuart: “Information Visualization”, in The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications, Sears, A. and Jacko, J., A. (eds.), Lawrence Erlbaum Assoc Inc, 2008

* [Few, 2006] Few, Stephen: "Table and Graph Design at a Glance". URL: [http://ieg.ifs.tuwien.ac.at/%7Egschwand/teaching/infovis_ue_ws10/download/Table-and-Graph-Design-at-a-Glance.pdf http://ieg.ifs.tuwien.ac.at/%7Egschwand/teaching/infovis_ue_ws10/download/Table-and-Graph-Design-at-a-Glance.pdf], last access on January 17, 2011.

* [Few, 2007] Few, Stephen: "Visualizing Change", Visual Business Intelligence Newsletter (2007), p.1-15. URL: [http://www.perceptualedge.com/articles/visual_business_intelligence/visualizing_change.pdf http://www.perceptualedge.com/articles/visual_business_intelligence/visualizing_change.pdf], last access on January 17, 2011.

* [JMP, 2010] Joint Monitoring Programme: "Progress on sanitation and drinking water – 2010 Update", (2010) WHO Library Cataloguing-in-Publication Data. URL: [http://www.wssinfo.org/fileadmin/user_upload/resources/1278061137-JMP_report_2010_en.pdf http://www.wssinfo.org/fileadmin/user_upload/resources/1278061137-JMP_report_2010_en.pdf], last access on January 17, 2011.

* [Mazza, 2009] Mazza, Riccardo: "Introduction to Information Visualization", Springer-Verlag, London, 2009

* [Shneiderman, 1996] Shneiderman, B: "The eyes have it: a task by data type taxonomy for information visualizations", Proceedings 1996 IEEE Symposium on Visual Languages (1996), p.336-343. URL: [http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=545307 http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=545307], last access on January 17, 2011.

* [Tufte, 1983] Tufte, E.R.: "The Visual Display of Quantitative Information", Cheshire, Connecticut, Graphics Press, 1983.

* [Ward et al., 2010] Ward, M., Grindstein, G. and Keim, D.: "Interactive Data Visualization: Foundations, Techniques, and Application", A K Peters, 2010.

File:Watervis-stackedtimeseries.jpg

2011-01-17T18:04:10Z

InfoVis1011 9925916: The visualisation shows the development of access to unimproved water sources, based on data from the Joint Monitoring Program for Water Supply and Sanitation (JMP) of the World Health Organization (WHO) and the UN Children’s Fund (UNICEF). http://www.w

== Summary ==
The visualisation shows the development of access to unimproved water sources, based on data from the Joint Monitoring Program for Water Supply and Sanitation (JMP) of the World Health Organization (WHO) and the UN Children’s Fund (UNICEF). http://www.wssinfo.org/data-estimates/table/
== Copyright status ==

== Source ==

User:InfoVis1011 9925916

2011-01-17T17:13:07Z

InfoVis1011 9925916:

<big>'''Michael Kraxner'''</big>

[[Image:MKraxner.JPG|200px]]
== Affiliation ==
* [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 (Emrich, ???, ???)|Gruppe 01]]
* [http://www.tuwien.ac.at Vienna University of Technology] 
* [http://ifs.tuwien.ac.at Information & Software Engineering Group]

[[CATEGORY: Persons]]

User:InfoVis1011 9925916

2011-01-17T17:12:17Z

InfoVis1011 9925916:

<big>'''Michael Kraxner'''</big>

[[Image:MKraxner.JPG|200px]]
== Affiliation ==
* [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 (Emrich, Schneider, Kraxner)|Gruppe 01]]
* [http://www.tuwien.ac.at Vienna University of Technology] 
* [http://ifs.tuwien.ac.at Information & Software Engineering Group]

[[CATEGORY: Persons]]

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-17T13:38:09Z

InfoVis1011 9925916: tippfehler behoben

= Graphical Inference for Infovis =
The following article summarizes the work of [Wickham et al., 2010] on graphical inference, which has been highly appreciated by [Scheidegger, 2010]:
<blockquote>"If I had to decide on a single paper this year which I think people will easily remember in 10 years, this would be it."</blockquote>

== Introduction ==
Information visualization provides tools to uncover new relations in data. While these technique can be very effective they are jeopardized by apophenia, the human ability to detect patterns in noise. On the other hand, statistics provide methods that examine if such relationships can be deduced from sample data - or if the hypotheses are invalid and subsequently have to be rejected. Graphical inference tries to find a balance between these two methods: information visualisation to improve identification of new hypotheses, and statistics to reveal faulty conclusions.

The authors present two experimental [[#Protocols|protocols]], [[#Rorschach|Rorschach]] and [[#Line-Up|Line-Up]], which show how the techniques mentioned above (statistics and information visualisation) can be combined.

== Motivation ==
Statistical methods generally try to show if a hypothesis is true or not. More specifically statistic investigate whether a difference exists (testing) or how big the difference is (estimating). For graphical inference you want to know if a difference actually exists, thus graphical inference works as testing procedure.

=== Statistic Foundation ===
For such a statistic test one needs to define a so-called null hypothesis H0 which is tested against the alternative hypothesis H1. As statistics produce results based on probabilities, mistakes can happen. The two possible errors are classified as follows:

{| border bordercolor="lightgrey" bgcolor="#C0C0C0" cellspacing=0 cellpadding="10

|----- bgcolor="#C0C0C0"
!
! Null Hypothesis (H0) is true
! Alternative Hypothesis (H1) is true
|-----
| Null Hypothesis is accepted
| align="center"| Right decision
| align="center"| Type II Error False Negative
|-----
| Null Hypothesis is rejected
| align="center"| Type I Error False Positive
| align="center"| Right decision
|}

(See [[#External Links|external link section]] for further information on [http://en.wikipedia.org/wiki/Statistical_hypothesis_testing statistical hypothesis testing])

The statistical testing process can be compared to the criminal justice system where an accused is judged guilty or innocent. During the trial the defense tries to show that the null hypothesis is true, the prosecution advocates the alternative hypothesis.

The population of innocents is generated by a combination of null hypothesis and test statistic, and is called null distribution. 
In the article a plot of a null distribution is refered to as null plot.

The statistical test compares the accused to the known innocents, using a specific metric. To assess the guilt of the accused, the ratio of the innocent that look more guilty than the accused is computed. A type I error would be a convicted innocent and a type II error would be an acquitted guilty.

In statistics we use tests (e.g. ''t''-statistic) to calculate the probability (the ''p''-value) of rejecting or accepting the null hypothesis. When visual testing is used instead, the data is plotted and the visual difference measured (tested) by a human judge or jury.

== Protocols ==
[[Image:Rorschach_Protocol.JPG | 250px | thumb | '''Figure 1''' : ''Rorschach protocol'']] Within the paper two different protocols for graphical inference are presented (Rorschach and Line-Up) which are described in the following sections.

=== Rorschach ===
The Rorschach protocol (named after the [http://en.wikipedia.org/wiki/Rorschach_test Rorschach test], in which a subject has to interpret abstract ink blots) is used to calibrate the analysts intuition by showing only null plots.

An example of such a Rorschach is given in Figure 1: Nine histograms summarizing the accuracy at which 500 participants perform nine tasks. What do you see?

The goal of this operation is to train the senses to random deviations, and therefore reduce the effect of apophenia for the given type of visualisation. Although, in order to keep the analysts alert, plots of the real data may be interspersed.

=== Line-Up ===
[[Image:Line-Up-Cancer_Deaths_in_Texas.JPG | 450px | thumb | '''Figure 2''' : ''Line-up protocol'']]

The idea behind line-up (named after [http://en.wikipedia.org/wiki/Police_lineup the police lineup]) is showing the real data plot camouflaged by decoys. In case the observer is able to identify the real data, we can assume that it differs from the null plots. The line-up procedure consists of the following steps:
* generate ''n'' - 1 decoys (null datasets)
* make a plot of the decoys and the real data (positioning the real data plot ranomly)
* let an observer assess which plot shows the real data.

The probability (''p''-value) of such a line-up is easily calculated. A practicable ''n'' of 19 leads to a probability of 1/20 = 0.05 (classical ''p''-value) to pick the right plot by chance. To generate even more precise ''p''-values the judge (single observer) can be replaced by a jury.

It is desireable to perform the test in a double-blind environment with neither the observer(s) nor the administrator knowing the true plots. If one has not seen the data yet a self-administered test is possible. Following software was implemented to assist such a procedure.

=== Software ===
The above mentioned protocols have been implemented by the authors as an [http://en.wikipedia.org/wiki/R_%28programming_language%29 R-package] called <code>Nullabor</code>. This package is available for [https://github.com/ggobi/nullabor download] (as of 16 November 2010).

== External Links ==

=== Statistics ===
* [http://en.wikipedia.org/wiki/Statistics General overview of statistics] (en.Wikipedia.org)
* [http://en.wikipedia.org/wiki/Statistical_hypothesis_testing Statistical hypothesis testing] (en.Wikipedia.org)
* [http://en.wikipedia.org/wiki/Null-hypothesis The null hypothesis] (en.Wikipedia.org)
* [http://en.wikipedia.org/wiki/Normal_distribution Normal distribution] (en.Wikipedia.org)
* [http://en.wikipedia.org/wiki/Binomial_distribution Binomial distribution] (en.Wikipedia.org)
* [http://www.r-project.org/ Official R-homepage]
* [https://github.com/ggobi/nullabor Download-site for R-package Nullabor], last accessed November 16, 2010.

=== Other ===
* [http://vimeo.com/15791526 A Sesame Street interpretation of the line-up]
* [http://www.slideshare.net/hadley/graphical-inference-5732044 Interactive presentation of] [Wickham et al., 2010]

== References ==
* [Wickham et al., 2010] Hadley Wickham, Dianne Cook, Heike Hofmann, and Adreas Buja. [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434 Graphical Inference for Infovis]. IEEE Transaction on Visualization and Computer Graphics, 16(6):973-979, November/December 2010
* [Scheidegger, 2010] Carlos Eduardo Scheidegger. visualization, etc. - scivis, data vis, infovis and other. Posted on October 24, 2010. Last access on November 17, 2010. URL: [http://carlosscheidegger.wordpress.com/2010/10/24/visweek-papers-2-graphical-inference-for-infovis/ http://carlosscheidegger.wordpress.com/2010/10/24/visweek-papers-2-graphical-inference-for-infovis/]

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-17T13:34:29Z

InfoVis1011 9925916: null distribution/null plot

= Graphical Inference for Infovis =
The following article summarizes the work of [Wickham et al., 2010] on graphical inference, which has been highly appreciated by [Scheidegger, 2010]:
<blockquote>"If I had to decide on a single paper this year which I think people will easily remember in 10 years, this would be it."</blockquote>

== Introduction ==
Information visualization provides tools to uncover new relations in data. While these technique can be very effective they are jeopardized by apophenia, the human ability to detect patterns in noise. On the other hand, statistics provide methods that examine if such relationships can be deduced from sample data - or if the hypotheses are invalid and subsequently have to be rejected. Graphical inference tries to find a balance between these two methods: information visualisation to improve identification of new hypotheses, and statistics to reveal faulty conclusions.

The authors present two experimental [[#Protocols|protocols]], [[#Rorschach|Rorschach]] and [[#Line-Up|Line-Up]], which show how the techniques mentioned above (statistics and information visualisation) can be combined.

== Motivation ==
Statistical methods generally try to show if a hypothesis is true or not. More specifically statistic investigate whether a difference exists (testing) or how big the difference is (estimating). For graphical inference you want to know if a difference actually exists, thus graphical inference works as testing procedure.

=== Statistic Foundation ===
For such a statistic test one needs to define a so-called null hypothesis H0 which is tested against the alternative hypothesis H1. As statistics produce results based on probabilities, mistakes can happen. The two possible errors are classified as follows:

{| border bordercolor="lightgrey" bgcolor="#C0C0C0" cellspacing=0 cellpadding="10

|----- bgcolor="#C0C0C0"
!
! Null Hypothesis (H0) is true
! Alternative Hypothesis (H1) is true
|-----
| Null Hypothesis is accepted
| align="center"| Right decision
| align="center"| Type II Error False Negative
|-----
| Null Hypothesis is rejected
| align="center"| Type I Error False Positive
| align="center"| Right decision
|}

(See [[#External Links|external link section]] for further information on [http://en.wikipedia.org/wiki/Statistical_hypothesis_testing statistical hypothesis testing])

The statistical testing process can be compared to the criminal justice system where an accused is judged guilty or innocent. During the trial the defense tries to show that the null hypothesis is true, the prosecution advocates the alternative hypothesis.

The population of innocents is generated by a combination of null hypothesis and test statistic, and is called null distribution. 
In the article a plot of a null distribution is refered to as null plot.

The static test compares the accused to the known innocents, using a specific metric. To assess the guilt of the accused, the ratio of the innocent that look more guilty than the accused is computed. A type I error would be a convicted innocent and a type II error would be an acquitted guilty.

In statistics we use tests (e.g. ''t''-statistic) to calculate the probability (the ''p''-value) of rejecting or accepting the null hypothesis. When visual testing is used instead, the data is plotted and the visual difference measured (tested) by a human judge or jury.

== Protocols ==
[[Image:Rorschach_Protocol.JPG | 250px | thumb | '''Figure 1''' : ''Rorschach protocol'']] Within the paper two different protocols for graphical inference are presented (Rorschach and Line-Up) which are described in the following sections.

=== Rorschach ===
The Rorschach protocol (named after the [http://en.wikipedia.org/wiki/Rorschach_test Rorschach test], in which a subject has to interpret abstract ink blots) is used to calibrate the analysts intuition by showing only null plots.

An example of such a Rorschach is given in Figure 1: Nine histograms summarizing the accuracy at which 500 participants perform nine tasks. What do you see?

The goal of this operation is to train the senses to random deviations, and therefore reduce the effect of apophenia for the given type of visualisation. Although, in order to keep the analysts alert, plots of the real data may be interspersed.

=== Line-Up ===
[[Image:Line-Up-Cancer_Deaths_in_Texas.JPG | 450px | thumb | '''Figure 2''' : ''Line-up protocol'']]

The idea behind line-up (named after [http://en.wikipedia.org/wiki/Police_lineup the police lineup]) is showing the real data plot camouflaged by decoys. In case the observer is able to identify the real data, we can assume that it differs from the null plots. The line-up procedure consists of the following steps:
* generate ''n'' - 1 decoys (null datasets)
* make a plot of the decoys and the real data (positioning the real data plot ranomly)
* let an observer assess which plot shows the real data.

The probability (''p''-value) of such a line-up is easily calculated. A practicable ''n'' of 19 leads to a probability of 1/20 = 0.05 (classical ''p''-value) to pick the right plot by chance. To generate even more precise ''p''-values the judge (single observer) can be replaced by a jury.

It is desireable to perform the test in a double-blind environment with neither the observer(s) nor the administrator knowing the true plots. If one has not seen the data yet a self-administered test is possible. Following software was implemented to assist such a procedure.

=== Software ===
The above mentioned protocols have been implemented by the authors as an [http://en.wikipedia.org/wiki/R_%28programming_language%29 R-package] called <code>Nullabor</code>. This package is available for [https://github.com/ggobi/nullabor download] (as of 16 November 2010).

== External Links ==

=== Statistics ===
* [http://en.wikipedia.org/wiki/Statistics General overview of statistics] (en.Wikipedia.org)
* [http://en.wikipedia.org/wiki/Statistical_hypothesis_testing Statistical hypothesis testing] (en.Wikipedia.org)
* [http://en.wikipedia.org/wiki/Null-hypothesis The null hypothesis] (en.Wikipedia.org)
* [http://en.wikipedia.org/wiki/Normal_distribution Normal distribution] (en.Wikipedia.org)
* [http://en.wikipedia.org/wiki/Binomial_distribution Binomial distribution] (en.Wikipedia.org)
* [http://www.r-project.org/ Official R-homepage]
* [https://github.com/ggobi/nullabor Download-site for R-package Nullabor], last accessed November 16, 2010.

=== Other ===
* [http://vimeo.com/15791526 Sesame's street interpretation of the line-up]
* [http://www.slideshare.net/hadley/graphical-inference-5732044 Interactive presentation of] [Wickham et al., 2010]

== References ==
* [Wickham et al., 2010] Hadley Wickham, Dianne Cook, Heike Hofmann, and Adreas Buja. [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434 Graphical Inference for Infovis]. IEEE Transaction on Visualization and Computer Graphics, 16(6):973-979, November/December 2010
* [Scheidegger, 2010] Carlos Eduardo Scheidegger. visualization, etc. - scivis, data vis, infovis and other. Posted on October 24, 2010. Last access on November 17, 2010. URL: [http://carlosscheidegger.wordpress.com/2010/10/24/visweek-papers-2-graphical-inference-for-infovis/ http://carlosscheidegger.wordpress.com/2010/10/24/visweek-papers-2-graphical-inference-for-infovis/]

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-17T01:14:45Z

InfoVis1011 9925916:

= Graphical Inference for Infovis =
The following article summarizes the work of [Wickham et al., 2010] on graphical inference.

== Introduction ==
Information visualization provides tools to uncover new relations in data. While these technique can be very effective they are jeopardized by apophenia, the human ability to detect patterns in noise. On the other hand, statistics provide methods that examine if such relationships can be deduced from sample data - or if the hypotheses are invalid and subsequently have to be rejected. Graphical inference tries to find a balance between these two methods: information visualisation to improve identification of new hypotheses, and statistics to reveal faulty conclusions.

The authors present two experimental [[#Protocols|protocols]], [[#Rorschach|Rorschach]] and [[#Line-Up|Line-Up]], which show how the techniques mentioned above (statistics and information visualisation) can be combined.

== Motivation ==
Statistical methods generally try to show if a hypothesis is true or not. More specifically statistic investigate whether a difference exists (testing) or how big the difference is (estimating). For graphical inference you want to know if a difference actually exists, thus graphical inference works as testing procedure.

=== Statistic Foundation ===
For such a statistic test one needs to define a so-called null hypothesis H0 which is tested against the alternative hypothesis H1. As statistics produce results based on probabilities, mistakes can happen. The two possible errors are classified as follows:

{| border bordercolor="lightgrey" bgcolor="#C0C0C0" cellspacing=0 cellpadding="10

|----- bgcolor="#C0C0C0"
!
! Null Hypothesis (H0) is true
! Alternative Hypothesis (H1) is true
|-----
| Null Hypothesis is accepted
| align="center"| Right decision
| align="center"| Type II Error False Negative
|-----
| Null Hypothesis is rejected
| align="center"| Type I Error False Positive
| align="center"| Right decision
|}

(See [[#External Links|external link section]] for further information on [http://en.wikipedia.org/wiki/Statistical_hypothesis_testing statistical hypothesis testing])

The statistical testing process can be compared to the criminal justice system where an accused is judged guilty or innocent. During the trial the defense tries to show that the null hypothesis is true, the prosecution advocates the alternative hypothesis.

The static test compares the accused and known innocents, using a specific metric. To assess the guilt of the accused, the ration fo the innocent that look more guilty than the accused is computed. A type I error would be a convicted innocent and a type II error would be an acquitted guilty.

In statistics we use tests (e.g. ''t''-statistic) to calculate the probability (the ''p''-value) of rejecting or accepting the null hypothesis. When visual testing is used instead, the data is plotted and the visual difference measured (tested) by a human judge or jury.

== Protocols ==
Within the paper two different protocols for graphical inference are presented (Rorschach and Line-Up) which are described in the following sections.

=== Rorschach ===
[[Image:Rorschach_Protocol.JPG | 250px | thumb | '''Figure 1''' : ''Rorschach protocol'']]
The Rorschach protocol (named after the [http://en.wikipedia.org/wiki/Rorschach_test Rorschach test], in which a subject has to interpret abstract ink blots) is used to calibrate the analysts intuition by showing only null plots.

An example of such a Rorschach is given in Figure 1: Nine histograms summarizing the accuracy at which 500 participants perform nine tasks. What do you see?

The goal of this operation is to train the senses to random deviations, and therefore reduce the effect of apophenia for the given type of visualisation. Although, in order to keep the analysts alert, plots of the real data may be interspersed.

=== Line-Up ===
[[Image:Line-Up-Cancer_Deaths_in_Texas.JPG | 450px | thumb | '''Figure 2''' : ''Line-up protocol'']]

The idea behind line-up (named after [http://en.wikipedia.org/wiki/Police_lineup the police lineup]) is showing the real data plot camouflaged by decoys. In case the observer is able to identify the real data, we can assume that it differs from the null plots. The line-up procedure consists of the following steps:
* generate ''n'' - 1 decoys (null datasets)
* make a plot of the decoys and the real data (positioning the real data plot ranomly)
* let an observer assess which plot shows the real data.

The probability (''p''-value) of such a line-up is easily calculated. A practicable ''n'' of 19 leads to a probability of 1/20 = 0.05 (classical ''p''-value) to pick the right plot by chance. To generate even more precise ''p''-values the judge (single observer) can be replaced by a jury.

It is desireable to perform the test in a double-blind environment with neither the observer(s) nor the administrator knowing the true plots. If one has not seen the data yet a self-administered test is possible. Following software was implemented to assist such a procedure.

=== Software ===
The above mentioned protocols have been implemented by the authors as an [http://en.wikipedia.org/wiki/R_%28programming_language%29 R-package] called <code>Nullabor</code>. This package is available for [https://github.com/ggobi/nullabor download] (as of 16 November 2010).

== External Links ==

=== Statistics ===
* [http://en.wikipedia.org/wiki/Statistics General overview of statistics]
* [http://en.wikipedia.org/wiki/Statistical_hypothesis_testing Statistical hypothesis testing]
* [http://en.wikipedia.org/wiki/Null-hypothesis The null hypothesis]
* [http://en.wikipedia.org/wiki/Normal_distribution Normal distribution]
* [http://en.wikipedia.org/wiki/Binomial_distribution Binomial distribution]
* [http://www.r-project.org/ Official R-homepage]
* [https://github.com/ggobi/nullabor Download-site for R-package Nullabor], accessed 16 November 2010.

=== Other ===
* [http://vimeo.com/15791526 Sesame's street interpretation of the line-up]
* [http://www.slideshare.net/hadley/graphical-inference-5732044 Interactive presentation of] [Wickham et al., 2010]

== References ==
* [Wickham et al., 2010] Hadley Wickham, Dianne Cook, Heike Hofmann, and Adreas Buja. Graphical Inference for Infovis. [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434 IEEE Transaction on Visualization and Computer Graphics], 16(6):973-979, November/December 2010

File:Line-Up-Cancer Deaths in Texas.JPG

2010-11-17T00:03:08Z

InfoVis1011 9925916: Graphical Inference for InfoVis, Line-up: One of these plots doesn’t belong. These six plots show choropleth maps of cancer deaths in Texas, where darker colors = more deaths. Can you spot which of the six plots is made from a real dataset and not simul

== Summary ==
Graphical Inference for InfoVis, Line-up: One of these plots doesn’t belong. These six plots show choropleth maps of cancer deaths in Texas, where darker colors =
more deaths. Can you spot which of the six plots is made from a real dataset and not simulated under the null hypothesis of spatial
independence? If so, you’ve provided formal statistical evidence that deaths from cancer have spatial dependence. See Section 8 for the answer.
== Copyright status ==
[Wickham et al., 2010]
== Source ==
[Wickham et al., 2010] Hadley Wickham, Dianne Cook, Heike Hofmann, and Adreas Buja. Graphical Inference for Infovis. IEEE Transaction on Visualization and Computer Graphics, 16(6):973-979 (Fig.1), November/December 2010

File:Rorschach Protocol.JPG

2010-11-16T20:21:04Z

InfoVis1011 9925916: Rorschach protocol: Nine histograms summarizing the accuracy at which 500 participants perform nine tasks. What do you see?

== Summary ==
Rorschach protocol: Nine histograms summarizing the accuracy at which 500 participants perform nine tasks. What do you see?
== Copyright status ==
[Wickham et al., 2010]
== Source ==
[Wickham et al., 2010] Hadley Wickham, Dianne Cook, Heike Hofmann, and Adreas Buja. Graphical Inference for Infovis. IEEE Transaction on Visualization and Computer Graphics, 16(6):973-979 (Fig.4), November/December 2010

File:Fig4-Rorschach.JPG

2010-11-16T20:15:47Z

InfoVis1011 9925916: Rorschach protocol: Nine histograms summarizing the accuracy at which 500 participants perform nine tasks. What do you see?

Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-16T19:09:20Z

InfoVis1011 9925916:

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-16T19:05:08Z

InfoVis1011 9925916:

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-16T18:52:48Z

InfoVis1011 9925916:

Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-16T18:51:41Z

InfoVis1011 9925916:

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-16T18:49:47Z

InfoVis1011 9925916:

Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-16T14:13:14Z

InfoVis1011 9925916:

Euer Artikel:

Graphical Inference for Infovis
Hadley Wickham, Dianne Cook, Heike Hofmann, Andreas Buja
IEEE Transactions on Visualization and Computer Graphics (TVCG)
November/December 2010 (vol. 16 no. 6):973-979, 2010.

[http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434] (Zugriff im TU Netzwerk)

-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 07:42, 08 November 2010 (CEST)

== Änderungsvorschläge ==
größere Änderungen habe ich nicht eingearbeitet, da sonst das Original verloren gehen würde:
=== Introduction ===
da das Erkennen von nicht vorhandenen Mustern ja ausgeschlossen werden soll, würde ich es umformulieren:

Information visualization provides tools to show new relations in data. While this technique can be very effictive, it is threatened by apophenia, the human ability to detect patterns in noise.
Statistics instead provides methods that can examine if an assumption can be deduced from given sample data, and is therefore used to expose invalid hypotheses.
Graphical inference tries to find a balance between these two methods: information visualisation to improve identification of new hypotheses, and statistics to reveal faulty conclusions.

The authors present two experimental protocols, Rorschach and Line-Up, which show how both techniques can be combined.

=== Rorschach ===
The Rorschach protocol was named after the Rorschach test, in which a proband has to interpret abstract ink blots.
Similar to that, for the Rorschach protocol a series of null plots is generated and presented to the proband, who is then asked to find patterns in the visualisations.

The goal of this operation is to train the senses to random deviations, and therefore reduce the effect of apophenia for the given type of visualisation.

== Additional Resources ==

=== Fun and Trivia ===
* [http://carlosscheidegger.wordpress.com/2010/10/24/visweek-papers-2-graphical-inference-for-infovis/ blog-o-sphere reactions 01]
* [http://vimeo.com/15791526 sesame street's Line-up ]
[[User:InfoVis1011 9925916|InfoVis1011 9925916]] 12:28, 16 November 2010 (CET)

Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-16T14:09:50Z

InfoVis1011 9925916:

Euer Artikel:

Graphical Inference for Infovis
Hadley Wickham, Dianne Cook, Heike Hofmann, Andreas Buja
IEEE Transactions on Visualization and Computer Graphics (TVCG)
November/December 2010 (vol. 16 no. 6):973-979, 2010.

[http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434] (Zugriff im TU Netzwerk)

-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 07:42, 08 November 2010 (CEST)

== Änderungsvorschläge ==
größere Änderungen habe ich nicht eingearbeitet, da sonst das Original verloren gehen würde:
=== Introduction ===
da das Erkennen von nicht vorhandenen Mustern ja ausgeschlossen werden soll, würde ich es umformulieren:

Information visualization provides tools to show new relations in data. While this technique can be very effictive, it is threatened by apophenia, the human capability to detect patterns in random noise.
Statistics instead provides methods that can examine if an assumption can be deduced from given sample data, and is therefore used to expose invalid hypotheses.
Graphical inference tries to find a balance between these two methods: information visualisation to improve identification of new hypotheses, and statistics to reveal faulty conclusions.

The authors present two experimental protocols, Rorschach and Line-Up, which show how both techniques can be combined.

=== Rorschach ===
The Rorschach protocol was named after the Rorschach test, in which a proband has to interpret abstract ink blots.
Similar to that, for the Rorschach protocol a series of null plots is generated and presented to the proband, who is then asked to find patterns in the visualisations.

The goal of this operation is to train the senses to random deviations, and therefore reduce the effect of apophenia for the given type of visualisation.

== Additional Resources ==

=== Fun and Trivia ===
* [http://carlosscheidegger.wordpress.com/2010/10/24/visweek-papers-2-graphical-inference-for-infovis/ blog-o-sphere reactions 01]
* [http://vimeo.com/15791526 sesame street's Line-up ]
[[User:InfoVis1011 9925916|InfoVis1011 9925916]] 12:28, 16 November 2010 (CET)

Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-16T14:07:19Z

InfoVis1011 9925916:

Euer Artikel:

Graphical Inference for Infovis
Hadley Wickham, Dianne Cook, Heike Hofmann, Andreas Buja
IEEE Transactions on Visualization and Computer Graphics (TVCG)
November/December 2010 (vol. 16 no. 6):973-979, 2010.

[http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434] (Zugriff im TU Netzwerk)

-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 07:42, 08 November 2010 (CEST)

== Änderungsvorschläge ==

=== Introduction ===
da das Erkennen von nicht vorhandenen Mustern ja ausgeschlossen werden soll, würde ich es umformulieren:

Information visualization provides tools to show new relations in data. While this technique can be very effictive, it is threatened by apophenia, the human capability to detect patterns in random noise.
Statistics instead provides methods that can examine if an assumption can be deduced from given sample data, and is therefore used to expose invalid hypotheses.
Graphical inference tries to find a balance between these two methods: information visualisation to improve identification of new hypotheses, and statistics to reveal faulty conclusions.

The authors present two experimental protocols, Rorschach and Line-Up, which show how both techniques can be combined.

=== Rorschach ===
The Rorschach protocol was named after the Rorschach test, in which a proband has to interpret abstract ink blots.
Similar to that, for the Rorschach protocol a series of null plots is generated, and presented to the proband, who is then asked to find patterns in the visualisations.

The goal of this operation is to train the senses to random deviations, and therefore reduce the effect of apophenia for the given type of visualisation.

== Additional Resources ==

=== Fun and Trivia ===
* [http://carlosscheidegger.wordpress.com/2010/10/24/visweek-papers-2-graphical-inference-for-infovis/ blog-o-sphere reactions 01]
* [http://vimeo.com/15791526 sesame street's Line-up ]
[[User:InfoVis1011 9925916|InfoVis1011 9925916]] 12:28, 16 November 2010 (CET)

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-16T13:36:40Z

InfoVis1011 9925916:

= Graphical Inference for Infovis =

The following article summarizes the work of [Wickham et al., 2010] on graphical inference.

== Introduction ==

Information visualization provides tools to show new relations in data. Statistics instead provides methods that can examine if an assumption is correct or not. Graphical inference tries to find a balance between these two methods. With the help of apophenia, the capability of human to detect patterns in noise, hypotheses can be established. The goal of graphical inference is, as in statistics, to reveal faulty conclusions.

== Motivation ==

Statistical methods generally try to show that a hypothesis is true or not. More specifically statistic investigate whether a difference exists (testing) or how big the difference is (estimating). For graphical inference you want to know whether a difference is actually here, so graphical inference works as testing procedure. In statistics this is called a null hypothesis H0 (the situation) and the alternative hypothesis (the assumption). The result of a statistical test can take two fault conditions:

* false positive: H0 is rejected, although a H1 is not true (also called type I error)
* false negative: H0 is not rejected, although a H1 is true (also called type II error)

The testing process in statistics can be compared with the criminal justice system, where an accused is judged guilty or innocent. During the trial the defense tries to show that the null hypothesis is true, the prosecution advocates the alternative hypothesis.

The static test compares the accused and known innocents, using a specific metric. To assess the guilt of the accused, the ration fo the innocent that look more guilty than the accused is computed. A type I error would be a convicted innocent and a type II error would be an acquitted guilty.

In statistics we use test statistcs (like the t-statistic)to calculate the propapility (the p-value) that the decision for the alternate hypothesis is wrong. When we use visual testing instead, the data is ploted and the visual difference is measured by a human judge or jury.

== Protocols ==

Two different protocols are presented: Rorschach and Line-Up

=== Rorschach ===

The Rorschach protocol was named after the Rorschach test where a testing person tries to give ink blots a meaning. The testing person is asked what she sees in the null plots. Target of the operation is to train the senses to random deviations.

=== Line-Up ===

The idea behind line-up is to show the real data plot together with decoys. When the observer is able to identificate the real data, we can assume that the real data differs. The line-up consists of the following steps:
* generate n - 1 decoys
* make a plot of the decoys together with a plot of the real data (positioning the real data plot ranomly)
* let an observer assess, which data shows the deviation.

== References ==

* [Wickham et al., 2010] Hadley Wickham, Dianne Cook, Heike Hofmann, and Adreas Buja. Graphical Inference for Infovis. IEEE Transaction on Visualization and Computer Graphics, 16(6):973-979, November/December 2010

Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-16T13:27:40Z

InfoVis1011 9925916:

Euer Artikel:

Graphical Inference for Infovis
Hadley Wickham, Dianne Cook, Heike Hofmann, Andreas Buja
IEEE Transactions on Visualization and Computer Graphics (TVCG)
November/December 2010 (vol. 16 no. 6):973-979, 2010.

[http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434] (Zugriff im TU Netzwerk)

-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 07:42, 08 November 2010 (CEST)

== Änderungsvorschläge ==

=== Introduction ===
da das Erkennen von nicht vorhandenen Mustern ja ausgeschlossen werden soll, würde ich es umformulieren:

Information visualization provides tools to show new relations in data. While this technique can be very effictive, it is threatened by apophenia, the human capability to detect patterns in random noise.
Statistics instead provides methods that can examine if an assumption can be deduced from given sample data, and is therefore used to expose invalid hypotheses.
Graphical inference tries to find a balance between these two methods: information visualisation to improve identification of new hypotheses, and statistics to reveal faulty conclusions.

The authors present two experimental protocols, Rorschach and Line-Up, which show how both techniques can be combined.

== Additional Resources ==

=== Fun and Trivia ===
* [http://carlosscheidegger.wordpress.com/2010/10/24/visweek-papers-2-graphical-inference-for-infovis/ blog-o-sphere reactions 01]
* [http://vimeo.com/15791526 sesame street's Line-up ]
[[User:InfoVis1011 9925916|InfoVis1011 9925916]] 12:28, 16 November 2010 (CET)

Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-16T13:26:20Z

InfoVis1011 9925916:

Euer Artikel:

Graphical Inference for Infovis
Hadley Wickham, Dianne Cook, Heike Hofmann, Andreas Buja
IEEE Transactions on Visualization and Computer Graphics (TVCG)
November/December 2010 (vol. 16 no. 6):973-979, 2010.

[http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434] (Zugriff im TU Netzwerk)

-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 07:42, 08 November 2010 (CEST)

== Änderungsvorschläge ==

=== Introduction ===
da das Erkennen von nicht vorhandenen Mustern ja ausgeschlossen werden soll, würde ich es umformulieren:

Information visualization provides tools to show new relations in data. While this technique can be very effictive, it is threatened by apophenia, the human capability to detect patterns in random noise.
Statistics instead provides methods that can examine if an assumption can be approved by the given data sample, and is therefore used to expose invalid hypotheses.
Graphical inference tries to find a balance between these two methods: information visualisation to improve identification of new hypotheses, and statistics to reveal faulty conclusions.

The authors present two experimental protocols, Rorschach and Line-Up, which show how both techniques can be combined.

== Additional Resources ==

=== Fun and Trivia ===
* [http://carlosscheidegger.wordpress.com/2010/10/24/visweek-papers-2-graphical-inference-for-infovis/ blog-o-sphere reactions 01]
* [http://vimeo.com/15791526 sesame street's Line-up ]
[[User:InfoVis1011 9925916|InfoVis1011 9925916]] 12:28, 16 November 2010 (CET)

Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-16T13:06:36Z

InfoVis1011 9925916:

Euer Artikel:

Graphical Inference for Infovis
Hadley Wickham, Dianne Cook, Heike Hofmann, Andreas Buja
IEEE Transactions on Visualization and Computer Graphics (TVCG)
November/December 2010 (vol. 16 no. 6):973-979, 2010.

[http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434] (Zugriff im TU Netzwerk)

-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 07:42, 08 November 2010 (CEST)

== Änderungsvorschläge ==

=== Introduction ===
da das Erkennen von nicht vorhandenen Mustern ja ausgeschlossen werden soll, würde ich es umformulieren:

Information visualization provides tools to show new relations in data. While this technique can be very effictive, it is threatened by apophenia, the human capability to detect patterns in random noise.
Statistics instead provides methods that can examine if an assumption can be approved by the given data sample, and is therefore used to expose invalid hypotheses.
Graphical inference tries to find a balance between these two methods: use of information visualisation to increase finding new hypotheses, and statistics to reveal faulty conclusions.

The authors present two experimental protocols, Rorschach and Line-Up, which show how both approaches can be combined.

== Additional Resources ==

=== Fun and Trivia ===
* [http://carlosscheidegger.wordpress.com/2010/10/24/visweek-papers-2-graphical-inference-for-infovis/ blog-o-sphere reactions 01]
* [http://vimeo.com/15791526 sesame street's Line-up ]
[[User:InfoVis1011 9925916|InfoVis1011 9925916]] 12:28, 16 November 2010 (CET)

Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-16T13:06:13Z

InfoVis1011 9925916:

Euer Artikel:

Graphical Inference for Infovis
Hadley Wickham, Dianne Cook, Heike Hofmann, Andreas Buja
IEEE Transactions on Visualization and Computer Graphics (TVCG)
November/December 2010 (vol. 16 no. 6):973-979, 2010.

[http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434] (Zugriff im TU Netzwerk)

-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 07:42, 08 November 2010 (CEST)

== Änderungsvorschläge ==

=== Introduction ===
da das Erkennen von nicht vorhandenen Mustern ja ausgeschlossen werden soll, würde ich es umformulieren:

Information visualization provides tools to show new relations in data. While this technique can be very effictive, it is threatened by apophenia, the human capability to detect patterns in random noise.
Statistics instead provides methods that can examine if an assumption can be approved by the given data sample, and is therefore used to expose invalid hypotheses.
Graphical inference tries to find a balance between these two methods: use of information visualisation to increase finding new hypotheses, and statistics to reveal faulty conclusions.
The authors present two experimental protocols, Rorschach and Line-Up, which show how both approaches can be combined.

== Additional Resources ==

=== Fun and Trivia ===
* [http://carlosscheidegger.wordpress.com/2010/10/24/visweek-papers-2-graphical-inference-for-infovis/ blog-o-sphere reactions 01]
* [http://vimeo.com/15791526 sesame street's Line-up ]
[[User:InfoVis1011 9925916|InfoVis1011 9925916]] 12:28, 16 November 2010 (CET)

Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-16T12:28:45Z

InfoVis1011 9925916:

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-16T12:04:00Z

InfoVis1011 9925916:

= Graphical Inference for Infovis =

The following article summarizes the work of [Wickham et al., 2010] on graphical inference.

== Introduction ==

Information visualization provides tools to show new relations in data. Statistics instead provides methods that can examine if an assumption is correct or not. Graphical inference tries to find a balance between these two methods. With the help of apophenia, the capability of human to detect patterns in noise, hypotheses can be established. The goal of graphical inference is, as in statistics, to reveal faulty conclusions.

== Motivation ==

Statistical methods generally try to show that a hypothesis is true or not. More specifically statistic investigate whether a difference exists (testing) or how big the difference is (estimating). For graphical inference you want to know whether a difference is actually here, so graphical inference works as testing procedure. In statistics this is called a null hypothesis H0 (the situation) and the alternative hypothesis (the assumption). The result of a statistical test can take two fault conditions:

* a H0 is rejected, although a H1 is not true (also called type I error)
* a H0 is no rejected, although a H1 is true (also called type II error)

The testing process in statistics can be compared with the criminal justice system, where an accused is judged guilty or innocent. During the trial the defense tries to show that the null hypothesis is true, the prosecution advocates the alternative hypothesis.

The static test compares the accused and known innocents, using a specific metric. To assess the guilt of the accused, the ration fo the innocent that look more guilty than the accused is computed. A type I error would be a convicted innocent and a type II error would be an acquitted guilty.

In statistics we use test statistcs (like the t-statistic)to calculate the propapility (the p-value) that the decision for the alternate hypothesis is wrong. When we use visual testing instead, the data is ploted and the visual difference is measured by a human judge or jury.

== Protocols ==

Two different protocols are presented: Rorschach and Line-Up

=== Rorschach ===

The Rorschach protocol was named after the Rorschach test where a testing person tries to give ink blots a meaning. The testing person is asked what she sees in the null plots. Target of the operation is to train the senses to random deviations.

=== Line-Up ===

The idea behind line-up is to show the real data plot together with decoys. When the observer is able to identificate the real data, we can assume that the real data differs. The line-up consists of the following steps:
* generate n - 1 decoys
* make a plot of the decoys together with a plot of the real data (positioning the real data plot ranomly)
* let an observer assess, which data shows the deviation.

== References ==

* [Wickham et al., 2010] Hadley Wickham, Dianne Cook, Heike Hofmann, and Adreas Buja. Graphical Inference for Infovis. IEEE Transaction on Visualization and Computer Graphics, 16(6):973-979, November/December 2010

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-16T11:57:34Z

InfoVis1011 9925916:

= Graphical Inference for Infovis =

The following article summarizes the work of [Wickham et al., 2010] on graphical inference.

== Introduction ==

Information visualization provides tools to show new relations in data. Statistics instead provides methods that can examine if an assumption is correct or not. Graphical inference tries to find a balance between these two methods. With the help of apophenia, the capability of human to detect patterns in noise, hypotheses can be established. The goal of graphical inference is, as in statistics, to reveal faulty conclusions.

== Motivation ==

Statistical methods generally try to show that a hypothesis is true or not. More specifically statistic investigate whether a difference exists (testing) or how big the difference is (estimating). For graphical inference you want to know whether a difference is actually here, so graphical inference works as testing procedure. In statistics this is called a null hypothesis H0 (the situation) and the alternative hypothesis (the assumption). The result of a statistical test can take two fault conditions:

* a H0 is rejected, although a H1 is not true (also called type I error)
* a H0 is no rejected, although a H1 is true (also called type II error)

The testing process in statistics can be compared with the criminal justice system, where an accused is judged guilty or innocent. During the trial the defense tries to show that the null hypothesis is true, the prosecution advocates the alternative hypothesis.

The static test compares the accused and known innocents, using a specific metric. To assess the guilt of the accused, the ration fo the innocent that look more guilty than the accused is computed. A type I error would be a convicted innocent and a type II error would be an acquitted guilty.

In statistics we use test statistcs (like the t-statistic)to calculate the propapility (the p-value) that the decision for the alternate hypothesis is wrong. When we use visual testing instead, the data is ploted and the visual difference is measured by a human judge or jury.

== Protocols ==

Two different protocols are presented: Rorschach and Line-Up

=== Rorschach ===

The Rorschach protocol was named after the Rorschach test where a testing person tries to give ink blots a meaning. The testing person is asked what she sees in the null plots. Target of the operation is to train the senses to random deviations.

=== Line-Up ===

The idea behind line-up is to show the real data plot together with decoys. When the observer is able to identificate the real data, we can assume that the real data differs. The line-up consists of the following steps:
* generate n - 1 decoys
* make a plot of the decoys together with a plot of the real data (positioning the real data plot ranomly)
* let an observer assess, which data shows the deviation.

== References ==

* [Wickham et al., 2010] Hadley Wickham, Dianne Cook, Heike Hofmann, and Adreas Buja. Graphical Inference for Infovis. IEEE Transaction on Visualization and Computer Graphics, Vol. 16, No. 6, November/December 2010

Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

2010-11-16T11:28:13Z

InfoVis1011 9925916:

Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 01

2010-10-31T13:35:06Z

InfoVis1011 9925916:

bitte an die anderen gruppenmitglieder:
eure namen auch auf der [[Teaching:TUW - UE InfoVis WS 2010/11|start-seite]] der UE bei der Gruppe eintragen. danke! 
-- [[User:UE-InfoVis1011 0026203|Štefan EMRICH]] 13:00, 14 October 2010 (CEST)
 
Punkte: 5 von 5 
-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 10:19, 27 October 2010 (CEST)

yeah! 100% :) 
-- [[User:UE-InfoVis1011 0026203|Štefan EMRICH]] 10:22, 27 October 2010 (CEST)

passt!
anscheinend sind wir zu zweit, vielleicht kann man uns mit einer Gruppe mit einer Person fusionieren...

ich nehm an, dass das von seiten der LVA-leitung/-betreuung geschehen wird. oder kennst du eineN der/die zu uns rein will/soll (um es korrekt gegendert zu haben ;)?
 
-- [[User:UE-InfoVis1011 0026203|Štefan EMRICH]] 08:17, 28 October 2010 (CEST)

ich schlage vor, dass Michael Kraxner zu euch kommt. Wie gesagt, falls ihr andere Präferenzen habt 
und die Gruppen sich untereinander einigen können, ist uns das auch recht. 
-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 15:23, 28 October 2010 (CEST)

ist in ordnung für mich
 
-- [[User:UE-InfoVis1011 0026203|Štefan EMRICH]] 15:46, 28 October 2010 (CEST)

fein :) hab mich jetzt für eure/unsere Gruppe eingetragen 
-- [[User:InfoVis1011 9925916|Michael Kraxner]] 14:32, 31 October 2010 (CET)

Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 01

2010-10-31T13:32:50Z

InfoVis1011 9925916:

bitte an die anderen gruppenmitglieder:
eure namen auch auf der [[Teaching:TUW - UE InfoVis WS 2010/11|start-seite]] der UE bei der Gruppe eintragen. danke! 
-- [[User:UE-InfoVis1011 0026203|Štefan EMRICH]] 13:00, 14 October 2010 (CEST)
 
Punkte: 5 von 5 
-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 10:19, 27 October 2010 (CEST)

yeah! 100% :) 
-- [[User:UE-InfoVis1011 0026203|Štefan EMRICH]] 10:22, 27 October 2010 (CEST)

passt!
anscheinend sind wir zu zweit, vielleicht kann man uns mit einer Gruppe mit einer Person fusionieren...

ich nehm an, dass das von seiten der LVA-leitung/-betreuung geschehen wird. oder kennst du eineN der/die zu uns rein will/soll (um es korrekt gegendert zu haben ;)?
 
-- [[User:UE-InfoVis1011 0026203|Štefan EMRICH]] 08:17, 28 October 2010 (CEST)

ich schlage vor, dass Michael Kraxner zu euch kommt. Wie gesagt, falls ihr andere Präferenzen habt 
und die Gruppen sich untereinander einigen können, ist uns das auch recht. 
-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 15:23, 28 October 2010 (CEST)

ist in ordnung für mich
 
-- [[User:UE-InfoVis1011 0026203|Štefan EMRICH]] 15:46, 28 October 2010 (CEST)

fein :) hab mich jetzt für eure/unsere Gruppe eingetragen 
[[User:InfoVis1011 9925916|InfoVis1011 9925916]] 14:32, 31 October 2010 (CET)

Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 02

2010-10-31T13:23:40Z

InfoVis1011 9925916: test

Punkte: 5 von 5 
-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 10:19, 27 October 2010 (CEST)

== test ==

1,2,3

Teaching:TUW - UE InfoVis WS 2010/11

2010-10-31T13:03:05Z

InfoVis1011 9925916: moved Kraxner to Gruppe 01

[[Image:Aigner03infovis ue.gif]] <big>WS 2010/11</big>

'''LVA Nr:''' 188.308 ([https://tiss.tuwien.ac.at/course/courseDetails.xhtml?courseNr=188308&semester=2010W TISS Seite])

'''LVA Homepage:''' http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/index.html

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

== Gruppen ==

*[[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01|Gruppe 01 (Emrich, Schneider, Kraxner)]]
*[[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 03|Gruppe 03 (Leichtfried, Chwistek, Kastner)]]
*[[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05|Gruppe 05 (Alili, Bachhuber, Marschik)]]
*[[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 06|Gruppe 06 (Fikar, Posset, ???)]]
*[[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 07|Gruppe 07 (Nezveda, ???, ???)]]

== News / Bemerkungen ==
Liebe Studierende, 
die Grafiken für Aufgabe 1 (Workshop) sind [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe1.html online]. 
-- [[User:Iwolf|Wolfgang Aigner]] 11:46, 28 October 2010 (CEST)

Liebe TeilnehmerInnen! 
Die Aufgabe 0 habt ihr erfolgreich abgeschlossen (die Punkte findet ihr auf der jeweiligen Talk Seite zu eurer Gruppenseite). 
Bitte füllt die Gruppen auf, damit wir in 3er Gruppen weiterarbeiten können. Ich schlage vor:
Michael Kraxner zu Gruppe 1,
Philipp Kastner zu Gruppe 3 und
Matej Nezveda zu Gruppe 6.
Falls ihr euch intern einigt wer wohin wechselt ist uns das auch recht. 
Die zu verbessernden Grafiken für Aufgabe 1 findet ihr auf der LVA Homepage: http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe1.html. 
-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 12:39, 27 October 2010 (CEST)

Liebe TeilnehmerInnen! 
Um diese Seite einheitlich zu gestalten (auch bezüglich der Vorjahre), schlage ich vor die Nachnamen
der Gruppenmitglieder in Klammer neben der Gruppe anzugeben, 
z.B.: Gruppe XX (Maier, Müller, Mustermann). 
-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 10:34, 08 October 2010 (CEST)

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01

2010-10-31T12:56:11Z

InfoVis1011 9925916: moved to this group

Stub der Gruppe 01 für die [[Teaching:TUW - UE InfoVis WS 2010/11|UE InfoVis (188.308)]] im WS 2010/11.

== Gruppenmitglieder ==
*[[User:UE-InfoVis1011 0026203|Emrich, Štefan]]
*[[User:UE-InfoVis1011_0326062|Schneider, Thomas]]
*[[User:InfoVis1011 9925916 | Kraxner, Michael]]
== Aufgaben ==
*Aufgabe 0 - ist hiermit abgeschlossen.
*[[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 1|Aufgabe 1]]
*[[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2|Aufgabe 2]]
*[[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 3|Aufgabe 3]]

== Externe Links ==
*[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe0.html Übungsaufgabe 0 - Angabe]
*[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe1.html Übungsaufgabe 1 - Angabe]
*[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe2.html Übungsaufgabe 2 - Angabe]
*[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe3.html Übungsaufgabe 3 - Angabe]

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02

2010-10-22T20:15:22Z

InfoVis1011 9925916:

Übersichtsseite der Gruppe 02 für die [[Teaching:TUW_-_UE_InfoVis_WS_2010/11|UE InfoVis WS 2010/11]]

== Gruppenmitglieder ==
* [[User:InfoVis1011 9925916 | Kraxner, Michael]]

== Aufgaben ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2010/11_-_Gruppe_02|Aufgabe 0]]
* [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02 - Aufgabe 1|Aufgabe 1]]
* [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02 - Aufgabe 2|Aufgabe 2]]
* [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02 - Aufgabe 3|Aufgabe 3]]

== Aufgabenstellungen ==

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe0.html Aufgabe 0]
* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe1.html Aufgabe 1]
* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe2.html Aufgabe 2]
* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe3.html Aufgabe 3]

User:InfoVis1011 9925916

2010-10-22T20:13:20Z

InfoVis1011 9925916: added picture

<big>'''Michael Kraxner'''</big>

[[Image:MKraxner.JPG|200px]]
== Affiliation ==

* [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02|Gruppe 02 (Kraxner, ???, ???)]]
* [http://www.tuwien.ac.at Vienna University of Technology] 
* [http://ifs.tuwien.ac.at Information & Software Engineering Group]

[[CATEGORY: Persons]]

File:MKraxner.JPG

2010-10-22T19:58:05Z

InfoVis1011 9925916: Michael Kraxner, 2010

== Summary ==
Michael Kraxner, 2010
== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02

2010-10-22T19:46:36Z

InfoVis1011 9925916:

Informationsseite der Gruppe 02 für die [[Teaching:TUW_-_UE_InfoVis_WS_2010/11|UE InfoVis WS 2010/11]]

== Gruppenmitglieder ==
* [[User:InfoVis1011 9925916 | Kraxner, Michael]]

== Aufgaben ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2010/11_-_Gruppe_02|Aufgabe 0]]
* [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02 - Aufgabe 1|Aufgabe 1]]
* [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02 - Aufgabe 2|Aufgabe 2]]
* [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02 - Aufgabe 3|Aufgabe 3]]

== Aufgabenstellungen ==

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe0.html Aufgabe 0]
* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe1.html Aufgabe 1]
* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe2.html Aufgabe 2]
* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe3.html Aufgabe 3]

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02 - Aufgabe 3

2010-10-22T19:44:41Z

InfoVis1011 9925916: Stub info

Diese Seite wird Informationen zur Aufgabe 3 der Gruppe 02 für die [[Teaching:TUW_-_UE_InfoVis_WS_2010/11|UE InfoVis WS 2010/11]] enthalten

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02 - Aufgabe 2

2010-10-22T19:44:15Z

InfoVis1011 9925916: Stub info

Diese Seite wird Informationen zur Aufgabe 2 der Gruppe 02 für die [[Teaching:TUW_-_UE_InfoVis_WS_2010/11|UE InfoVis WS 2010/11]] enthalten

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02 - Aufgabe 1

2010-10-22T19:43:30Z

InfoVis1011 9925916: Stub info

Diese Seite wird Informationen zur Aufgabe 1 der Gruppe 02 für die [[Teaching:TUW_-_UE_InfoVis_WS_2010/11|UE InfoVis WS 2010/11]] enthalten

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02

2010-10-22T19:32:40Z

InfoVis1011 9925916: Hinzufügen der Aufgabenstellungen

Informationsseite der Gruppe 02 für die [[Teaching:TUW_-_UE_InfoVis_WS_2010/11|UE InfoVis WS 2010/11]]

== Gruppenmitglieder ==
* [[User:InfoVis1011 9925916 | Kraxner, Michael]]

== Aufgaben ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2010/11_-_Gruppe_02|Aufgabe 0]]
* [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02 - Aufgabe 1|Aufgabe 1]]
* [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02 - Aufgabe 2|Aufgabe 2]]
* [[Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02 - Aufgabe 3|Aufgabe 3]]

== Aufgabenstellung ==

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe0.html Aufgabe 0]
* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe1.html Aufgabe 1]
* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe2.html Aufgabe 2]
* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws10/infovis_ue_aufgabe3.html Aufgabe 3]

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02 - Aufgabe 0

2010-10-22T19:23:13Z

InfoVis1011 9925916: Removing all content from page

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02 - Aufgabe 0

2010-10-22T19:20:17Z

InfoVis1011 9925916: New page: Informationsseite der Gruppe 02 für die UE InfoVis WS 2010/11 == Gruppenmitglieder == * Kraxner, Michael == Aufga...

Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 02

2010-10-22T19:18:12Z

InfoVis1011 9925916: