visualization design: how to design and address data visualizations

8/3/2019 Visualization Design: How to Design and Address Data Visualizations

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Visualization Design How to design and address data visualizations


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2011

This assignment was written for school purpose.

University of Utrecht // Faculty of HumanitiesDegree/program // MA New Media & Digital CultureCourse // Get Real!

StudentSalko Joost Kattenberg // 3614875Paper // Visualization Design: How to design and address data visualizations.

SupervisorAnn-Sophie Lehman // www.ann-sophielehmann.nl


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Complexity is a perceived quality that comes from the difficulty inunderstanding or describing many layers of inter-related parts(Benjamin Jotham Fry, 2000 )

By Tomasz Malisiewicz


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AbstractIn this paper I will write a practical approach about themaking and use of data visualizations. How should weuse or address data visualizations? And what shouldtherefore be key factors in the process of making datavisualizations? To do this, I critically look into the processof making and understanding data visualizations. Usingcases and scientific analyses I will pinpoint importantaspects of data visualizations that should be taken intoaccount when making data visualizations.

Keywords: visualization, data, design, dynamic, making

Introduction

Over the last ten years we have seen an increasingly amount of data visualizations: from infographics explainingnews topics in the daily newspaper to Facebook applications visualizing your social network. But the increasingamount of data visualizations is not the greatest change, while looking at the development over the past tenyears. Data visualizations have transformed from plain statistic graphs and images to interactive mind blowinggraphical designs. Doing so, some data visualizations are opening up new aesthetics and are venturing into hybrid

appearances between data visualizations and art. Besides their appearance, they are also changing in complexity.High-tech computers and algorithms are visualizing huge datasets that are consisting over more than millions ofentries. The increase of data in the digital 21st century comes with new technology and techniques. Yet this is nota mere increase of data but also a change in the sort of data. The emergence of social media in the middle of2000s created opportunities to study social and cultural processes and dynamics in new ways. (Lev Manovich,2011. pp.2). The fact that we now use these new social and cultural data also change the way we make new datavisualizations. With the change in aesthetics, technology and content scientists find themselves questioning andhoping to understand these new and sometimes hybrid data visualizations.

Not only science should start to reanalyze and critically look at todays data visualizations. While analyzing datavisualization one cannot forget the involvement and choices of the maker. Makers of data visualizations are

designers, programmers, scientists and perhaps even artists, and most of them do not have a scientific approachto data visualizations. I myself -being a junior graphic designer and programmer- can see that in this area ofexpertise there is much room for improvement. Makers of data visualizations do not address or make datavisualization from a scientific perspective; they just design or program it. In this paper I will confront these twoperceptions of data visualizations and come up with important aspects that should be taken into account whenmaking todays data visualizations. I believe that in the future this will become more and more important, lookingat the rise of data and the use of data visualizations. Next to that, there is an increasing amount of open softwareand tools like Many Eyes, Tableau, Gephi and Processing, making data visualizations available for a much widerpublic. Educating them to better address and make better data visualizations will perhaps prove to be a challengein the future.


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What are data visualizations?

Recently I have given a presentation about data visualization. The event was hosted in a movie theatre with themain question being: are data visualizations just proposing new graphical styles/aesthetics without the graphicaldesigns being functional for providing us information/data. Infographics designer Chad Hagen beautifully shows

this in his work1

. By creating beautiful images that look like data visualization, but in fact do not represent anydata. In my research for this presentation I began to wonder about this question. What was the purpose of theartists who were making data visualizations as art? For them there was no purpose of finding new insights in thedata analyses or showing new data and information to their viewers. For them data visualizations are just anartistic expression. In my opinion these are no real data visualizations anymore. I needed to state a clear definitionof the term data visualizations.

Lev Manovich writes in his paper What is Visualization? the following definition of information visualization: Letsdefine information visualization as a mapping between discrete data and a visual representation (Lev Manovich,2010. pp.2). But as for instance DNA11 2 shows that something can represent discrete data (in the form of humanDNA) but in fact can be nothing more than art. So I needed to come up with another definition on how data

visualizations can be defined, a definition that would exclude the artistic expressions that just wanted to make artinstead of visualizing information or data.

I was inspired how to better formulate my definition by a presentation of Bernhard Reider (Reider, 2011). In hispresentation he talked about how researchers can and are using data visualizations, and showed examples howhe is currently using data visualizations in his own research. During the presentation he stated that datavisualizations used in scientific research cannot always be seen as standalone images. Data visualizations inscientific research are used as part of the research method and do not have the purpose of presenting data andinformation to a layman. Hearing this notion of purpose, I came up with the idea to include the purpose of themakers of the data visualizations into the definition, thereby showing why a data visualization was made.Introducing purpose into my definition leads me to use the following definition in this paper: a data visualizationcan be defined as a visual representation of data with the purpose of presenting that data. With this more narrowdefinition of data visualization we can exclude the artists that make data visualizations as art, because in mostcases art does not have the purpose to present discrete data. Although I will use this definition in my paper, I willmostly use data visualizations that have been programmed. Programmed data visualizations allow for largerdatasets and therefore are more complex in the process of making and understanding. This does not mean that Iwont use static data visualizations like infographics in this paper, but I will use them less because they could lackthe involvement with digital technology or programming.

We feel fine

Throughout this paper I will use one main case that I will reflect upon from different angles. This case is the datavisualization We Feel Fine 3 , launched in 2006 by Jonathan Harris and Sepandar D. Kamvar. We Feel Fine is a datavisualization that aims to collect the worlds emotions to help people better understand themselves and others(Harris & Kamvar, 2011. pp.1). The dynamic data visualization scrapes sentences from LiveJournal,MSNSpaces, MySpace, Blogger, Flickr,Technorati, Feedster, Ice Rocket, and Google with the occurrences of thephrases I feel and I am feeling every 5 minutes. The result is a database of several million feelings, increasingby 10,000 -15,000 new feelings per day.(Harris & Kamvar, 2011. pp.1). The data visualization is a real looker and

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www.chadhagen.com2 www.dna11.com3 www.wefeelfine.org
http://www.livejournal.com/http://spaces.msn.com/http://spaces.msn.com/http://www.myspace.com/http://www.blogger.com/http://www.flickr.com/http://www.technorati.com/http://www.feedster.com/http://www.icerocket.com/http://blogsearch.google.com/http://www.chadhagen.com/#56490/Nonsensical-Infographicshttp://www.chadhagen.com/#56490/Nonsensical-Infographicshttp://www.dna11.com/http://www.dna11.com/http://www.chadhagen.com/#56490/Nonsensical-Infographicshttp://blogsearch.google.com/http://www.icerocket.com/http://www.feedster.com/http://www.technorati.com/http://www.flickr.com/http://www.blogger.com/http://www.myspace.com/http://spaces.msn.com/http://spaces.msn.com/http://www.livejournal.com/


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became a hit. Using no static but mostly dynamic data visualization techniques, We Feel Fine has been used as aninteractive installation and has been exhibited in museums all over the world. We Feel Fine is also partiallyavailable online 3. Over the course from 2006 until 2011 We Feel Tine has been used by 8,5 million visitors (Harris &Kamvar, 2011. pp.1). For the purpose to better understand the main case that I am using in this paper, I will adviseyou to try out the We Feel Fine data visualization on www.wefeelfine.org.

I will repeatedly come back to We Feel Fine in this paper, but for now lets situate it a bit more. Looking at mydefinition stated earlier we can say We Feel Fine is a data visualization. It has a clear purpose to show the datathat has been gathered and uses visuals for presenting the gathered data. But on the other hand, we can say thatWe Feel Fine has been used by museums as an artistic expressions or art, and therefore has for instance beenexhibited in the National Museum of Contemporary Art in Athens (Harris & Kamvar, 2011. pp.1). We could say thatWe Feel Fine is right on the edge between functional data visualizations and art. The data used in We Feel Finebelongs to the relatively new data, coming from digital social and cultural processes available on the internet.Because We Feel Fine uses new social data and is leaning towards art we can situate it at the front of todaysnew and interactive data visualizations. This is also the reason why I chose this data visualization to be the maincase in this paper.

The process of makingThe process of making data visualizations is a complex process. Unique algorithms work to gather and representdata. The programmed algorithms works hand in hand with complex graphical designs and together createunpredictable outcomes. But the process of making starts not with programming or designing the datavisualization. To get a clear understanding of the process of making data visualizations, I have divided the processof making into three different stages. All of these stages are important to understand and give new insight in theway data visualizations are constituted and given meaning to.

Gathering of dataThe makers of data visualizations always choose their dataset and therefore confine their data visualization withinthat chosen frame of data. So a dataset can always be seen as the initial framework where data visualizationsrest upon. The dataset determines the outcome of data visualizations as much as the choices in the design of datavisualizations. Even in static data visualizations this is the case because the maker will still use no other data thanthe one within the used dataset. This means that choosing your dataset is the first important step when makingdata visualizations and can have huge impact on the outcome of the data visualization.

Christine Paul writes about the use of databases and storage of data in her paper The Database as System and Cultural Form . She writes that even the way we store data and design data models already imply some kind ofnarrative, patterns or structures within the data. This would mean that the choices we make in structuring andstoring data in databases already in some way form the outcome of the data visualization. The understanding ofa database as the underlying principle and structure of any new media object delineates a broad field thatincludes anything from a network such as the Internet (as one gigantic database) to a particular data set (Paul,2007. pp.5). You might say that basis for structure and pattern analysis in data visualizations are already made bydesigning databases. Another example is shown by Duncan Watts et al. in the paper Identity and Search in Social Networks . They discuss the underlying data structures of social media. With these analyses they reveal that thereare already patterns within social media that afford us to view and gather data is some way. Looking at thesepatterns they address the relationship with for instance non digital social structures and find interesting ways tomake algorithms to correctly gather data from social media.

Manovich writes about a change in the current use of datasets, and addresses this in his article Trending: The promises and the challenges of Big Social Data . He states that for the first time we can use huge quantities ofdata. While before we could only use data from a few (deep data) to explain the overall state, we can now use


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large quantities of data (surface data). While some ethnographers might say that large quantities are very shallowand cant explain more about cultural or social studies than we could with deep data or perhaps even less,Manovich disagrees. Manovich writes that he sees a scenario in which both sorts of data are used differently. Ibelieve that in our hypothetical scenario, ethnographers and computer scientists have access to different kinds ofdata. (Manovich, 2011. pp. 7) He sees a new form, a way of analyzing great quantities but still dig deep enough

into different samples to explain the overall state, effectively using both methods of deep and surface data atonce.

You can easily see from Manovich perspective that the way we gather data is extremely important for theoutcome of our research or data visualization. Considering the right amount of data and also the right way toaddress different samples of data, you can determine the quality and impact of your data visualization. Thisbecomes even more important when not gathering the data yourself but writing algorithms to gather data foryou. Here the margins for errors or misinterpretation are even greater and can ruin or make your entire researchor visualization. In the case of We Feel Fine we can see that they chose to very strictly gather data. They usedalgorithms to gather sentences form specific sites with the occurrence of I feel or I am feeling. But this wasnot the only thing; We Feel Fine also tries to collect some personal information about the writer of the sentence:

age, location, gender and time. What does this imply for the quality and outcome of the data visualization? Howare these different data used and collected? This will be discussed in the chapter on Science versus practice.

Programming/designing of data visualizationsThe second stage of the process of making and giving meaning to data visualizations is the actual programming ordesigning of data visualizations. This stage is also a vital part of the making process of data visualization, in theway that this stage will determine how the data visualization will be presented to the viewer/user. Here we seethat common issues of style, form, color and other basis design principles are important for the understanding andclarity of data visualizations.

Monovich highlights two district aspects of this process in his paper What is Visualization? . He sees reduction and

spatiality as two of the main factors in data visualizations. Reduction is the process of using samples in reducedforms in order to clarify of represent complex structures. Infovis uses graphical primitives such as points, straitlines, curves, and simple geometric shapes to stand in for objects and relations between them - regardless ofwhether these are people, their social relations, stock prices, income of nations, unemployment statistics, oranything else (Manovich, 2010. pp. 5). For Monovich reduction is an element of design that is very important forour understanding and perception of data visualizations. And I believe he is right, by choosing different forms orshapes we are designing and situating taxonomies into our data visualization or information design. Therefore wemake choices in the use of reduction, what is important and what is less important for instance. By addingreduction to data we thus control the interpretation of our viewer/user. For spatiality this is roughly the same:They all use spatial variables (position, size, shape, and more recently curvature of lines and movement) torepresent key differences in the data and reveal most important patterns and relations (Manovich, 2010. pp. 7).

While spatiality also implies some forms of taxonomies, spatiality is more often used for the visualization of therelationship between different data. A bunch of tight circles might mean a close relation of reductive elementswhile a very scattered number of circles might be more perceived as an open or distanced relationship.

Reduction and spatiality is not always used to analyze data. Jeremy Douglass is a researcher working withManovich who gatherers and analyzes gameplay data. Here he uses techniques like motion tracking and RGBlevel analysis and newly developed technique called eigenmodes to visually analyze the gameplay data. The fullrange of uses for simple computational approaches to gameplay recording has barely been considered, but thepotential for new kinds of artistic representations and analytic insights about games is huge.(Douglass,2009.pp.3)

This stage of making data visualizations has changed a lot with the introduction of interactive and digitaltechnology. The introduction of interactive and even dynamic ways to design and create data visualizations opens


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up new ways of designing and programming data visualizations. These new/extra attributes of data visualizationsought to be addressed in the same careful way reduction and spatiality should be used. An insight into the use ofinteraction and dynamic data visualizations is given in the chapter on Interactive/dynamic data visualizations .

Interpretation of the viewer/user

The last stage of the process of making and giving meaning to data visualizations is the interpretation of theviewer/user. It is always true, just like any other medium, that you can never fully predict the interpretations ofusers. While this is hard to see as a stand along stage of the process of making, I believe otherwise. This is mainlywhy I not merely use the word viewer, but include the word user when talking about the receiver of datavisualizations. Todays data visualizations just like We Feel Fine, facilitates the user with the means to dynamicallychange and alter the data used in data visualizations. This was not possible with the traditional static datavisualizations. This transformation causes for a huge change in the way the viewer can interpret the data or thevisualization. Interpretation has become an interactive process that has become more dependent on the choicesof interaction by the makers and users of data visualizations. Making good interaction designs for datavisualization is therefore becoming more and more important because they shape the way we interpret and givemeaning to data visualizations. Jasper Schelling who wrote his thesis on data visualization writes: Since the

interpretation of the visualized data is key, user (like in interaction design and usability) tend to be involved fromthe beginning, to insure that a visualization has its intended result. (Schelling, 2007. pp. 28). So he already talksabout the way interaction design is being used with the practice of making data visualization, and in fact isalready imbedded within the process of making.

To go a step further we can take Manovich concept of direct visualization . What Manovich described is aninteraction design that skips the second stage of the process of making data visualizations. By interacting with thedata visualization it is possible to directly show the source of the (gathered) data, and therefore eliminates part ofthe second stage in the process of making. Interaction design also makes it possible to address only a portion orsample of the complete dataset. These processes help to understand and discover patterns in data visualizations.Displaying the actual visual media as opposed to representing it by graphical primitives helps the researcher to

understand meaning and/or cause behind the pattern she may observe, as well as discover additional patterns(Manovich, 2010. pp. 23).

The process of understanding

Besides the process of making there is also the process of analyzing how we interpret or understand datavisualizations. In the definition of data visualization I talk about the purpose of the data visualization. To be able tomake good data visualizations you need to be aware how data visualizations fulfill their purpose. What are thekey factors in which we receive data or information from data visualizations. Why do we feel the need tovisualize data?

Benjamin Jotham Fry writes about this, and looks how humans interpret data visualizations: Because of theaccuracy and speed with which the human visual system works, graphic representations make it possible for largeamounts of information to be displayed in a small space (Fry, 2000. pp. 14). It seems that we are very fast atseeing complex graphics. Older data was represented by long lists of data or endless arrays of numbers. With amore textual/non-visual representation of data we cant make out differences or relations, we need visual toolsand systems to analyze and reproduce data in a visual way to better analyze them.

Manovich talks about seeing and discovering patterns and relations in data visualizations. In our experience,

practically every time we analyze and then visualize a new image video collection, or even a single time-basedmedia artifact (a music video, a feature film, a video recording of a game play), we find some surprising new


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patterns. (Manovich, 2011. pp. 8). So these relations and patterns can be seen as new insights, new informationgathered by the clever use of visualizing data.

During my research and work with data visualizations I always use the sentence: Making the invisible visible. Forme a good data visualization is one that has the potential to reveal coherence, relations, patterns and comparison

between different samples of data, thereby revealing/allowing new insight to be formed or analyzed. In that wayyou can even say that old bar charts for instance are bad data visualizations, because they hardly reveal or allowany other analysis than the data that is behind the bar chart. For me the user is an important aspect here, andshould be allowed to actively interpret/analyze the data. Japser Schelling seems to agree with me and writesabout data visualizations: They derive their strength from the fact that they let people use their eyes and mindsto draw their own conclusions rather than explicitly state a fact. (Schelling, 2007 pp 27)

Looking at We Feel Fine we can see that they tried to attract and afford active user participation: making differentways to visually represent, analyze and sample data. This lets to users create a part of their own analysis andthereby stimulate the process of analyzing data for themselves. data visualization has been defined as a tool toamplify cognition (Harris & Kamvar, 2011. pp.8)

Interactive/dynamic visualization

I already addressed interaction and dynamic data visualizations a couple of times in this paper. I think this is oneof the most interesting new aspects of data visualizations and needs some further critical analysis. We have seenthat interaction design is integrated is as well the making as the understanding of data visualizations. Next to thefact that interaction is important to the makers and users of data visualization, interaction also is important for themedium/technology on which the data visualization is displayed. Interaction is becoming a very important aspectof data visualizations and is entwined with every process surrounding data visualizations.

Benjamin Jotham Fry, but also Jasper Schelling, write about data visualizations that with the use of motion andinteractive techniques comes a whole new array of possibilities to represent, analyze and design data. In thethesis of Fry, Organic Information Design , he tries to answer questions like: How can a continually changingstructure be represented? (Fry, 2000 pp. 13). In his thesis Fry constructs his concept of organic informationvisualization: An Organic Information Visualization provides a means for viewers to engage in an activedeconstruction of a data set (Fry, 2000 pp. 16). Fry searches for ways in which complex and interactive data canbe best represented. Looking at behaviors of organic systems/organisms like structure, appearance, metabolism,growth, homeostasis, responsiveness, adaptation, movement and reproduction Fry hopes to find new andinnovation ways to represent complex data. These behavioral rules map meanings determined by the designer,such as importance, to a property like appearance (Fry, 2000 pp. 43). You can note that with the rise of

interactive media these new (organic) representations have become available, creating new data visualizations.Again interactivity is a changing factor in all three stages of making and also changes the way we are able tounderstand data visualizations.

Although interaction is a big changing factor, modern technology and database structures let us change/sampledatasets and perhaps even work with instant data gathering. Dynamic aspects of data visualizations are able totransform and adjust appearances and data samples (data-framework) in data visualizations. Fry shortlyaddresses this as emergent characteristics; the way in with visualizations can be transformed to form a newappearance. The aspect of dynamic emergent systems is one of the main topics of the thesis of Schelling. Herewe talk about the many possibilities that rise form a dynamic emergent system. Schelling cites the work of ChrisCrawford explaining that with the possibility of many outcomes, we introduce another state of mind or the

behavior: the behavior of exploring and actively thinking and interacting. This state of mind would be one aboutthe meaning and interpretation of the data used together with other possibilities.


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In game studies we see that the same concept is applied to games. Katie Salen and Eric Zimmerman write in Rulesof Play about games as emergent systems: creates unexpected patterns of events out of very simple set ofrules (Salen&Zimmerman, 2004. pp.538). By designing systems that allow for large quantities of outcome youwould create unexpected complex outcomes that are able to transform and show new insight and meaning. Using

these techniques in data visualization will perhaps give way to new and undiscovered patterns and relations.Salen and Zimmerman go a step further and address culturally emergent systems, allowing us to look at thecultural aspects of these complex systems. Cultural emergent systems allow for the exchange of game content(data) and therefore change the outcome of the game and its meaning according to Salen en Zimmerman(Salen&Zimmerman, 2004. pp.538). This is already the case in some data visualizations allowing us to import ourown social media data or import our own text or web-addresses to form the input for dynamic data visualizations.Using emergent systems we again see a huge impact on the all three stages of the making of data visualizations.

Looking at We Feel Fine we indeed see that with the use of algorithms to gather data we see a dynamic datavisualization that changes over time. If we can speak of a true emergent system is perhaps a bit too far becausewith these dynamic data in We Feel Fine we dont get new insights and therefore will not find new patterns or

relations. Culturally looking we could only indirectly change the content, what would mean going to a lot of blogsand sites to post weird sentences about how we feel. Although We Feel Fine might not be the best case to writeabout innovating dynamics it gives us a clear new perspective to approach and think about data visualizations.This could lead to a better understanding and making of data visualizations in the future.

Looking at visualization tools and software as cultural emergent systems might be an interesting topic for furtherresearch but will not be addressed in this paper.

Science versus practiceDuring the research for this paper I somehow felt cheated or twisted about the We Feel Fine. Looking at it from apractical perspective being a maker of data visualizations myself, you can say that it is a great success, both forthe makers and for users. If I would have made a similar data visualization I would be proud and perhaps alsopublish a book about the findings and reactions of the data visualization, just like Harris and Kamvar did 4. In othercases from Harris and Kamvar we see the same amount of excitement and curiosity. Take for instance I Want You To Want Me, another data visualizations, here they scrape data form dating sites to reveal longings en feelings ofonline people (Harris & Kamvar, 2008).

On the other hand I am a student in media studies. Because of that background I am quickly reminded of the

goals, findings and conclusions from Kamvar and Harris. We Feel Fine gives us the ability to better understandemotions themselves (Harris & Kamvar, 2011. pp.1). The underlying questions We Feel Fine raises arescientifically impossible to prove. You would not get answers about our understanding of emotions just byscraping some sentences form some random chosen forums and blog posts. You would need a betterunderstanding of how, why and where all these sentences are written.

This is not just the problem with We Feel Fine, there are bigger problems here. Mostly the new social and culturaldata that has become available prove to be difficult to use in science. Bernhard Reider who I mentioned earliershowed during the presentation some of his work. He also coped with the same sort of problem using twitterposts for his current research and data visualizations. Reider questioned the means in which these data canscientifically be seen as a source of information. He also does this on his own blog page were he makes more

4 www.wefeelfine.org/book


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data visualizations from for instance Wikipedia. The problem is that there is no context other than twitter itself,which basically can be mutated and altered or even lacking. Most of the data is also not seen by the researchersbut was gathered using algorithms. How can we validate these findings? Reider even went as far as to ask thequestion if all the thousands of twitter users he used could become co-authors of his paper in order toscientifically validate his findings (Reider, 2011).

Looking at these social and cultural data online we quickly venture into the study of our online identity versus ournormal identity. Danah Boyd writes about this issue in the form of how friendships are represented and made inonline environments. In her research she shows that real life friendship has little to do with online friendship. Theonline environment forces us to change our way of thinking and expression. While networked context shifts thefocus away from interests onto people, it is also vulnerable to the architectural aspects of mediatedenvironments (Boyd, 2006. pp.12). This means that online structures change the way we think and act in onlineenvironments. While this already implies that we cant make worldly statements out of one-sided online dataalone, we now do begin to grasp the idea that it is really difficult to use these data. On the other hand we canalso look at how we behave online, our online identity. Valerie Frissen and Jos de Mul write about the constitutionof our online identity. They see a movement in online identity towards a more culturally constructed notion of

identity. An identity that is more aware of the cultural contexts, norms and personal narratives. Therefore anonline identity changes from our own physical identity, again showing us that mere twitter tweets and othersentences online cannot be directly compared with off-line identities.

These notions make it difficult to actually use these data for the use of scientific research. Reider concludedtherefore at the end of the presentation that in science we should use data visualizations as a part of our researchmethod, not as end result or final image. This eliminates some of our problems using social and cultural data.

In the text of Harris and Kamvar we do find some small solution to the whole problem. Harris and Kamvar start toanalyze their own findings of We Feel Fine through the use of comments and filmed usability testing. Here yousee that they use test subjects to form opinions and ideas about these difficult social data to reveal real usableopinions of people. With these they make interesting discoveries how emotions might actually affect us. Whileusers of We Feel Fine are generally not trained data analysts and the time spent in exploration is often short, theinsights from the community have been both real-time and sophisticated. (Harris & Kamvar, 2011. pp.9). Itmeans that there is hope in changing the method of how we use data visualizations in science. By opening up thevisualizations to a target-group we might be able to use large amounts for digitally gathered data in our scientificresearch. This would introduce an fourth stage in the process of making and giving meaning to data visualizations,a stage were researchers use findings from data visualizations as basis for the analysis of the data.

Conclusions

In this paper we critically looked at the making and key features of todays data visualizations. We have seenthat data visualizations are quickly introducing state of the art technology and algorithms. With the use of newgraphical styles, technology and interaction we see that data visualizations are changing and are even venturinginto hybrid form between data/information design and art. These new data visualizations give us new methodsand ways to analyze and research data visualizations. With the knowledge how data visualizations are interacting,designed, programmed and comprehend, we can now use this knowledge to make better and more appealingdata visualizations in the future.

In the matter of the use of data visualizations in science: we have seen that there al already new methods toinclude social and cultural data visualizations in scientific research. Hereby we find ourselves exploring new ways

of retrieving information from data visualizations, and adding new stages to the process of making and givingmeaning to data visualizations.


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Bibliography

Boyd, Danah. Friends, friendsters and top 8. First Monday 11(12), December 2006.http://www.firstmonday.org/issues/issue11_12/boyd/index.html

Christiane Paul, The Database as System and Cultural Form: Anatomies of Cultural Narratives, in: Victoria Vesna (ed.) DatabaseAesthetics: Art in the Age of Information Overflow. Minneapolis, MN: University of Minnesota Press, 2007,http://victoriavesna.com/dataesthetics/readings.php

Frissen, Valerie en Jos de Mul. Under Construction. Persoonlijke en culturele identiteit in het multimediatijdperk. Infodrome,2000.

Fry, Ben. Organic Information Design. M.S. Thesis. Massachusetts Institute of Technology, Program in Media Arts & Sciences,2000.

Harris, Jonathan & Kamvar, Sepandar. We Feel Fine and Searching the Emotional Web. www.feelfeelfine.org, 2011.

Jeremy Douglass. "Computer Visions of Computer Games: analysis and visualization of play recordings.". Workshop on MediaArts, Science, and Technology (MAST) 2009: The Future of Interactive Media. UC Santa Barbara, January 2009.

Lev Manovich, "What is Visualization?" Manovich.net, 2010.http://manovich.net/2010/10/25/new articlewhat isvisualization/

Lev Manovich. "Trending: The Promises and the Challenges of Big Social Data."Debates in the Digital Humanities, edited by Matthew K. Gold. The University of Minnesota Press, forthcoming 2012.PDF:http://lab.softwarestudies.com/2011/04/new-article-by-lev-manovich-trending.html.

Schelling A., Jasper, Social Network Visualization. Hogeschool Rotterdam, 2007.http://thesis.jasperschelling.com/thesis_jasperschelling_socialnetworkvisualization.pdf

Watts, Duncan. Sheridan Dodds, Peter. Newman, M.E.J. Identity and search in social networks. Colombia University, New Tork.February 1, 2008.

Zimmerman, Eric & Salen, Katie.Rules of Play. Cambridge: MIT Press. 2004

Cases And Presentations

Kamvar, Sepandar and Harris, Jonathan. We Feel Fine and Searching the Emotianal Web. 2010. http://www.wefeelfine.org/

Kamvar, Sepandar and Harris, Jonathan. I want you to want me. 2008. http://iwantyoutowantme.org/

Rieder, Bernhard. Workshop: Computer Simulation & Data Visualisation in the Humanities. Host: Mirko Tobias Schfer,Universiteit Utrecht, 18-10-2011.

Rieder, Bernhard. The Politics of Systems (Blog). http://thepoliticsofsystems.net/about/
http://www.firstmonday.org/issues/issue11_12/boyd/index.htmlhttp://victoriavesna.com/dataesthetics/readings.phphttp://mast.mat.ucsb.edu/docs/submission_53.pdfhttp://lab.softwarestudies.com/2011/04/new-article-by-lev-manovich-trending.htmlhttp://thesis.jasperschelling.com/thesis_jasperschelling_socialnetworkvisualization.pdfhttp://www.wefeelfine.org/http://iwantyoutowantme.org/http://thepoliticsofsystems.net/about/http://thepoliticsofsystems.net/about/http://iwantyoutowantme.org/http://www.wefeelfine.org/http://thesis.jasperschelling.com/thesis_jasperschelling_socialnetworkvisualization.pdfhttp://lab.softwarestudies.com/2011/04/new-article-by-lev-manovich-trending.htmlhttp://mast.mat.ucsb.edu/docs/submission_53.pdfhttp://victoriavesna.com/dataesthetics/readings.phphttp://www.firstmonday.org/issues/issue11_12/boyd/index.html

visualization design: how to design and address data visualizations

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