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  • Constructive VisualizationSamuel Huron1,2, Sheelagh Carpendale2, Alice Thudt2, Anthony Tang2, and Michael Mauerer2

    1IRI, 4 rue aubry le boucher, Paris, France2University of Calgary, Department of Computer Science, Calgary, Canada

    samuel.huron@cybunk.com, {sheelagh, tonyt}@ucalgary.ca, {alice.thudt, damischael}@googlemail.com

    ABSTRACTIf visualization is to be democratized, we need to providemeans for non-experts to create visualizations that allow themto engage directly with datasets. We present constructive vi-sualization a new paradigm for the simple creation of flex-ible, dynamic visualizations. Constructive visualization issimplein that the skills required to build and manipulate thevisualizations are akin to kindergarten play; it is expressivein that one can build within the constraints of the chosen envi-ronment, and it also supports dynamics in that these con-structed visualizations can be rebuilt and adjusted. We de-scribe the conceptual components and processes underlyingconstructive visualization, and present real-world examplesto illustrate the utility of this approach. The constructive visu-alization approach builds on our inherent understanding andexperience with physical building blocks, offering a modelthat enables non-experts to create entirely novel visualiza-tions, and to engage with datasets in a manner that would nothave otherwise been possible.

    Author KeywordsDesign; Visualization; Construction; Assembling;Constructivism; Constructionism; Education; Visual literacy.

    ACM Classification KeywordsH.5.m. Information Interfaces and Presentation (e.g. HCI)

    INTRODUCTIONIn this paper, we focus on supporting the democratization ofvisualization [72], which is in contrast to conventional visu-alization work where the focus has been to support data in-tensive science, industry and government. We are now seeingvisualizations in such places as: personal blogs [36], as partof art works [42, 56, 73], in the news media [35, 66, 77],and as a growing part of the quantified self movement [4].These visualizations range from the intensely personal suchas the visualization of the contents of ones freezer [71], tocommunity-based visualizations such as crime in ones neigh-borhood [71]. This movement opens several research ques-tions. If democratization means that people (not just experts)will design and construct their own visualizations, what kindsof tools will these people need? What questions are they ask-

    Permission to make digital or hard copies of all or part of this work for per-sonal or classroom use is granted without fee provided that copies are notmade or distributed for profit or commercial advantage and that copies bearthis notice and the full citation on the first page. To copy otherwise, to re-publish, to post on servers, or to redistribute to lists, contact the Owner/Au-thor. c Samuel Huron, Sheelagh Carpendale, Alice Thudt, Anthony Tang,Michael Mauerer ACM 2014. This is the authors version of the work. Itis posted here for your personal use. The definitive Version of Record waspublished in DIS14, June 21 - 25 2014, Vancouver, BC, Canada. ACM978-1-4503-2902-6/14/06.http://dx.doi.org/10.1145/2598510.2598566

    ing, and how will they explore the data that interests them?We investigate these questions by exploring situations wheresimple tools are being used to creatively and effectively con-struct complicated structures, in particular examining howthese ideas arose in kindergarten education. Our goal is toexplore educational theory to learn how to design tools thatsupport non-professionals in the creation of visualizations.The democratization of visualization is readily motivated byour current data deluge. Similarly to the era following theinvention of the printing press, where information becameavailable in unprecedented amounts, response is both posi-tive and negative. While in the media the phrase big datais discussed as an unparalleled opportunity, people individu-ally often think of this as overwhelming. Our focus here is tolook at how to make this data deluge tractable to us as indi-viduals. For example, Grammel et al. [29] show us that non-computer scientists have considerable trouble when design-ing visualizations particularly when selecting good data at-tributes, formulating visual mappings, and interpreting the vi-sualizations produced. We examine how to address this prob-lem by changing the design paradigm through which peoplemanipulate the data in conjunction with visual variables toconstruct a visual mapping. To this end, we take a freshlook at theories that speak to how people understand conceptsthat are new to them. The current way to support people inthe creation of visual representations is to develop code li-braries, toolkits, or create visualization templates and providean infrastructure with which the created visualizations can beshared. In this paper we discuss an alternate approach: weconsider how people can craft visualizations by using famil-iar elements. We build our perspective on the observationof practices in non-academic situations where people are ac-tively engaging in the construction of their own visualizationsin spite of the fact that there is a lack of tools supporting thesekinds of activities. Our contribution is to propose a new vi-sual mapping paradigm, to relate it to existing theories, and todiscuss how it opens new directions for visualization design.In this paper we start by exploring the idea of democratiza-tion of visualization design: we identify and compare threeexisting paradigms in which one can create a visual mapping.Then we explain how educational approaches from Froebel,Piaget, and Papert point the way to a new design paradigm,which we term constructive visualization. Then we de-fine the components, processes and benefits of constructivevisualization. Finally we illustrate it through real world non-academic examples and discuss the implications for develop-ing new designs and research.

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    Author manuscript, published in "ACM conference on Designing Interactive Systems in 2014 (2014)"

    http://dx.doi.org/10.1145/2598510.2598566 http://hal.inria.fr/hal-00978437http://hal.archives-ouvertes.fr

  • TOWARDS DEMOCRATIZATION OF VISUALIZATIONThe InfoVis research community has focused on making vi-sual representations of data and on supporting data tasks viainteraction. A considerable emphasis has been placed onleveraging perceptual skills to improve readability of visualrepresentations [76], studying the effectiveness of visualiza-tion in regards to the intended task [9], and making progresstowards assessing insight triggered through use of visualiza-tions [63]. A major underlying goal of much of this work hasbeen to empower information workers and data experts.Viegas et al.s discussion about the democratization of visu-alization [72] provides a fresh perspective on how InfoViscould impact society more broadly if the tools to build anduse visualizations are accessible to those other than informa-tion workers. To this end, they identify three major problemsthat should be addressed in order to make InfoVis accessible,understandable and beneficial for the general population: The ability to create ones own visualizations. The ability to publish or make these visualizations gener-

    ally available. The possibility for discussion of these visualizations.The ManyEyes [72] project was an initial exploration in thisdirection. It provides facilities to upload ones data, choosefrom a variety of InfoVis templates to create a visualizationof ones own data through a common website. The created vi-sualization is then automatically published online, and an as-sociated discussion forum is automatically generated for eachof the visualizations on the site. This site has been successfuland well used, but it limits possible visualization variations tothe given set of templates.Victors [70] discussion on the creation of visualizationssheds new light on challenges to be overcome in the pursuitof the democratization of visualization, focusing on the issueof creation. He identifies three paradigms that people use tocreate visualizations: Using a pre-coded visualization (as a template), Drawing a visualization freehand, and Coding a visualization through computer programming.He argues that each of these approaches has pitfalls. Thefirst one, using a pre-coded visualization, describes theManyEyes [72] solution where one can choose from ex-isting InfoVis templates (e.g. bar chart, scatter plot, etc.).The advantages here are that these templates can be wellknown, well understood and even well researched in termsof readability, etc. The disadvantages are that data is usu-ally unique and often has distinct needs to achieve best re-sults in terms of possible understanding and insight. Alsousing established templates can limit natural human creativ-ity. This type of approach is exemplified by software suchas Excel, Tableau [6], Spotfire [5], and also web tools likeManyEyes or Chart Editor in Google Docs [2]. In these casesvariations in results are limited by the set of predefined map-ping functions and the possible options to tune their param-eters. These are great in that they are easy to use but theycan limit the power of expression. The second approach isthe use of free hand drawing. Victor includes within drawingthat done by hand with a pen and a paper, on a whiteboard,as well as that done through using drawing software such as

    Photoshop or Illustrator. It is well accepted and studied thatpeople often use freehand drawing on napkins [19], sketch-books [30], and whiteboards [75, 74] to help them think visu-ally [45, 11] about ideas and to create ad hoc data represen-tations for thought and discussion purposes. With freehanddrawing people have great creative and expressive freedom,which is somewhat limited by

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