39 research outputs found

    Data Materialization: A Hybrid Process for Crafting a Teapot

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    Data materialization is a workflow developed to create 3D objects from data-informed designs. Building upon traditional metalwork and craft, and new technology's data visualization with generative art, this workflow expresses conceptually relevant data through 3D forms which are fabricated in traditional media. The process allows for the subtle application of data in visual art, allowing the aesthetic allure of the art object or installation to inspire intellectual intrigue. This paper describes the technical and creative process of Modern Dowry, a silver-plated 3D-print teapot on view at the Museum of the City of New York, June 2017--June 2018.Museum of the City of New York; Jeannine Falino; the National Science Foundation for supporting the Computing in the Arts workshops (DUE 1323610, DUE 1323605, DUE 1323593); Reiserโ€™s fellow principle investigators (Bill Manaris, Renee McCauley, Jennifer Burg and Rebecca Bruce); Seton Hall Universityโ€™s Digital Humanities Fellowship Program for funding and support; and Vassar Collegeโ€™s Creative Arts Across Disciplines Program for their summer residency and exceptional collegiality

    NONLINEAR APPROACH IN CLASSIFICATION VISUALIZATION AND EVALUATION

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    In this paper we have proposed the novel methodology to visualize classification scheme in informatics domain. We have mapped a documents collection of ACM (Association for Computing Machinery) Digital Library to a sphere surface. Two main stages of visualization processes complement one another: classification and clusterization. Primarily classified documents were visualized and their further clusterization by means of keywords was crucial in evaluation process. For clusters analysis of given visualization maps nonlinear digital filtering techniques were applied. The clusters of keywords were characterized by a local accuracy. Obtained semantic map was included to validation process

    Visualizing the scientific information nowadays: the problems and challenges

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    In recent years, a comparably fresh research field โ€” information visualization has become commonly available for the researchers of all specialties. Information or knowledge maps play a role of interface for the analysis and intensive study of scientific community and knowledge domains development. The popularity of visualization techniques and interdisciplinary framework has resulted in many problems that have not been solved since the field had emerged. The article introduces the instrumental problems and challenges in this field. Exposing the functions information visualization allows to understand the difficulties and barriers within the whole visualizing process. A particular example of insight into the Polish science map is considered in the context of a new knowledge

    ํŠธ๋ฆฌ ๊ตฌ์กฐ๋ฅผ ์ด์šฉํ•œ 3์ฐจ์› ๊ณต๊ฐ„ ๋‚ด ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™” ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋ฏธ์ˆ ๋Œ€ํ•™ ๋””์ž์ธํ•™๋ถ€ ๋””์ž์ธ์ „๊ณต, 2019. 2. ๊น€์ˆ˜์ •.Speculative visualization combines both data visualization methods and aesthetics to draw attention to specific social, political and environmental issues. The speculative data visualization project proposed in this work explores electronic waste trade and the environmental performance of various nations. Illegal trading of electronic waste without proper disposal and recycling measures has a severe impact on both human health and the environment. This trade can be represented as a network data structure. The overall environmental health and ecosystem vitality of those trading countries, represented by their Environmental Performance Index (EPI), can also give greater insight into this issue. This EPI data has a hierarchical structure. This work explores methods to visualize these two data sets simultaneously in a manner that allows for analytical exploration of the data while communicating its underlying meaning. This project-based design research specifically focuses on visualizing hierarchical datasets with a node-link type tree structure and suggests a novel data visualization method, called the data garden, to visualize these hierarchical datasets within a spatial network. This draws inspiration from networks found between trees in nature. This is applied to the illegal e-waste trade and environmental datasets to provoke discussion, provide a holistic understanding and improve the peoples awareness on these issues. This uses both analytical data visualization techniques, along with a more aesthetic approach. The data garden approach is used to create a 3D interactive data visualization that users can use to navigate and explore the data in a meaningful way while also providing an emotional connection to the subject. This is due to the ability of the data garden approach to accurately show the underlying data while also closely mimicking natural structures. The visualization project intends to encourage creative professionals to create both visually appealing and thought-provoking data visualizations on significant issues that can reach a mass audience and improve awareness of citizens. Additionally, this design research intends to cause further discussion on the role of aesthetics and creative practices in data visualizations.์‚ฌ๋ณ€์  ์‹œ๊ฐํ™”(speculative visualization)๋Š” ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™” ๋ฐฉ๋ฒ•๊ณผ ๋ฏธํ•™์„ ๊ฒฐํ•ฉํ•˜์—ฌ ํŠน์ •ํ•œ ์‚ฌํšŒ, ์ •์น˜ ๋ฐ ํ™˜๊ฒฝ ๋ฌธ์ œ์— ๊ด€์‹ฌ์„ ์œ ๋„ํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ œ์•ˆํ•œ ์‚ฌ๋ณ€์  ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™” ํ”„๋กœ์ ํŠธ๋ฅผ ํ†ตํ•ด ๋‹ค์–‘ํ•œ ๊ตญ๊ฐ€์˜ ์ „์ž ํ๊ธฐ๋ฌผ ๊ฑฐ๋ž˜์™€ ํ™˜๊ฒฝ ์„ฑ๊ณผ๋ฅผ ์‚ดํŽด๋ด…๋‹ˆ๋‹ค. ์ ์ ˆํ•œ ์ฒ˜๋ฆฌ์™€ ์žฌํ™œ์šฉ ์กฐ์น˜๊ฐ€ ์ด๋ค„์ง€์ง€ ์•Š์€ ์ „์žํ๊ธฐ๋ฌผ์˜ ๋ถˆ๋ฒ• ๊ฑฐ๋ž˜๋Š” ํ™˜๊ฒฝ๊ณผ ์ธ๊ฐ„์— ์‹ฌ๊ฐํ•œ ์˜ํ–ฅ์„ ๋ฏธ์นฉ๋‹ˆ๋‹ค. ์ด ๊ฑฐ๋ž˜๋Š” ๋„คํŠธ์›Œํฌ ๋ฐ์ดํ„ฐ ๊ตฌ์กฐ๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ํ™˜๊ฒฝ์„ฑ๊ณผ์ง€์ˆ˜(EPI)๋ฅผ ํ†ตํ•ด ์ด ๊ฑฐ๋ž˜์— ์ฐธ์—ฌํ•˜๋Š” ๊ตญ๊ฐ€๋“ค์˜ ์ „๋ฐ˜์ ์ธ ํ™˜๊ฒฝ ๋ณด๊ฑด๊ณผ ์ƒํƒœ๊ณ„ ํ™œ๋ ฅ์„ ์‚ดํŽด๋ณด๋Š” ๊ฒƒ์€ ์ด ๋ฌธ์ œ์— ๋” ๊นŠ์€ ํ†ต์ฐฐ๋ ฅ์„ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ํ™˜๊ฒฝ์„ฑ๊ณผ์ง€์ˆ˜๋Š” ๊ณ„์ธต ๊ตฌ์กฐ๋กœ ๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๋ฐ์ดํ„ฐ๋ฅผ ๋ถ„์„์ ์œผ๋กœ ํƒ๊ตฌํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๋‘ ๊ฐ€์ง€ ๋ฐ์ดํ„ฐ๋ฅผ ๋™์‹œ์— ์‹œ๊ฐํ™”ํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ํ‘œ๋ฉด์— ๋“œ๋Ÿฌ๋‚˜์ง€ ์•Š๋Š” ๋ฐ์ดํ„ฐ์˜ ์˜๋ฏธ๋ฅผ ์ „๋‹ฌํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ํƒ๊ตฌํ•ฉ๋‹ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํ”„๋กœ์ ํŠธ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ๋””์ž์ธ ์—ฐ๊ตฌ๋กœ, ๋…ธ๋“œ ๋งํฌ ์œ ํ˜• ํŠธ๋ฆฌ ๊ตฌ์กฐ๋ฅผ ํ†ตํ•ด ๊ณ„์ธต์  ๋ฐ์ดํ„ฐ๋ฅผ ์‹œ๊ฐํ™”ํ•˜๋Š” ๊ฒƒ์— ์ค‘์ ์„ ๋‘๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ž์—ฐ์—์„œ ๋ฐœ๊ฒฌํ•  ์ˆ˜ ์žˆ๋Š” ๋‚˜๋ฌด ๊ฐ„ ๋„คํŠธ์›Œํฌ์—์„œ ์˜๊ฐ์„ ์–ป์–ด ๊ณต๊ฐ„ ๋„คํŠธ์›Œํฌ์—์„œ ๊ณ„์ธต์  ๋ฐ์ดํ„ฐ ์„ธํŠธ๋ฅผ ์‹œ๊ฐํ™”ํ•ฉ๋‹ˆ๋‹ค. ๋ฐ์ดํ„ฐ ์ •์›์ด๋ผ๊ณ  ํ•˜๋Š” ์ด ์ƒˆ๋กœ์šด ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™” ๋ฐฉ๋ฒ•์„ ๋ถˆ๋ฒ• ์ „์ž ํ๊ธฐ๋ฌผ ๊ฑฐ๋ž˜์™€ ํ™˜๊ฒฝ ๋ฐ์ดํ„ฐ์— ์ ์šฉํ•˜์—ฌ ํ† ๋ก ์„ ์œ ๋ฐœํ•˜๊ณ  ์ „์ฒด์ ์ธ ์ดํ•ด๋ฅผ ์ œ๊ณตํ•˜๋ฉฐ ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์— ๋Œ€ํ•œ ์‚ฌ๋žŒ๋“ค์˜ ์ธ์‹์„ ๊ฐœ์„ ํ•˜๊ณ ์ž ํ•ฉ๋‹ˆ๋‹ค. ์ด๋Š” ๋ณด๋‹ค ๋ฏธ์ ์ธ ์ ‘๊ทผ๊ณผ ๋ถ„์„์  ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™” ๊ธฐ์ˆ ์„ ๋ชจ๋‘ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค. ๋ฐ์ดํ„ฐ ์ •์›์„ ํ†ตํ•œ ์ ‘๊ทผ์œผ๋กœ ์‚ผ์ฐจ์› ๋Œ€ํ™”ํ˜• ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”๋ฅผ ๋งŒ๋“ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ์‹œ๊ฐํ™”๋ฅผ ํ†ตํ•ด ์‚ฌ์šฉ์ž๋Š” ๋ฐ์ดํ„ฐ๋ฅผ ์˜๋ฏธ ์žˆ๋Š” ๋ฐฉ์‹์œผ๋กœ ์‚ดํŽด๋ณด๋Š” ๋™์‹œ์— ์ฃผ์ œ์™€ ๊ฐ์„ฑ์ ์ธ ์—ฐ๊ฒฐ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋Š” ๋ฐ์ดํ„ฐ ์ •์› ๋ฐฉ๋ฒ•์ด ๋ฐ์ดํ„ฐ๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ๋ณด์—ฌ์ฃผ๋Š” ๋™์‹œ์— ์ž์—ฐ ๊ตฌ์กฐ๋ฅผ ๋ฉด๋ฐ€ํ•˜๊ฒŒ ๋ชจ๋ฐฉํ•˜๊ธฐ ๋•Œ๋ฌธ์ž…๋‹ˆ๋‹ค. ๋ณธ ์‹œ๊ฐํ™” ํ”„๋กœ์ ํŠธ๋Š” ์ฐฝ์˜์ ์ธ ์ „๋ฌธ๊ฐ€๋“ค์ด ์ค‘์š”ํ•œ ๋ฌธ์ œ์— ๋Œ€ํ•ด ์‹œ๊ฐ์ ์œผ๋กœ ๋งค๋ ฅ์ ์ด๊ณ  ์ƒ๊ฐ์„ ์ž๊ทนํ•˜๋Š” ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”๋ฅผ ๋งŒ๋“ค์–ด ๋Œ€์ค‘์—๊ฒŒ ๋„๋‹ฌํ•˜๊ณ  ์‹œ๋ฏผ๋“ค์˜ ์ธ์‹์„ ํ–ฅ์ƒํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ถŒ์žฅํ•ฉ๋‹ˆ๋‹ค. ๋˜ํ•œ, ๋ณธ ๋””์ž์ธ ์—ฐ๊ตฌ๋Š” ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”์—์„œ ๋ฏธํ•™๊ณผ ์ฐฝ์กฐ์ ์ธ ์‹ค์ฒœ์˜ ์—ญํ• ์— ๋Œ€ํ•œ ๋” ๋งŽ์€ ๋…ผ์˜๋ฅผ ์œ ๋„ํ•˜๊ณ ์ž ํ•ฉ๋‹ˆ๋‹ค.Abstract I Table of Contents III List of Figures VI 1. Introduction 1 1.1 Research Background 2 1.2 Research Goal and Method 6 1.3 Terminology 9 2. Hierarchical Relationships: Trees 14 2.1 The History of Tree Diagrams 16 2.1.1 Significance of Trees 16 2.1.2 Aristotles Hierarchical Order of Life 19 2.1.3 Early Religious Depictions of Hierarchical Structures 22 2.1.4 Depicting Evolution 26 2.2 Tree Structures 29 2.3 Tree Layouts 31 3. Complex Relationships: Networks 34 3.1 Attributes of Networks 36 3.1.1 Interdependence and Interconnectedness 38 3.1.2 Decentralization 42 3.1.3 Nonlinearity 45 3.1.4 Multiplicity 46 3.2 Spatial Networks 46 3.3 Combining Tree Structures and Networks 48 4. Design Study Goals and Criteria 51 4.1 Objectives of the Design Study 71 4.2 Data Visualization Approaches 54 4.3 Criteria of Data Visualization 57 4.3.1 Aesthetics 58 4.3.2 Information Visualization Principles 62 4.3.2.1 Visual Cues in Data Visualization 62 4.3.2.2 Gestalt Principles 65 4.3.2.3 Increasing Efficiency of Network Visualizations 67 4.4 Case Study 70 5. Design Study: Data Garden Method 78 5.1 Concept of the Data Garden Structure 79 5.2 Data Garden Tree Structure 84 5.2.1 360ยฐVertical Branches 85 5.2.2 Break Point of the Branches 87 5.2.3 Aligning Hierarchy Levels 89 5.2.3.1 Design 01 โ€“ Extend Method 90 5.2.3.2 Design 02 โ€“ Collapse Method 91 5.2.4 Node Placement Technique 92 5.3 Conveying 3D Information 95 6. Design Study: Visualization Project 98 6.1 Theme 99 6.1.1 E-waste Trade 100 6.1.2 Environmental Performance Index 102 6.2 Visual Design Concept 104 6.3 Assigning Attributes 105 6.4 Visual Design Process 107 6.4.1 Leaf (Node) Design Process 107 6.4.1.1 Leaf Inspiration 107 6.4.1.2 Leaf Design 108 6.4.1.3 Leaf Area Calculation and Alignment 113 6.4.2 Stem (Branch) Design Process 116 6.4.3 Root (Link) Design Process 117 6.5 Interaction Design 118 6.5.1 Navigation 118 6.5.2 User Interface 119 6.5.3 Free and Detail Modes 120 6.5.4 Data Details 121 6.6 Visualization Renders 122 6.7 Exhibition 129 7. Conclusion 131 7.1 Conclusion 132 7.2 Limitations and Further Research 133 Bibliography 135 ๊ตญ๋ฌธ์ดˆ๋ก (Abstract in Korean) 144Docto

    Inj Prev

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    BackgroundThe complexity of current injury-related health issues demands the usage of diverse and massive data sets for comprehensive analyses, and application of novel methods to communicate data effectively to the public health community, decision-makers and the public. Recent advances in information visualisation, availability of new visual analytic methods and tools, and progress on information technology provide an opportunity for shaping the next generation of injury surveillance.ObjectiveTo introduce data visualisation conceptual bases, and propose a visual analytic and visualisation platform in public health surveillance for injury prevention and control.MethodsThe paper introduces data visualisation conceptual bases, describes a visual analytic and visualisation platform, and presents two real-world case studies illustrating their application in public health surveillance for injury prevention and control.ResultsApplication of visual analytic and visualisation platform is presented as solution for improved access to heterogeneous data sources, enhance data exploration and analysis, communicate data effectively, and support decision-making.ConclusionsApplications of data visualisation concepts and visual analytic platform could play a key role to shape the next generation of injury surveillance. Visual analytic and visualisation platform could improve data use, the analytic capacity, and ability to effectively communicate findings and key messages. The public health surveillance community is encouraged to identify opportunities to develop and expand its use in injury prevention and control.CC999999/Intramural CDC HHS/United States2017-04-01T00:00:00Z26728006PMC483309

    Designing Controversies and their Publics

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    Controversy mapping is a teaching and research method derived from the Science and Technology Studies and meant to explore and represent modern sociotechnical issues. Striving to make the intricacy of scientific debate readable for a larger public, controversy mapping is trapped in a classic simplicity/complexity trade-?โ€off: how to respect the richness of controversies without designing maps too complicated to be useful? Having worked on the question for almost two years in a project bringing together social scientists and designers (emapsproject.com1), we can now propose a way out of this contradiction and suggest three ways of moving through the simplicity/complexity continuum. The first movement -?โ€by multiplying the number of maps and by taking into account users before the beginning and after the end of the design process-?โ€ allows to bypass the simplicity/complexity trade-?โ€off. The second movement bind together narration and exploration and allows the publics to venture in the maze of controversies unraveling the story that will guide them out. The third movement allows to involve the publics through all the phases of a cartographic campaign and to engage it again and again

    The role of the audience in contemporary data art

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    As a new form of contemporary art, data art has changed the audienceโ€™s artistic experience. This paper first introduces the characteristics of data art which are different from other contemporary art. Secondly, in the context of public participation in art, it explores how data art enables audiences to participate in various stages of artistic creation, so that audiences can become one of the creators rather than mere art connoisseurs. Finally, it summarizes participatory issues in different stages of data art

    SoundSculpt:A Design Framework for 3D Modelling and Digitally Fabricating Sound Patterns

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    Teaching Tip: Evaluating Visualizations with a Compact Rubric

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    Students now have readily available and powerful tools to access, manipulate, combine, and visualize data. Acquiring data and visual literacy requires more than knowledge of how to use these tools. Students need to engage with assignments that challenge them to make relatively complex visualizations, interpret them, and explain why these interpretations matter for given problem situations. This paper describes how to structure feedback for these assignments. The few published visualization evaluation rubrics are mainly oriented toward how-to-do-it heuristics. This paper makes a contribution by presenting, defining, and giving examples of the use of an innovative compact rubric for evaluating visualizations (CRVE). This rubric eliminates some of the length and complexity of heuristic evaluation, focusing on interpretation and relevance. In a graduate business intelligence course, students showed definite improvement over the course of the semester in the construction of visualizations, telling a story with headlines, and striving for data exploration. However, higher levels of technical correctness of visualizations did not necessarily correspond to better interpretations. This finding underscores the importance of emphasizing interpretation through a feedback mechanism like the CRVE presented here

    A Visual Analysis of EHR Flowsheet to Assist Cliniciansโ€™ Interpretation of Health-related Data

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    This project designed and implemented visualization interface of EHR Systems based on the requirements of the doctors. The visualizations combined both clinic-reported data and patient self-reported data to provide a better representation of patient health related information for the doctor to make decisions. The visualizations highlighted the trend of values, the outliers and make it possible to compare across time and measures. The user study of ten participants suggests that the visualization interface helped them find the information in an efficient way.Master of Science in Information Scienc
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