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Visualization and Interpretation Visualization and Interpretation Humanistic Approaches to Display Johanna Drucker The MIT Press Cambridge, Massachusetts London, England © 2020 Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. This book was set in Stone Serif and Stone Sans by Westchester Publishing Ser vices. Library of Congress Cataloging- in- Publication Data Names: Drucker, Johanna, 1952- author. Title: Visualization and interpretation : humanistic approaches to display / Johanna Drucker. Description: Cambridge, Massachusetts : The MIT Press, 2020. | Includes bibliographical references and index. Identifiers: LCCN 2020002673 | ISBN 9780262044738 (hardcover) Subjects: LCSH: Visual communication. | Graphic arts. | Visual analytics. Classification: LCC P93.5 .D85 2020 | DDC 302.2/26-- dc23 LC record available at https:// lccn . loc . gov / 2020002673 Contents Acknowledgments vii Framework: Creating the Right Tools and Platforms 1 1 Visual Knowledge (or Graphesis): Is Drawing as Powerful as Computation? 11 2 Interpretation as Probabilistic: Showing How a Text Is Made by Reading 43 3 Graphic Arguments: Nonrepresentational Approaches to Modeling Interpretation 69 4 Interface and Enunciation, or, Who Is Speaking? 91 5 The Projects in Modeling Interpretation, or, Can We Make Arguments Visually? 111 Appendix: Design Concepts and Prototypes 139 Notes 177 Index 193 Acknowledgments Some sections of this work draw on materials from the author’s previously published work. All are republished or cited either with explicit permission or in accord with the authors’ rights guidelines: “Humanities Approaches to Interface Theory,” Culture Machine 12 (2011) (by permission). “Humanistic Theory and Digital Scholarship,” in Debates in Digital Humanities, ed. Matthew Gold (Minneapolis: University of Minnesota Press, 2012), pp. 85–9 5. “Non-r epresentational Approaches to Modelling Interpretation in a Graphical Envi- ronment,” Digital Scholarship in the Humanities 33, no. 2 (June 2018) (by permission and guidelines), https:// doi . org / 10 . 1093 / llc / fqx034 (5300). “Information Visualization and/as Enunciation,” Journal of Documentation 73, no. 5 (2017), pp. 903– 916, https:// doi . org / 10 . 1108 / JD - 01 - 2017 - 0004 . “Performative Materiality and Theoretical Approaches to Interface,” DHQ (Digital Humanities Quarterly) 7, no. 1 (Summer 2013). “Design Agency,” Dialectic 1, no. 2 (2017), pp. 11–1 6. “Digital Ontologies: The Ideality of Form in/and Code Storage—o r—C an Graphesis Challenge Mathesis?,” Leonardo 34, no. 2 (2001), pp. 141–1 45. All images are property of Johanna Drucker. Framework: Creating the Right Tools and Platforms In the several decades since humanists have taken up computational tools, they have borrowed many techniques from other fields. This has included the appropriation of visualization methods to create charts, graphs, diagrams, maps, and other graphic displays of information. But are these visualizations actually adequate for the interpretative approach that distinguishes much of the work in the humanities? To answer that question, we have to define the features of that interpretative work and identify the ways its assumptions and premises are distinct from those of other research methods. We also have to ask what kinds of attitudes toward knowledge can be expressed with current conventions of visualization and consider alternatives. If, as will be argued here, the activity of modeling interpretation is fundamentally at odds with current methods of information visualization (data display), then how would such interpretative work be structured within a computational environment and a user interface? The challenges are many. We can start by considering what is being referred to in the phrase infor mation visualization and how it contrasts with that of modeling interpreta tion. The standard approach to information visualization is to generate a graphic from live or static data. The process is relatively straightforward. A set of quantitative values is charted on a grid, plane, or space governed by a regular, standard metric. Columns, bars, pie charts, points, lines, network diagrams, and so on are all produced in this way. The interpretative work of shaping the data disappears from view in the final result. The image displayed on screen, in print, or through other output devices appears as a statement of fact. The interpretative dimensions of the activity that shaped the data are rendered invisible, not so much concealed as simply missing from view, absent without a trace.