Visualization and Interpretation: Humanistic Approaches to Display

205 Pages·2020·9.301 MB·English

by  Johanna Drucker

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 cur-
rent 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.