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Visual Complexity

Page history last edited by eshayne 8 years, 4 months ago

Research Report: Manuel Lima's Visual Complexity: Mapping Patterns of Information.


By Liz Shayne, Playful Visualizations at Work/Working Visualizations at Play Team



Manuel Lima’s project in Visual Complexity is twofold: he wishes to provide material examples of outstanding complex network visualizations and, alongside them, present a coherent narrative of the importance of network visualization in current culture. The textual elements of the book provide a history of the network as an epistemological tool and then provide a set of guidelines aimed at producing meaningful and beautiful visualizations. Lima uses visualizations to illustrate his points as well as devoting several chapters to examining different types of visualization techniques. Lima’s explanatory passages make a compelling argument for the aesthetic and informative value of complex data visualization.



Lima’s text begins with an explanation of how the network came to be an important force in structuring contemporary modes of thought and understanding. He opens with the network’s roots in the idea of the tree, discussing the latter’s usefulness in shaping how people categorized information before introducing the idea of the network, or the rhizome as Gilles Deleuze and Felix Guattari would call it, as a better conceptual framework for the sort of visualization now necessary. “The complex connectedness of modern times requires new tools of analysis and exploration, but above all, it demands a new way of thinking. It demands a pluralistic understanding of the world that is able to envision the wider structural plan and at the same time examine the intricate mesh of connections among its smallest elements...it calls for networked thinking” (Lima, 45-46). Lima provides several examples of complex modern entities that can only be thought of in terms of networked thinking. Prominent among these examples are cities and urban planning, the neurons in the brain and the Internet. Alongside each, he includes visualizations that have been done with these networks to illustrate his points.


The visualizations interspersed among the text continue as Lima instructs his readers in what visualizations are meant to accomplish and how the viewer can interpret them. Visualizations have five specific purposes; they document new systems, clarify existing systems, reveal new insights, expand possible uses for a given system and serve as a form of abstract expression (80-81).  In order for them to do so effectively, Lima calls for a corresponding grammar of visualization that sets out rules for how information is conveyed and how meaning is made with visual language, much like English grammar. His grammar is less a set of inviolate rules and more a list of best practices that makes visualization coherent and interpretable. His suggestions range from exhorting the visual designer to think about the relevance of his visualization to advising on matters of color and node size as convenient and aesthetically pleasing methods of conveying information. Like a well-written document, a well-designed visualization is better at speaking its information.

These earlier sections set up the second half of the book, which consists predominantly of visualizations that showcase—using images—what Lima has heretofore been conveying primarily through text. These visualizations were originally collected on Lima’s blog, Visual Complexity, and they are divided into two chapters. The first chapter sorts visualizations based on what kinds of data sets they use, grouping together different types of visualizations interested in graphing works of literature or terrorist cells or the distribution of academic journal article citations, to name a few examples. The second chapter is a survey of the different kinds of visualizations, such as “organic rhizome,” “radial implosion” or “scaled circle” . Other than the barest descriptions of the visualizations themselves, these sections are devoid of textual elaboration and Lima expects the reader to apply the principles of visualization laid out in the previous chapters.


Lima closes his work with some thoughts about the aesthetic appeal of complexity. Complex visualization is not simply the only tool capable of dealing with large amounts of data—the inevitable result of the modern era—but is also a form of art that speaks to the pattern-sensing centers of our brains. “Networks show us that there is order in disorder, that there is unity in diversity, and above all, that complexity is astonishingly beautiful” (243).



Lima’s book serves almost as a crash-course in visual reading, split as it is between the sections that explain the purpose of graphing and those that display multiple visualizations per page. Though not, perhaps, the author’s intention, the book’s structure evokes that of a lecture with a quiz at the end as a way for the student—reader—to test her knowledge. This structure is particularly helpful to the literary scholar who understands textual analysis quite well, but has rarely been asked to read a graph or appreciate the visual grammar it uses. Though Lima’s book is not a textbook, it does lay out a number of useful principles with which to judge the visualizations created from my text, Daniel Deronda, and to assess whether said visualizations are speaking as eloquently as I would like.


Lima’s identification of the five possible purposes of visualizations was illuminating, especially as a reminder that visualizations are not meant to take the place of the data they analyze. A fair number of the visualizations in the book were incomprehensible, at least to this reader who lacks a background in genomics or European traffic patterns, and Lima’s inclusion of such images serves both force his reader’s eye to appreciate them on a purely aesthetic level and to remind his readers that images are just as worthwhile when they comment on what is already known to those who already know it, rather than when they take large amounts of data and package it into a readable format. When thinking about visualizations of novel-length texts, it seems fairly obvious that staring at a word cloud, even one that includes every word in the novel, is not the same as reading it and cannot provide the same effect. The effect it does produce, however, is striking in its own right and often reveals new insights into the text or simply works to defamiliarize the reader with the text. The novel is a temporal creature, one that literary scholars encounter (at least at first) in a linear fashion and that we experience over the course of several hours or days. The visualization exists in a different dimension from the novel, sprawling out over space instead of over time, and Lima’s struggle in this book is to teach his reader how to appreciate the organic unity of that spatial creature.


In the end, Lima’s biggest problem is that his words and the illustrations he provides to accompany them never truly speak to one another. While he does provide valuable insight into what a reader of graphs should be looking for, he rarely enacts such readings on the images he provides. There are moments when he gestures towards the figures on the opposite pages, but he never performs a detailed reading of the sort he expects his readers to perform. I noted earlier that Lima’s book was rather like a lecture followed by a quiz and I will qualify that now by saying that I wish the quiz had some form of grading rubric included. For all that Lima positions himself as an educator, much of the work he performs here is curatorial, leaving the reader to form her own impressions. From the perspective of aesthetic visualizations, such things are all well and good, but for those of us who also wish to expand our knowledge about effective visualizations, his reticence leaves something to be desired.


This is not to say that I fault Lima for providing a text that does what he wishes it to do, rather than what I wish it did. Lima’s work privileges aesthetics over information; he places beautiful visualizations with no discernable purposes next to equally lovely works that are crucial for scientific developments. Within the digital humanities, I have observed the tendency to provide visualizations alongside data analysis that are neither particularly readable nor particularly visually compelling. Reexamining those graphs after spending time poring over Lima’s glossy, full page spreads was disappointing, but also a reminder of something Lima elides somewhat, though it underpins the entire book. Complex networks are made beautiful in the hands of competent graphic designers; beauty is no more innate to the act of data visualization than it is to the act of spattering paint on a canvas. Graphs, like paintings, are works of art and, like the most compelling of the latter, speak using the aesthetics of their media. Several scholars in the Digital Humanities, Stephen Ramsay foremost among them, have stated that one of the requirements of working in this field is knowing how to code. Is there an equivalent requirement for those of us working with visualizations that we learn at least the rudiments of graphic design? A work like Lima’s provides a compelling argument for me to answer yes.


Resources for Further Study:

Dondis, Donis A. A Primer for Visual Literacy. Boston: MIT Press. 1973. Print.


Klanten, Robert, Sven Ehmann and Floyd Schulze. Visual Storytelling: Inspiring a New Visual Language. Berlin: Gestalten. 2011. Print.


Lima, Manuel. Visual Complexity: Mapping Patterns of Information. New York: Princeton Architectural Press. 2011. Print.

--VisualComplexity.com. 2005. Accessed: 5/17/2012. Web.


Paley, W. Bradford. TextArc. http://www.textarc.org/ Accessed: 5/16/2012. Web.




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