Manuel Lima Visual Complexity Pdf Download
Our ability to generate information now far exceeds our capacity to understand it. Finding patterns and making meaningful connections inside complex data networks has emerged as one of the biggest challenges of the twenty-first century. In recent years, designers, researchers, and scientists have begun employing an innovative mix of colors, symbols, graphics, algorithms, a Our ability to generate information now far exceeds our capacity to understand it. Finding patterns and making meaningful connections inside complex data networks has emerged as one of the biggest challenges of the twenty-first century.
In recent years, designers, researchers, and scientists have begun employing an innovative mix of colors, symbols, graphics, algorithms, and interactivity to clarify, and often beautify, the clutter. From representing networks of friends on Facebook to depicting interactions among proteins in a human cell, Visual Complexity presents one hundred of the most interesting examples of information-visualization by the field's leading practitioners. I hate to down-rate this book, because it is very well researched and well written, but I just wasn't able to be as excited about this as I have been with Edward Tufte's books, to which this one is compared by one of the blurb writers.
Part of the problem is that this book deals solely with visual representations of networks - an interesting genre, I'm sure, but one that stands more in the realm or art than science unless you drill down pretty far into the details of the network analysis and the I hate to down-rate this book, because it is very well researched and well written, but I just wasn't able to be as excited about this as I have been with Edward Tufte's books, to which this one is compared by one of the blurb writers. Part of the problem is that this book deals solely with visual representations of networks - an interesting genre, I'm sure, but one that stands more in the realm or art than science unless you drill down pretty far into the details of the network analysis and the techniques used to produce the visualization. Lima does at one point present a 'syntax' of network representations - interesting, I suppose, but presented too superficially to be of actual use to the would-be practitioner.
Finally, after chasing him for a long while we have Manuel Lima on the show! Manuel has been around for a very long time. He created Visual Complexity in 2005, an archive of network visualizations which became very popular. He is also the author of two great books: Visual Complexity and The Book of.
Oct 9, 2015 7. Twitter, Terrorism, Blogosphere— became the main corpus of Chapter 04 of Visual Complexity. VisualComplexity.com was one of the few resources for a. In this follow-up to his hugely popular The Book of Trees and Visual Complexity, Manuel Lima takes us on a lively tour through millennia of circular information design.Three hundred detailed and colorful illustrations from around the world cover an encyclopedic array of subjects-architecture, urban planning, fine art, design, fashion, technology, religion, cartography, biology, astronomy,.
By the latter part of the book I was just flipping pages, eager to get to the end, hoping to see something that would engage my attention. Didn't happen. Update: after reading the other reviews of this book on goodreads, I want to add that, like them, I did not get any deeper knowledge or insight into the underlying data by looking at the visualizations. Contrast that with the graphics, and the approach to visualization, offered by Tufte, and you can understand my disappointment with this book. Tufte generally shows before and after representations of data, and has clear, consistent principles on which to evaluate or create a visualization. The foremost principle is that of conveying as much information as possible; aesthetics are important, but secondary, and artistic merit barely deserves consideration.
But what are we to make of a network graph that allegedly represents millions of nodes and tens of millions of connections? Where is the information content? How does such a graph (represented visually) advance our understanding? I would claim that in nearly all cases, it does not. The subtitle of this book should be - Visualization done wrong.
This book seems to be all about dumping raw data into one frame and hope to wow readers. The result, giant hairball with no discernible pattern - fail. Miniscule, unreadable and overlapping labels - fail. No legend, no description of what visual elements like color, size, weight is encoding - fail.
There are over a hundred graph show cased. Most graph, often unreadable hairball, occupying a full page. It is accompany by a descriptio The subtitle of this book should be - Visualization done wrong. This book seems to be all about dumping raw data into one frame and hope to wow readers.
The result, giant hairball with no discernible pattern - fail. Miniscule, unreadable and overlapping labels - fail. No legend, no description of what visual elements like color, size, weight is encoding - fail. There are over a hundred graph show cased. Most graph, often unreadable hairball, occupying a full page. It is accompany by a description of about one to three sentences. In most case I cannot figure out from the minimal description what is the underlying data they are trying to depict.
Compare this to O'Reilly's Beautiful Visualization, which use an entire chapter for each topic. Not only do they have the final visualization, there are a lot of explanation on the data and quite importantly, the process used to make sense of the data. This book has pretty much nothing but colorful graphs. The are several dozen of visualization around the theme of social network. After glancing at all these pictures, I cannot say I have garner any insight at all. Out of the entire book there might be a dozen graph I considered useful. So my conclusion - this is visualization done wrong.
If you want a coffee table book on data visualization that is heavy on aesthetics and philosophical noodling, but light on practical value, then this book is for you. There are many pretty pictures, but most of them make no sense and offer little to no insight.
It seems to have a good representation of the different types of visualizations that are popular but next to no advice on why one type is better than another type for any particular purpose. Personally I would prefer less flowery delivery If you want a coffee table book on data visualization that is heavy on aesthetics and philosophical noodling, but light on practical value, then this book is for you. There are many pretty pictures, but most of them make no sense and offer little to no insight. It seems to have a good representation of the different types of visualizations that are popular but next to no advice on why one type is better than another type for any particular purpose. Personally I would prefer less flowery delivery and more analysis of why a Segmented Radial Convergence is more appropriate in certain cases than an Organic Rhizome or Centralized Burst.
There is no such analysis anywhere in this book. I picture this thing sitting conspicuously on the coffee table of half the dot-com infoviz nerds in San Francisco, next to their vaporizer and etsy coffee mug. Great book making for a great read replete with beautiful pictures. In short, I want it for my own library. It was the ideal companion to the complexity studies I am currently engaged in.
The history of the tree being used as a visualization technique in the history of knowledge and how it is being abandoned for the network visualization technique of the nodes and links model is very enlightening. It then goes into the ubiquitous nature of the interconnected network model. The book comes stocked Great book making for a great read replete with beautiful pictures. In short, I want it for my own library.
It was the ideal companion to the complexity studies I am currently engaged in. The history of the tree being used as a visualization technique in the history of knowledge and how it is being abandoned for the network visualization technique of the nodes and links model is very enlightening. It then goes into the ubiquitous nature of the interconnected network model. The book comes stocked with so many beautiful pictures of how information is being mapped and visualized and towards the end it leads to amazingly beautiful artistic renditions of modern artists.
A great read and visualization tool for anyone interested in complexity and interconnected networks studies. 'Meeting the needs of global society does not require infinite economic growth but an understanding of and respect for the regenerative limits of the biosphere.' Manuel Lima is an ecological optimist, and he believes that collecting, connecting, presenting and integrating data is the key to human survival.
He thinks we can overcome human selfishness and greed through visualization, through networkism-'the net has no center'-a complex open system that mimics natural processes, rather than the hi 'Meeting the needs of global society does not require infinite economic growth but an understanding of and respect for the regenerative limits of the biosphere.' Manuel Lima is an ecological optimist, and he believes that collecting, connecting, presenting and integrating data is the key to human survival. He thinks we can overcome human selfishness and greed through visualization, through networkism-'the net has no center'-a complex open system that mimics natural processes, rather than the hierarchical organization we are used to employing to construct our world. It's a beautiful idea, although I'm not as optimistic as he is.
I took this book out of the library for the visuals, which are amazing and often stunningly beautiful. And as sometimes happens with these things, I read a bit of the text, then more, and eventually all of it. Wide-ranging to say the least, starting with tree models and the history of mapping connections and ending with his outline for saving the world through artistry. In-between Lima covers such things as phrenology and the eerie similarity between Jackson Pollack's drip paintings and the growth of trees or neuron connections. He talks about our need to control and shape the endless stream of data that is our environment now, that defines who we are. Not just the information itself, but the time and space through which this information travels.
The illustrations are dazzling, and the author mentions '.the urge for creating visualizations that have more decorative than informative qualities.' For me, that is a huge problem-I couldn't 'read' or understand most of them as conveyers of knowledge. I realize that the systems they explain are interconnected and complex-that is easy to see. But it would be difficult for me to use them to either inform myself or make decisions based on what they say. It could be that they can only be interpreted by specialists, which is actually OK, but I would like to see attempts to portray some of these concepts in a way that reaches a larger audience. Or maybe reading these new maps needs to be taught as a subject in school, so at least the next generation is fluent in the language they speak. But back to my original reason for looking at Lima's book: wow!
Even the archival tree-maps that he begins with are beautiful. By the time you reach the visualization of Lisbon's traffic or the chromosonal relationships within one genome you will be on sensory overload (in a good way). This is a keeper-a book to return to and also think about many times, in many ways.
And I hope his vision comes true. Lima is a passionate guide to visualization. He begins with a brief history of attempts to visualize complex information (many of them religious in nature) and moves into the computer age briskly, covering a number of essential concepts regarding data and Big Data, networking, and the internet. The strongest part of the book is the section cataloging the multiple forms that new visualizations take, with examples of each and lots of references that enable one to find the live versions online (an Lima is a passionate guide to visualization. He begins with a brief history of attempts to visualize complex information (many of them religious in nature) and moves into the computer age briskly, covering a number of essential concepts regarding data and Big Data, networking, and the internet.
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The strongest part of the book is the section cataloging the multiple forms that new visualizations take, with examples of each and lots of references that enable one to find the live versions online (an irony of this volume is that many of the best visualizations are interactive and can be viewed better via computer than on the page). At the same time as I loved seeing these fabulous examples of how to move beyond the graph and pie chart, I often found myself entirely stupefied at what I was seeing and not really understanding whether or not I now knew something more as a result of having seen the data presented in a certain format. Sure, the images (such as this one by Chris Harrison visualizing biblical cross references: ) are somehow organically pleasing in the way that top grade micrographic images are (see, e.g., ), but do I glean great knowledge than I would have via close readings or careful scrutiny of data tables? My knee jerk reaction, as a fan of these visualizations is, 'Of course I do,' but I am actually hard put to demonstrate how that is the case.
Very nicely designed, glossy overview of network visualization approaches, as of a few years back. Insightful history and analysis as well as the pretty pictures. But, left me with the feeling that, with a few exceptions, plotting networks with more than a couple of dozen nodes is pointless, and that perhaps there are better ways of finding and providing insights from connectivity data. Very few of the images came with captions that said, 'and thus, we learned an impactful fact', or 'and thus, w Very nicely designed, glossy overview of network visualization approaches, as of a few years back.
Insightful history and analysis as well as the pretty pictures. But, left me with the feeling that, with a few exceptions, plotting networks with more than a couple of dozen nodes is pointless, and that perhaps there are better ways of finding and providing insights from connectivity data. Very few of the images came with captions that said, 'and thus, we learned an impactful fact', or 'and thus, were minds changed.' For non-network visualization, this sort of impact statement is very common. So perhaps this book should be read as a cautionary tale? This is not an easy book to rate, given that it's partially a guide to understanding data visualization and partially an enthralling coffee-table-esque tome (that still needed to be carried to and from coffee shops so I could read it). Lima writes in an engaging manner and his work in explicating what visualizations should do and are good for is well done.
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I could almost wish for a bit more text, actually, some of his thoughts and interpretations of the many visualizations within. Seeing him 'rea This is not an easy book to rate, given that it's partially a guide to understanding data visualization and partially an enthralling coffee-table-esque tome (that still needed to be carried to and from coffee shops so I could read it).
Lima writes in an engaging manner and his work in explicating what visualizations should do and are good for is well done. I could almost wish for a bit more text, actually, some of his thoughts and interpretations of the many visualizations within. Seeing him 'read' them would have made a very good primer to data visualization even better.