Coaches are highest paid public employees by Nathan Yau.
Nathan makes another amazing find!
The topic map lesson here is effective presentation of information.
For the same data, the Obama Administration would list all U.S. public employees in order by internal department names and separately list positions with salaries, in a PDF file.
I know which strategy I prefer.
You?
Binify + D3 = Gorgeous honeycomb maps by Chris Wilson.
From the post:
Most Americans prefer to huddle together around urban areas, which raises all sorts of problems for map-based visualizations. Coloring regions according to a data value, known as a choropleth map, leaves the map maker beholden to arbitrary political boundaries and, at the county level, pixel-wide polygons in parts of the Northeast. Many publications prefer to place dots proportional in area to the data values over the center of each county, which inevitably produces overlapping circles in these same congested regions. Here’s a particularly atrocious example of that strategy I once made at Slate:
Two weeks ago, Kevin Schaul released an exciting new command-line tool called binify that offers a brilliant alternative. Schaul’s tool takes a series of points and clusters them (or “bins” them) into hexagonal tiles. Check out the introductory blog post on his site.
Binify operates on .shp files, which can be a bit difficult to work with for those of us who aren’t GIS pros. I put together this tutorial to demonstrate how you can take a raw series of coordinates and end up with a binned hexagonal map rendered in the browser using d3js and topojson, both courtesy of the beautiful mind of Mike Bostock. All the source files we’ll need are on Github.
I think everyone will agree with Chris, that is truly an ugly map. 
Chris’ post takes you through how to make a much better one.
Motif Simplification: Improving Network Visualization Readability with Fan, Connector, and Clique Glyphs by Cody Dunne and Ben Shneiderman.
Abstract:
Analyzing networks involves understanding the complex relationships between entities, as well as any attributes they may have. The widely used node-link diagrams excel at this task, but many are difficult to extract meaning from because of the inherent complexity of the relationships and limited screen space. To help address this problem we introduce a technique called motif simplification, in which common patterns of nodes and links are replaced with compact and meaningful glyphs. Well-designed glyphs have several benefits: they (1) require less screen space and layout effort, (2) are easier to understand in the context of the network, (3) can reveal otherwise hidden relationships, and (4) preserve as much underlying information as possible. We tackle three frequently occurring and high-payoff motifs: fans of nodes with a single neighbor, connectors that link a set of anchor nodes, and cliques of completely connected nodes. We contribute design guidelines for motif glyphs; example glyphs for the fan, connector, and clique motifs; algorithms for detecting these motifs; a free and open source reference implementation; and results from a controlled study of 36 participants that demonstrates the effectiveness of motif simplification.
When I read “replace,” “aggregation,” etc., I automatically think about merging in topic maps.
After replacing “common patterns of nodes and links” I may still be interested in the original content of those nodes and links.
Or I may wish to partially unpack them based on some property in the original content.
Definitely a paper for a slow, deep read.
Not to mention research on the motifs in graph representations of your topic maps.
I first saw this in Visualization Papers at CHI 2013 by Enrico Bertini.
Visualization – Human-Computer Interaction Lab – University of Maryland
From the webpage:
We believe that the future of user interfaces is in the direction of larger, information-abundant displays. With such designs, the worrisome flood of information can be turned into a productive river of knowledge. Our experience during the past eight years has been that visual query formulation and visual display of results can be combined with the successful strategies of direct manipulation. Human perceptual skills are are quite remarkable and largely underutilized in current information and computing systems. Based on this insight, we developed dynamic queries, starfield displays, treemaps, treebrowsers, zoomable user interfaces, and a variety of widgets to present, search, browse, filter, and compare rich information spaces.
There are many visual alternatives but the basic principle for browsing and searching might be summarized as the Visual Information Seeking Mantra: Overview first, zoom and filter, then details-on-demand. In several projects we rediscovered this principle and therefore wrote it down and highlighted it as a continuing reminder. If we can design systems with effective visual displays, direct manipulation interfaces, and dynamic queries then users will be able to responsibly and confidently take on even more ambitious tasks.
Projects and summaries of projects too numerous to list.
Working my way through them now.
Thought you might enjoy perusing the list for yourself.
Lots of very excellent work!
Evaluating the Efficiency of Physical Visualizations by Yvonne Jansen, Pierre Dragicevic and Jean-Daniel Fekete.
Abstract:
Data sculptures are an increasingly popular form of physical visualization whose purposes are essentially artistic, communicative or educational. But can physical visualizations help carry out actual information visualization tasks? We present the first infovis study comparing physical to on-screen visualizations. We focus on 3D visualizations, as these are common among physical visualizations but known to be problematic on computers. Taking 3D bar charts as an example, we show that moving visualizations to the physical world can improve users’ efficiency at information retrieval tasks. In contrast, augmenting on-screen visualizations with stereoscopic rendering alone or with prop-based manipulation was of limited help. The efficiency of physical visualizations seems to stem from features that are unique to physical objects, such as their ability to be touched and their perfect visual realism. These findings provide empirical motivation for current research on fast digital fabrication and self-reconfiguring interfaces.
My first thought on reading this paper was a comparison of looking at a topographic map of an area and seeing it from the actual location.
May explain some of the disconnect between military planners looking at maps and troops looking at terrain.
I’m not current on the latest feedback research to simulate the sense of touch in VR.
Curious how good the simulation would need to be to approach the efficiency of physical visualizations?
While others struggle to deliver content to a 3″ to 5″ inch screen, you can work on the next generation of interfaces, which are as large as you can “see.”
I first saw this at: Visualization Papers at CHI 2013 by Enrico Bertini.
Weighted Graph Comparison Techniques for Brain Connectivity Analysis by Basak Alper, Benjamin Bach, Nathalie Henry Riche.
Abstract:
The analysis of brain connectivity is a vast field in neuroscience with a frequent use of visual representations and an increasing need for visual analysis tools. Based on an in-depth literature review and interviews with neuroscientists, we explore high-level brain connectivity analysis tasks that need to be supported by dedicated visual analysis tools. A significant example of such a task is the comparison of different connectivity data in the form of weighted graphs. Several approaches have been suggested for graph comparison within information visualization, but the comparison of weighted graphs has not been addressed. We explored the design space of applicable visual representations and present augmented adjacency matrix and node-link visualizations. To assess which representation best support weighted graph comparison tasks, we performed a controlled experiment. Our findings suggest that matrices support these tasks well, outperforming node-link diagrams. These results have significant implications for the design of brain connectivity analysis tools that require weighted graph comparisons. They can also inform the design of visual analysis tools in other domains, e.g. comparison of weighted social networks or biological pathways.
The study used only eleven (11) participants on tasks that are domain dependent, but the authors are to be lauded for noticing:
While weighted graphs are present in a plethora of domains: computer networks, social networks, biological pathways networks, air traffic networks, commercial trade net-works; very few tools currently exist to represent and compare them. As we used generic comparison tasks during the study, our results can also inform the design of general weighted graph comparison tools.
Rather than inventing yet another weighted graph comparison tool, the authors compared some of the options for visualizing a weighted graph with users.
Evidence based interface design?
I first saw this at: Visualization Papers at CHI 2013 by Enrico Bertini.
Spatially Visualize and Analyze Vast Data Stores with Esri’s GIS Tools for Hadoop
From the post:
Perhaps the greatest untapped IT resource available today is the ability to spatially analyze and visualize Big Data. As part of its continuing effort to expand the use of geographic information system (GIS) technology among web, mobile, and other developers, Esri has launched GIS Tools for Hadoop. The toolkit removes the obstacles of building map applications for developers to truly capitalize on geoenabling Big Data within Hadoop—the popular open source data management framework. Developers now will be able to answer the where questions in their large data stores.
“Hadoop’s method of processing volumes of information directly addresses the most significant challenge facing IT today,” says Marwa Mabrouk, product manager at Esri. “Enabling Hadoop with spatial capabilities is part of Esri’s continued effort to derive more value from Big Data through spatial analysis.”
Processing and displaying Big Data on maps requires functionality that core Hadoop lacks. GIS Tools for Hadoop extends the Hadoop platform with a series of libraries and utilities that connect Esri ArcGIS to the Hadoop environment. It allows ArcGIS users to export map data in HDFS format—Hadoop’s native file system—and intersect it with billions of records stored in Hadoop. Results can be either directly saved to the Hadoop database or reimported back to ArcGIS for higher-level geoprocessing and visualization.
GIS Tools for Hadoop includes the following:
- Sample tools and templates that demonstrate the power of GIS
- Spatial querying inside Hadoop using Hive—Hadoop’s ad hoc querying module
- Geometry Library to build spatial applications in Hadoop
“GIS Tools for Hadoop not only introduces spatial analysis to Hadoop but creates a looping workflow that pulls Big Data into the ArcGIS environment,” says Mansour Raad, senior software architect at Esri. “It provides tools for Hadoop users who need to visualize Big Data on maps.”
Esri recognizes Big Data as a challenge that community-level involvement can help solve. As such, Esri provides GIS Tools for Hadoop as an open source product available on GitHub. Esri encourages users to download the toolkit, report issues, and actively contribute to improving the tools through the GitHub system.
To download GIS Tools for Hadoop, visit http://esri.github.com/gis-tools-for-hadoop.
Once you have where, your topic map can merge in who, what, why and how.
Filtergraph: A Web-based Data Visualization Application by Dan Burger.
From the post:
Datasets.
If you work in astronomy, chances are you have them.
They are hardly the romantic vision that you had when you decided to go into astronomy: staring through a telescope in the middle of nowhere, surrounded by the stillness of the night sky and the sound of crickets in every direction, and updating your status on Facebook every time you find a new exoplanet.
They are the kind that arrive in your inbox as an ASCII file with thousands of lines of data. How are you going to visualize and make sense of it? Sure, you could fire up IDL, read in the data, and issue some plotting commands. But what if you wanted to quickly filter the data according to various criteria across multiple variables? What if you need to do some transformations on the data and then visualize the result? What if you want to share these data with your collaborators for on-the-fly visualization and discussion, and not just keep forwarding that big ASCII file around?
Fortunately for you there’s Filtergraph: a web application that allows you to upload data from your project and instantly generate a web portal that can build graphs and tables on-the-fly based on your dataset. Filtergraph can handle datasets of up to 3 million lines and is flexible enough to build sophisticated graphs from such large datasets in seconds. If you want to create a scatter plot of Teff versus flux, Filtergraph can do that. If you wanted something more complex, say, a scatter plot of Teff vs. log(flux), color-coded by [Fe/H] and symbol size scaled to the distance, for the stars that are between 8 and 18 hrs in RA. Filtergraph can do that as well, with just a few clicks. There is no need to write many lines of IDL code full of where commands.
I know you would have to hunt for an ASCII file of less than 3 million lines but they do exist.
This could be a very “lite” way to share a dataset for visualization.
Why the Obsession with Tables? by Robert Kosara.
From the post:
Lots of data are still presented and released as tables. But why, when we know that visual representations are so much easier to read and understand? Eric Newburger from the U.S. Census Bureau has an interesting theory.
In a short talk on visualization at the Census Bureau, he describes how in the 1880s, the Census published maps and charts. Many of those are actually amazingly well done, even by today’s standards. But starting with 1890 census, they were replaced with tables.
This, according to Newburger, was due to an important innovation: the Hollerith Tabulating Machine. The new machines were much faster and could slice and dice the data in a lot of new ways, but their output ended up in tables. Throughout the 20th century, the Census created enormous amount of tables, with only a small fraction of the data shown as maps or charts.
Newburger argues that people don’t bother trying to read tables, whereas visualizations are much more likely to catch their attention and get them interested in the underlying data. We clearly have the means to create any visualization we want today, and there is plenty of data available, so why keep publishing tables? It’s a matter of the attitudes towards data, and these can be hard to change after more than 100 years:
Suggestions of images from maps and charts from the Census in the 1880s?
If the Hollerith Tabulating Machine is responsible for the default to tables, it is also responsible for spreadsheets?
Quicker for a machine to produce but less useful to an end user.
From the webpage:
MindMup is a zero-friction mind map canvas. Our aim is to create the most productive mind mapping environment out there, removing all the distractions and providing powerful editing shortcuts.
This git project is the JavaScript visualisation portion of MindMup. It provides a canvas for users to create and edit mind maps in a browser. You can see an example of this live on http://www.mindmup.com.
This project is relatively stand alone and you can use it to create a nice mind map visualisation separate from the MindMup Server.
Do see the live demo at: http://www.mindmup.com.
It may not fit your needs but it is a great demo of thoughful UI design. (At least to me.)
Could be quite useful if you like The Back of the Napkin : Solving Problems and Selling Ideas with Pictures by Dan Roam.
I recently started reading “The Back of the Napkin,” and will have more to report on it in a future post. So far, it has been quite a delight to read.
I first saw this at JQuery Rain under: MindMup MapJs : Zero Friction Mind Map Canvas with jQuery.
Atlas of Design by Caitlin Dempsey.
From the post:
Do you love beautiful maps? The Atlas of Design has been reprinted and is now available for purchase. Published by the North American Cartographic Information Society (NACIS), this compendium showcases cartography at some of its finest. The atlas was originally published in 2012 and features the work of 27 cartographers. In early 2012, a call for contributions was sent out and 140 entries from 90 different individuals and groups submitted their work. A panel of eight volunteer judges plus the book’s editors evaluated the entries and selected the finalists.
The focus of the Atlas of Design is on the aesthetics and design involved in mapmaking. Tim Wallace and Daniel Huffman, the editors of Atlas of Design explain the book’s introduction about the focus of the book:
Aesthetics separate workable maps from elegant ones.
This book is about the latter category.
My personal suspicion is that aesthetics separate legible topic maps from those that attract repeat users.
The only way to teach aesthetics (which varies by culture and social group) is by experience.
This is a great starting point for your aesthetics education.
So you want to look at a graph by email: Carlos Scheidegger.
From the post:
Say you are given a graph and are told: “Tell me everything that is interesting about this graph”. What do you do? We visualization folks like to believe that good pictures show much of what is interesting about data; this series of posts will carve a path from graph data to good graph plots. The path will take us mostly through well-known research results and techniques; the trick here is I will try to motivate the choices from first principles, or at least as close to it as I can manage.
One of the ideas I hope to get across is that, when designing a visualization, it pays to systematically consider the design space. Jock MacKinlay’s 1986 real breakthrough was not the technique for turning a relational schema into a drawing specification. It was the realization that this systematization was possible and desirable. That his technique was formal enough to be encoded in a computer program is great gravy, but the basic insight is deeper.
Of course, the theory and practice of visualization in general is not ready for a complete systematization, but there are portions ripe for the picking. In this series, I want to see what I can do about graph visualization.
If you like this introduction, be sure to follow the series to:
So you want to look at a graph, part 1
This series of posts is a tour through of the design space of graph visualization. As I promised, I will do my best to objectively justify as many visualization decisions as I can. This means we will have to go slow; I won’t even draw anything today! In this post, I will only take the very first step: all we will do is think about graphs, and what might be interesting about them.
So you want to look at a graph, part 2
This series of posts is a thorough examination of the design space of graph visualization (Intro, part 1). In the previous post, we talked about graphs and their properties. We will now talk about constraints arising from the process of transforming our data into a visualization.
So you want to look at a graph, part 3
This series of posts is a tour of the design space of graph visualization. I’ve written about graphs and their properties, and how the encoding of data into a visual representation is crucial. In this post, I will use those ideas to justify the choices behind a classic algorithm for laying out directed, mostly-acyclic graphs.
More posts are coming!
Abstract Maps For Powerful Impact by Jim Vallandingham.
You can follow the abstraction, even from the bare slides.
Still, it is a slide deck that makes you wish for the video.
Rolling out a “How-To” Software Series
A long preface that ends with a list of posts on “how to” use NodeXL.
Looks very good!
Enjoy!
From the webpage:
Nozzle is an R package for generation of reports in high-throughput data analysis pipelines. Nozzle reports are implemented in HTML, JavaScript, and Cascading Style Sheets (CSS), but developers do not need any knowledge of these technologies to work with Nozzle. Instead they can use a simple R API to design and implement powerful reports with advanced features such as foldable sections, zoomable figures, sortable tables, and supplementary information. Please cite our Bioinformatics paper if you are using Nozzle in your work.
I have only looked at the demo reports but this looks quite handy.
It doesn’t hurt to have extensive documentation to justify a conclusion that took you only moments to reach.
Frank Nielsen blogged today that he had posted the C++ source code for “Visual Computing: Geometry, Graphics, and Vision.”
See: Source codes for all chapters of “Visual Computing: Geometry, Graphics, and Vision”
New demos are reported to be on the way.
A Tour through the Visualization Zoo by Jeffrey Heer, Michael Bostock, Vadim Ogievetsky.
From the article:
Thanks to advances in sensing, networking, and data management, our society is producing digital information at an astonishing rate. According to one estimate, in 2010 alone we will generate 1,200 exabytes—60 million times the content of the Library of Congress. Within this deluge of data lies a wealth of valuable information on how we conduct our businesses, governments, and personal lives. To put the information to good use, we must find ways to explore, relate, and communicate the data meaningfully.
The goal of visualization is to aid our understanding of data by leveraging the human visual system’s highly tuned ability to see patterns, spot trends, and identify outliers. Well-designed visual representations can replace cognitive calculations with simple perceptual inferences and improve comprehension, memory, and decision making. By making data more accessible and appealing, visual representations may also help engage more diverse audiences in exploration and analysis. The challenge is to create effective and engaging visualizations that are appropriate to the data.
Creating a visualization requires a number of nuanced judgments. One must determine which questions to ask, identify the appropriate data, and select effective visual encodings to map data values to graphical features such as position, size, shape, and color. The challenge is that for any given data set the number of visual encodings—and thus the space of possible visualization designs—is extremely large. To guide this process, computer scientists, psychologists, and statisticians have studied how well different encodings facilitate the comprehension of data types such as numbers, categories, and networks. For example, graphical perception experiments find that spatial position (as in a scatter plot or bar chart) leads to the most accurate decoding of numerical data and is generally preferable to visual variables such as angle, one-dimensional length, two-dimensional area, three-dimensional volume, and color saturation. Thus, it should be no surprise that the most common data graphics, including bar charts, line charts, and scatter plots, use position encodings. Our understanding of graphical perception remains incomplete, however, and must appropriately be balanced with interaction design and aesthetics.
This article provides a brief tour through the “visualization zoo,” showcasing techniques for visualizing and interacting with diverse data sets. In many situations, simple data graphics will not only suffice, they may also be preferable. Here we focus on a few of the more sophisticated and unusual techniques that deal with complex data sets. After all, you don’t go to the zoo to see Chihuahuas and raccoons; you go to admire the majestic polar bear, the graceful zebra, and the terrifying Sumatran tiger. Analogously, we cover some of the more exotic (but practically useful!) forms of visual data representation, starting with one of the most common, time-series data; continuing on to statistical data and maps; and then completing the tour with hierarchies and networks. Along the way, bear in mind that all visualizations share a common “DNA”—a set of mappings between data properties and visual attributes such as position, size, shape, and color—and that customized species of visualization might always be constructed by varying these encodings.
Most of the visualizations shown here are accompanied by interactive examples. The live examples were created using Protovis, an open source language for Web-based data visualization. To learn more about how a visualization was made (or to copy and paste it for your own use), simply “View Source” on the page. All example source code is released into the public domain and has no restrictions on reuse or modification. Note, however, that these examples will work only on a modern, standards-compliant browser supporting SVG (scalable vector graphics ). Supported browsers include recent versions of Firefox, Safari, Chrome, and Opera. Unfortunately, Internet Explorer 8 and earlier versions do not support SVG and so cannot be used to view the interactive examples.
….
Some what dated but still a useful overview of visualization.
I first saw this at A Tour Through the Visualization Zoo by Alex Popescu.
GeoLocation Friends Visualizer by Marcel Caraciolo.
Slides from a presentation at the XXVI Pernambuco’s Python User Group meeting.
Code at: https://github.com/marcelcaraciolo/Geo-Friendship-Visualization
Just to get you interested:
If you had the phone records (cell and land) from elected and appointed government officials, you could begin to build a visualization of the government network.
In terms of an “effective” data leak, it is hard to imagine a better one.
Concurrent and Parallel Programming by Joe Armstrong.
Joe explains the difference between concurrency and parallelism to a five year old.
This is the type of stark clarity that I am seeking for topic map explanations.
At least the first ones someone sees. Time enough later for the gory details.
Suggestions welcome!
Lazy D3 on some astronomical data by simonraper.
From the post:
I can’t claim to be anything near an expert on D3 (a JavaScript library for data visualisation) but being both greedy and lazy I wondered if I could get some nice results with minimum effort. In any case the hardest thing about D3 for a novice to the world of web design seems to be getting started at all so perhaps this post will be useful for getting people up and running.
The images above and below are visualisations using D3 of a classification hierarchy for astronomical objects provided by the IVOA (International Virtual Observatory Alliance). I take no credit for the layout. The designs are taken straight from the D3 examples gallery but I will show you how I got the environment set up and my data into the graphs. The process should be replicable for any hierarchical dataset stored in a similar fashion.
Even better than the static images are various interactive versions such as the rotating Reingold–Tilford Tree, the collapsible dendrogram and collapsible indented tree . These were all created fairly easily by substituting the astronomical object data for the data in the original examples. (I say fairly easily as you need to get the hierarchy into the right format but more on that later.)
Easier to start with visualization of standard information structures and then move onto more exotic ones.
Data Points: Preview by Nathan Yau.
As you already know, Nathan is a rich source for interesting graphics and visualizations, some of which I have the good sense to point to.
What you may not know is that Nathan has a new book out: Data Points: Visualizations That Mean Something.
Not a book about coding to visualize data but rather:
Data Points is all about process from a non-programming point of view. Start with the data, really understand it, and then go from there. Data Points is about looking at your data from different perspectives and how it relates to real life. Then design accordingly.
That’s the hard part isn’t it?
Like the ongoing discussion here about modeling for topic maps.
Unless you understand the data, models and visualizations alike are going to be meaningless.
Check out Nathan’s new book to increase your chances of models and visualizations that mean something.
Visualizing Biological Data Using the SVGmap Browser by Casey Bergman.
From the post:
Early in 2012, Nuria Lopez-Bigas‘ Biomedical Genomics Group published a paper in Bioinformatics describing a very interesting tool for visualizing biological data in a spatial context called SVGmap. The basic idea behind SVGMap is (like most good ideas) quite straightforward – to plot numerical data on a pre-defined image to give biological context to the data in an easy-to-interpret visual form.
To do this, SVGmap takes as input an image in Scalable Vector Graphics (SVG) format where elements of the image are tagged with an identifier, plus a table of numerical data with values assigned to the same identifier as in the elements of the image. SVGMap then integrates these files using either a graphical user interface that runs in standard web browser or a command line interface application that runs in your terminal, allowing the user to display color-coded numerical data on the original image. The overall framework of SVGMap is shown below in an image taken from a post on the Biomedical Genomics Group blog.
We’ve been using SVGMap over the last year to visualize tissue-specific gene expression data in Drosophila melanogaster from the FlyAtlas project, which comes as one of the pre-configured “experiments” in the SVGMap web application.
More recently, we’ve been also using the source distribution of SVGMap to display information about the insertion preferences of transposable elements in a tissue-specific context, which as required installing and configuring a local instance of SVGMap and run it via the browser. The documentation for SVGMap is good enough to do this on your own, but it took a while for us to get a working instance the first time around. We ran into the same issues again the second time, so I thought I write up my notes for future reference and to help others get SVGMap up and running as fast as possible.
Topic map interfaces aren’t required to take a particular form.
A drawing of a fly could be topic map interface.
Useful for people studying flies, less useful (maybe) if you are mapping Lady Gaga discography.
What interface do you want to create for a topic map?
The sources for topic map patterns I mentioned yesterday use a variety of modeling languages:
Data Model Patterns: Conventions of Thought by David C. Hay. (Uses CASE*Method™ (Baker’s Notation))
Domain-Driven Design: Tackling Complexity in the Heart of Software by Eric Evans. (Uses UML (Unified Modeling Language))
Developing High Quality Data Models by Matthew West. (Uses EXPRESS (EXPRESS-G is for information models))
The TMDM and Kal’s Design Patterns both use UML notation.
Although constraints will be expressed in TMCL, visually it looks to me like UML should be the notation of choice.
Will require transposition from non-UML notation but seems worthwhile to have a uniform notation.
Any strong reasons to use another notation?
From the post:
This simple force-directed graph shows character co-occurence in Les Misérables. A physical simulation of charged particles and springs places related characters in closer proximity, while unrelated characters are farther apart. Layout algorithm inspired by Tim Dwyer and Thomas Jakobsen. Data based on character coappearence in Victor Hugo’s Les Misérables, compiled by Donald Knuth.
Display of graphs (read topic maps) need not be limited to complex applications.
Sam Hunting suggested this link.
Internet Topology – Massive and Amazing Graphs by Vincent Granville.
From the post:
I selected a few from this Google search. Which one is best? Re-usable in other contexts? What about videos showing growth over time, or more sophisticated graphs where link thickness represents “Internet highway” bandwidth or speed. And what about a video representing a simulated reflected DNS attack, rendering 10% of the Internet virtually dead, and showing how the attack spreads across the network?
Source: http://javiergs.com/?p=983 (a must read)
Be prepared to pump up the image size to get any recognizable text.
Truly impressive but I mention it to illustrate one of the practical problems in authoring topic maps.
The AT&:T graph is “massive and amazing” but it is finite. By its very nature it is finite.
Topic maps are finite as well, but their finiteness is by design. An entirely different problem.
In a topic map, every topic has the potential to have one or more associations with other topics, but it also has potential associations with subjects not yet represented by topics in the topic map.
That is like an encyclopedia author, you have to draw an arbitrary line around your topic map and say:
No associations with subjects not already in the map!
and,
No more new subjects in the map!
Which is quite different from a network typology, which no matter how vast, ends with with nodes at the end of each connection.
As a matter of design and authorship, you have to choose the limits on your topic map.
Where the limits of your topic map should be set will depend upon the use cases, requirements and resources that govern the authoring of your topic map.
When Presenting Your Data, Get to the Point Fast by Nancy Duarte.
From the post:
Projecting your data on slides puts you at an immediate disadvantage: When you’re giving a presentation, people can’t pull the numbers in for a closer look or take as much time to examine them as they can with a report or a white paper. That’s why you need to direct their attention. What do you want people to get from your data? What’s the message you want them to take away?
Data slides aren’t really about the data. They’re about the meaning of the data. And it’s up to you to make that meaning clear before you click away. Otherwise, the audience won’t process — let alone buy — your argument.
Nancy starts off with a fairly detailed table full of numbers, that is less complex than some topic map diagrams I have seen.
Moves onto the infamous pie chart* and then to a bar chart.
The lesson being to present information in a way it can be immediately comprehended by your audience.
Here’s a non-topic map illustration, explaining time dilation:
Here’s another explanation of time dilation:
Both “explain” time dilation but one to c-suite types and the other to techies.
Problem: C-suite types control the purse strings.
Question: What issues do c-suite types see that topic maps can address?
*Leland Wilkinson in The Grammar of Graphics, 2nd ed., writes of pie charts:
A pie chart is perhaps the most ubiquitous of modern graphics. It has been reviled by statisticians (unjustifiably) and adored by managers (unjustifiably).
So far (I am at chapter 3), Wilkinson doesn’t elaborate on his response to criticisms of pie charts by statisticians.
Not important for this discussion but one of those tidbits that livens up a classroom discussion.
I first saw this in a tweet by Gregory Piatetsky.
MapEquation.org by Daniel Edler and Martin Rosvall.
From the “about” page:
What do we do?
We develop mathematics, algorithms and software to simplify and highlight important structures in complex systems.
What are our goals?
To navigate and understand big data like we navigate and understand the real world by maps.
Suggest you start with the Apps.
Very impressive and has data available for loading.
You can also upload your own data.
Spend some time with Code and Publications as well.
I first saw this in a tweet by Chris@SocialTexture.
An interactive graphic depicting results of U.S. drone strikes in Pakistan since 2004.
In order to kill 47 targets, drone attacks have also killed 175 children, 535 civilians, 2349 “others.”
Highly effective graphic in a number of ways.
Try the Attacks, Victims, News and Info links in the upper left, or mouse over the individual attacks.
When your topic map presents information this effectively, you will be on the road to success!
PS: For policy wonks, only ten (10) innocents were required at Sodom and Gomorrah to avoid destruction.
Or to put it differently, would you murder on average more than three (3) children to kill on terrorist target? For President Obama, that answer is yes.
I first saw this at Nathan Yau’s Every known drone attack in Pakistan.
Data Mining and Visualization: Bed Bug Edition by Brooke Borel.
A very good example of data mining and visualization making a compelling case for conventional wisdom being wrong!
What I wonder about and what isn’t shown by the graphics, is what relationships, if any, existed between the authors of papers on bed bugs?
Were there communities, so to speak, of bed bug authors who cited each other? But not authors from parallel bed bug communities?
Not to mention the usual semantic gaps between authors from different traditions.
It sounds like Brooke is going to make a compelling read about all things, bed bugs!
The power of data mining!
BitcoinVisualizer by John Russell.
From the webpage:
Block Viewer visualizes the Bitcoin block chain by building an ownership network on top of the underlying transaction network and presents a web-enabled user interface to display the visualization results.
Great mapping exercise!
Imagine what could be done tracking all banking transfers.
Before you object that banking transfer monitoring would require a search warrant, remember that Richard Nixon could not be prosecuted for treason because the evidence was the result of an illegal wiretap.
Take this mapping as a reminder to use cash whenever possible.
Demo: http://www.blockviewer.com/#30203900
I first saw this in a tweet by Max De Marzi.
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