Archive for the ‘Visualization’ Category

A Taxonomic Map of Philosophy

Wednesday, August 10th, 2016

A Taxonomic Map of Philosophy by Justin W..

From the post:

Some people go to PhilPapers, get the information they need, and then just go. Not Valentin Lageard, a graduate student in philosophy at Université Paris-Sorbonne. The Categories page at the site caught his eye. He says:

The completeness of their taxonomy was striking and I thought : “Could it be possible to map this taxonomy ?”. I decided it was a nice idea and i started to work on it.

The first step was to select the kind of graph and since their taxonomy includes a hierarchy permitting to sub-categories to be children of more than one parent categories, I selected a concentric circles graph.

Because I’m a python user, I choosed Networkx for the graph part and BeautifulSoup for the scraping part. Furthermore, since Philpapers gives the articles number for each category, I decided to add this data to my graph.

After some configurations of the display, I finally reached my goal: a map of the taxonomy of philosophy. And it was quite beautiful.


[See update, below, for the more detailed 5-layer version]

NEW UPDATE: Here is the 5-layer version. You can view it in more detail here (open it in a new tab or window for best results).

Impressive but is it informative?

In order to read the edge, I had to magnify the graph several times its original size, which then meant navigation was problematic.

Despite the beauty of the image, a graph file that enables filtering of nodes and edges would be far more useful for exploring the categories as well as the articles therein.

For example:


If you are wondering what falls under “whiteness,” apparently studies of “whiteness” in the racial sense but also authors whose surnames are “White.”

As the top of the categories page for whiteness advises:

This category needs an editor. We encourage you to help if you are qualified.

Caution: You may encounter resources at PhilPapers that render you unable to repeat commonly held opinions. Read at your own risk.


Node XL (641 Pins)

Friday, August 5th, 2016

Node XL

Just a quick sample:


That’s only a sample, another 629 await your viewing (perhaps more by the time you read this post).

I have a Pineterest account but this is the first set of pins I have chosen to follow.

Suggestions of similar visualization boards at Pinterest?


OnionRunner, ElasticSearch & Maltego

Wednesday, August 3rd, 2016

OnionRunner, ElasticSearch & Maltego by Adam Maxwell.

From the post:

Last week Justin Seitz over at released OnionRunner which is basically a python wrapper (because Python is awesome) for the OnionScan tool (

At the bottom of Justin’s blog post he wrote this:

For bonus points you can also push those JSON files into Elasticsearch (or modify to do so on the fly) and analyze the results using Kibana!

Always being up for a challenge I’ve done just that. The script outputs each scan result as a json file, you have two options for loading this into ElasticSearch. You can either load your results after you’ve run a scan or you can load them into ElasticSearch as a scan runs. Now this might sound scary but it’s not, lets tackle each option separately.

A great enhancement to Justin’s original OnionRunner!

You will need a version of Maltego to perform the visualization as described. Not a bad idea to become familiar with Maltego in general.

Data is just data, until it is analyzed.


Interactive 3D Clusters of all 721 Pokémon Using Spark and Plotly

Wednesday, August 3rd, 2016

Interactive 3D Clusters of all 721 Pokémon Using Spark and Plotly by Max Woolf.


My screen capture falls far short of doing justice to the 3D image, not to mention it isn’t interactive. See Max’s post if you really want to appreciate it.

From the post:

There has been a lot of talk lately about Pokémon due to the runaway success of Pokémon GO (I myself am Trainer Level 18 and on Team Valor). Players revel in the nostalgia of 1996 by now having the ability catching the original 151 Pokémon in real life.

However, while players most-fondly remember the first generation, Pokémon is currently on its sixth generation, with the seventh generation beginning later this year with Pokémon Sun and Moon. As of now, there are 721 total Pokémon in the Pokédex, from Bulbasaur to Volcanion, not counting alternate Forms of several Pokémon such as Mega Evolutions.

In the meantime, I’ve seen a few interesting data visualizations which capitalize on the frenzy. A highly-upvoted post on the Reddit subreddit /r/dataisbeautiful by /u/nvvknvvk charts the Height vs. Weight of the original 151 Pokémon. Anh Le of Duke University posted a cluster analysis of the original 151 Pokémon using principal component analysis (PCA), by compressing the 6 primary Pokémon stats into 2 dimensions.

However, those visualizations think too small, and only on a small subset of Pokémon. Why not capture every single aspect of every Pokémon and violently crush that data into three dimensions?

If you need encouragement to explore the recent release of Spark 2.0, Max’s post that in abundance!

Caveat: Pokémon is popular outside of geek/IT circles. Familiarity with Pokémon may result in social interaction with others and/or interest in Pokémon. You have been warned.

Whose Chose Trump and Clinton?

Monday, August 1st, 2016

If you have been wondering who is responsible for choosing Trump and Clinton as the presidential nominees in 2016, you will find Only 9% of America Chose Trump and Clinton as the Nominees by Alicia Parlapiano and Adam Pearce quite interesting.

Using a fixed grid on the left hand side of the page that represents 324 million Americans, 1 square = 1 million people, the article inscribes boundaries on the grid for a series of factual statements.

For example, the first statement after the grid reads:

103 million of them are children, noncitizens or ineligible felons, and they do not have the right to vote.

For that statement, the grid displays:


An excellent demonstration that effective visualization requires a lot of thought and not necessarily graphics that jump and buzz with every movement of the mouse.

Successive statements reduce the area of people who voted in the primaries and even further by who voted for Trump or Clinton.

Eventually you are left with the 9% who chose the current nominees.

To be safe, you need 5% of the voting population to secure the nomination. Check the voting rolls for who votes in primaries and pay them directly. Cheaper than media campaigns and has the added advantage of not annoying the rest of the electorate with your ads.

If that sounds “undemocratic,” tell me what definition of democracy you are using where 9% of the population chooses the candidates and a little more than 30% will choose the winner?

What That Election Probability Means
[500 Simulated Clinton-Trump Elections]

Thursday, July 28th, 2016

What That Election Probability Means by Nathan Yau.

From the post:

We now have our presidential candidates, and for the next few months you get to hear about the changing probability of Hillary Clinton and Donald Trump winning the election. As of this writing, the Upshot estimates a 68% probability for Clinton and 32% for Donald Trump. FiveThirtyEight estimates 52% and 48% for Clinton and Trump, respectively. Forecasts are kind of all over the place this far out from November. Plus, the numbers aren’t especially accurate post-convention.

But the probabilities will start to converge and grow more significant.

So what does it mean when Clinton has a 68% chance of becoming president? What if there were a 90% chance that Trump wins?

Some interpret a high percentage as a landslide, which often isn’t the case with these election forecasts, and it certainly doesn’t mean the candidate with a low chance will lose. If this were the case, the Cleveland Cavaliers would not have beaten the Golden State Warriors, and I would not be sitting here hating basketball.

Fiddle with the probabilities in the graphic below to see what I mean.

As always, visualizations from Nathan are a joy to view and valuable in practice.

You need to run it several times but here’s the result I got with “FiveThirtyEight estimates 52% and 48% for Clinton and Trump, respectively.”


You have to wonder what a similar simulation for breach/no-breach would look like for your enterprise?

Would that be an effective marketing tool for cybersecurity?

Perhaps not if you are putting insecure code on top of insecure code but there are other solutions.

For example, having state legislatures prohibit the operation of escape from liability clauses in EULAs.

Assuming someone who has read one in sufficient detail to draft legislation. 😉

That could be an interesting data project. Anyone have a pointer to a collection of EULAs?


Tuesday, July 19th, 2016

JuxtaposeJS Frame comparisons. Easy to make. Seamless to publish. (Northwestern University Knight Lab, Alex Duner.)

From the webpage:

JuxtaposeJS helps storytellers compare two pieces of similar media, including photos, and GIFs. It’s ideal for highlighting then/now stories that explain slow changes over time (growth of a city skyline, regrowth of a forest, etc.) or before/after stories that show the impact of single dramatic events (natural disasters, protests, wars, etc.).

It is free, easy to use, and works on all devices. All you need to get started are links to the images you’d like to compare.

Perhaps an unexpected use, but if you are stumped on a “find all the differences” pair of photos, split them and create a slider!

This isn’t a hard one but for example use these two images:

As the slider moves over a change between the two images, your eye will be drawn towards the motion. (Visit Cranium Crunches Blog for more puzzles and images like this one.)

On a more serious note, imagine the use of this app for comparison of aerial imagery (satellite, plane, drone) and using the human eye to spot changes in images. Could be more timely than streaming video for automated analysis.

Or put differently, it isn’t the person with the most intell, eventually, that wins, but the person with the best intell, in time.

Colorblind-Friendly Graphics

Tuesday, July 19th, 2016

Three tools to help you make colorblind-friendly graphics by Alex Duner.

From the post:

I am one of the 8% of men of Northern European descent who suffers from red-green colorblindness. Specifically, I have a mild case of protanopia (also called protanomaly), which means that my eyes lack a sufficient number of retinal cones to accurately see red wavelengths. To me some purples appear closer to blue; some oranges and light greens appear closer to yellow; dark greens and brown are sometimes indistinguishable.

Most of the time this has little impact on my day-to-day life, but as a news consumer and designer I often find myself struggling to read certain visualizations because my eyes just can’t distinguish the color scheme. (If you’re not colorblind and are interested in experiencing it, check out Dan Kaminsky’s iPhone app DanKam which uses augmented reality to let you experience the world through different color visions.)

As information architects, data visualizers and web designers, we need to make our work accessible to as many people as possible, which includes people with colorblindness.

Alex is writing from a journalism perspective but accessibility is a concern for any information delivery system.

A pair of rather remarkable tools, Vischeck, simulates colorblindness on your images and Daltonize, “corrects” images for colorblind users will be useful in vetting your graphics. Both are available at: Plugins for Photoshop (Win/Mac/ImageJ).

Loren Petrich has a collection of resources, including filters for GIMP to simulate colorblindness at: Color-Blindness Simulators.

D3 4.0.0

Tuesday, June 28th, 2016

Mike Bostock tweets:

After 12+ months and ~4,878 commits, I am excited to announce the release of D3 4.0! … #d3js

After looking at the highlights page on Github, I couldn’t in good conscience omit any of it:

D3 is now modular, composed of many small libraries that you can also use independently. Each library has its own repo and release cycle for faster development. The modular approach also improves the process for custom bundles and plugins.

There are a lot of improvements in 4.0: there were about as many commits in 4.0 as in all prior versions of D3. Some changes make D3 easier to learn and use, such as immutable selections. But there are lots of new features, too! These are covered in detail in the release notes; here are a few highlights.

Colors, Interpolators and Scales

Shapes and Layouts

Selections, Transitions, Easings and Timers

Even More!

Don’t complain to me that you are bored over the Fourth of July weekend in the United States.

Downloads:, Source code (zip), Source code (tar.gz).

Tufte-inspired LaTeX (handouts, papers, and books)

Monday, June 20th, 2016

Tufte-LaTeX – A Tufte-inspired LaTeX class for producing handouts, papers, and books.

From the webpage:

As discussed in the Book Design thread of Edward Tufte’s Ask E.T Forum, this site is home to LaTeX classes for producing handouts and books according to the style of Edward R. Tufte and Richard Feynman.

Download the latest release, browse the source, join the mailing list, and/or submit patches. Contributors are welcome to help polish these classes!

Some examples of the Tufte-LaTeX classes in action:

  • Some papers by Jason Catena using the handout class
  • A handout for a math club lecture on volumes of n-dimensional spheres by Marty Weissman
  • A draft copy of a book written by Marty Weissman using the new Tufte-book class
  • An example handout (source) using XeLaTeX with the bidi class option for the ancient Hebrew by Kirk Lowery

Caution: A Tufte-inspired LaTeX class is no substitute for professional design advice and assistance. It will help you do “better,” for some definition of “better,” but professional design is in a class of its own.

If you are interested in TeX/LaTeX tips, follow: TexTips. One of several excellent Twitter feeds by John D. Cook.

Volumetric Data Analysis – yt

Friday, June 17th, 2016

One of those rotating homepages:

Volumetric Data Analysis – yt

yt is a python package for analyzing and visualizing volumetric, multi-resolution data from astrophysical simulations, radio telescopes, and a burgeoning interdisciplinary community.

Quantitative Analysis and Visualization

yt is more than a visualization package: it is a tool to seamlessly handle simulation output files to make analysis simple. yt can easily knit together volumetric data to investigate phase-space distributions, averages, line integrals, streamline queries, region selection, halo finding, contour identification, surface extraction and more.

Many formats, one language

yt aims to provide a simple uniform way of handling volumetric data, regardless of where it is generated. yt currently supports FLASH, Enzo, Boxlib, Athena, arbitrary volumes, Gadget, Tipsy, ART, RAMSES and MOAB. If your data isn’t already supported, why not add it?

From the non-rotating part of the homepage:

To get started using yt to explore data, we provide resources including documentation, workshop material, and even a fully-executable quick start guide demonstrating many of yt’s capabilities.

But if you just want to dive in and start using yt, we have a long list of recipes demonstrating how to do various tasks in yt. We even have sample datasets from all of our supported codes on which you can test these recipes. While yt should just work with your data, here are some instructions on loading in datasets from our supported codes and formats.

Professional astronomical data and tools like yt put exploration of the universe at your fingertips!


Visualizing your Titan graph database:…

Friday, June 17th, 2016

Visualizing your Titan graph database: An update by Marco Liberati.

From the post:

Last summer, we wrote a blog with our five simple steps to visualizing your Titan graph database with KeyLines. Since then TinkerPop has emerged from the Apache Incubator program with TinkerPop3, and the Titan team have released v1.0 of their graph database:

  • TinkerPop3 is the latest major reincarnation of the graph proje­­­ct, pulling together the multiple ventures into a single united ecosystem.
  • Titan 1.0 is the first stable release of the Titan graph database, based on the TinkerPop3 stack.

We thought it was about time we updated our five-step process, so here’s:

Not exactly five (5) steps because you have to acquire a KeyLines trial key, etc.

A great endorsement of much improved installation process for TinkerPop3 and Titan 1.0.


For The Artistically Challenged (that includes me)

Thursday, May 12th, 2016


If you are looking for animated gifs for a blog post, presentation, etc., give GIPHY a try.

Now that I have found it, I’m likely to spend too much time looking for the perfect animated GIF.


Countries Wanting UK to Stay in EU [Bad Graphics]

Thursday, May 5th, 2016


Before you read The map showing which countries want the UK to stay in the EU or my comment below, a question for you:

Do countries shaded in lighter colors support the UK remaining in the EU?

Simple enough question.

Unfortunately you are looking at one of the worst representations of sentiment I have seen in a long time.

From the post:

The indy100 have created the following graphic based on the data. In the map, the darker the shade of blue, the more support there is in that country for the UK to remain in the EU. The scores are calculated by subtracting the percentage of people who want Britain to leave, from those who want Britain to remain.

That last line:

The scores are calculated by subtracting the percentage of people who want Britain to leave, from those who want Britain to remain.

is what results in the odd visualization.

A chart later in the post reports that support for UK leaving the EU is only 18% in France, which would be hard to guess from the “32” shown on the map.

The map shows the gap between two positions, one for the UK to stay and the other for it to leave, and the shading represents the distance between staying and supporting positions.

That is if public opinion were 50% to stay in the EU and 50% to leave the EU, that county would be colored clear with a score of 0.

Reporting support and/or opposition percentages with coloration based on those percentages would be far clearer.

MATISSE – Solar System Exploration

Saturday, April 30th, 2016

MATISSE: A novel tool to access, visualize and analyse data from planetary exploration missions by Angelo Zinzi, Maria Teresa Capria, Ernesto Palomba, Paolo Giommi, Lucio Angelo Antonelli.


The increasing number and complexity of planetary exploration space missions require new tools to access, visualize and analyse data to improve their scientific return.

ASI Science Data Center (ASDC) addresses this request with the web-tool MATISSE (Multi-purpose Advanced Tool for the Instruments of the Solar System Exploration), allowing the visualization of single observation or real-time computed high-order products, directly projected on the three-dimensional model of the selected target body.

Using MATISSE it will be no longer needed to download huge quantity of data or to write down a specific code for every instrument analysed, greatly encouraging studies based on joint analysis of different datasets.

In addition the extremely high-resolution output, to be used offline with a Python-based free software, together with the files to be read with specific GIS software, makes it a valuable tool to further process the data at the best spatial accuracy available.

MATISSE modular structure permits addition of new missions or tasks and, thanks to dedicated future developments, it would be possible to make it compliant to the Planetary Virtual Observatory standards currently under definition. In this context the recent development of an interface to the NASA ODE REST API by which it is possible to access to public repositories is set.

Continuing a long tradition of making big data and tools for processing big data freely available online (hint, hint, Panama Papers hoarders), this paper describes MATISSE (Multi-purpose Advanced Tool for the Instruments for the Solar System Exploration), which you can find online at:

Data currently available:

MATISSE currently ingests both public and proprietary data from 4 missions (ESA Rosetta, NASA Dawn, Chinese Chang’e-1 and Chang’e-2), 4 targets (4 Vesta, 21 Lutetia, 67P ChuryumovGerasimenko, the Moon) and 6 instruments (GIADA, OSIRIS, VIRTIS-M, all onboard Rosetta, VIR onboard Dawn, elemental abundance maps from Gamma Ray Spectrometer, Digital Elevation Models by Laser Altimeter and Digital Ortophoto by CCD Camera from Chang’e-1 and Chang’e-2).

If those names don’t sound familiar (links to mission pages):

4 Vesta – asteriod (NASA)

21 Lutetia – asteroid (ESA)

67P ChuryumovGerasimenko – comet (ESA)

the Moon – As in “our” moon.

You can do professional level research on extra-worldly data, but with worldly data (Panama Papers), not so much. Don’t be deceived by the forthcoming May 9th dribble of corporate data from the Panama Papers. Without the details contained in the documents, it’s little more than a suspect’s list.

SVGs beyond mere shapes

Tuesday, April 26th, 2016

SVGs beyond mere shapes by Nadieh Bremer

From the post:

I was exhilarated (and honored) to have my talk accepted for OpenVis 2016. Yesterday April 25th, 2016, I was on the stage of the Simons IMAX Theatre in Boston’s New England Aquarium to inspire the audience with some dataviz eye candy. My talk was titled SVGs beyond mere shapes:

SVG can do much more than create nice shapes and paths. In my talk I discuss several techniques and demonstrate how to implement them in D3: from dynamic gradients based on data, to SVG filters, to creating glow, gooey, and fuzzy effects that brighten up any visual.

My eventual goal was to give people a whole bunch of effective or fun examples but to also show them that, even if I focus on a subject as narrow as SVG gradient and filters, if you try to experiment and use things in an unconventional manner you can create some very interesting results. I hope I’ve managed to inspire the audience to show a dedication to the details, to go beyond the norm, so they have to make as few concessions to the computer as possible to recreate the image that they have in their mind.

I’ve received so many wonderful reactions, it was really an amazing experience and well worth the time invested and the nerves I’ve had building up inside of me since hearing I’d been accepted last November 🙂

Are you ready to take SVG beyond shapes?

The start of a series so check back often and/or follow @NadiehBremer.

Create a Heatmap in Excel

Saturday, April 23rd, 2016

Create a Heatmap in Excel by Jonathan Schwabish.

From the post:

Last week, I showed you how to use Excel’s Conditional Formatting menu to add cell formats to highlight specific data values. Here, I’ll show you how to easily use the Color Scales options in that menu to create a Heatmap.

Simply put, a heatmap is a table where the data are visualized using color. They pop up fairly regularly these days, sometimes showing the actual data values and sometimes not, like these two I pulled from FlowingData.

In addition to this post, there are a number of other Excel-centric visualization posts, podcasts and other high quality materials.

Even if you aren’t sold on Excel, you will learn a lot about visualization here.


Doom as a tool for system administration (1999) – Pen Testing?

Saturday, April 23rd, 2016

Doom as a tool for system administration by Dennis Chao.

From the webpage:

As I was listening to Anil talk about daemons spawning processes and sysadmins killing them, I thought, “What a great user interface!” Imagine running around with a shotgun blowing away your daemons and processes, never needing to type kill -9 again.

In Doom: The Aftermath you will find some later references, the most recent being from 2004.

You will have better luck at the ACM Digital library entry for Doom as an interface for process management that lists 29 subsequent papers citing Chao’s work on Doom. Latest is 2015.

If system administration with a Doom interface sounds cool, imagine a Doom hacking interface.

I can drive a car but I don’t set the timing, adjust the fuel injection, program the exhaust controls to beat inspectors, etc.

A higher level of abstraction for tools carries a cost but advantages as well.

Imagine cadres of junior high/high school students competing in pen testing contests.

Learning a marketable skill and helping cash-strapped IT departments with security testing.

Isn’t that a win-win situation?

Where You Look – Determines What You See

Friday, April 22nd, 2016

Mapping an audience-centric World Wide Web: A departure from hyperlink analysis by Harsh Taneja.


This article argues that maps of the Web’s structure based solely on technical infrastructure such as hyperlinks may bear little resemblance to maps based on Web usage, as cultural factors drive the latter to a larger extent. To test this thesis, the study constructs two network maps of 1000 globally most popular Web domains, one based on hyperlinks and the other using an “audience-centric” approach with ties based on shared audience traffic between these domains. Analyses of the two networks reveal that unlike the centralized structure of the hyperlink network with few dominant “core” Websites, the audience network is more decentralized and clustered to a larger extent along geo-linguistic lines.

Apologies but the article is behind a firewall.

A good example of what you look for determining your results. And an example of how firewalls prevent meaningful discussion of such research.

Unless you know of a site like of course.


PS: This is what an audience-centric web mapping looks like:


Impressive work!

Cosmic Web

Thursday, April 21st, 2016

Cosmic Web

From the webpage:

Immerse yourself in a network of 24,000 galaxies with more than 100,000 connections. By selecting a model, panning and zooming, and filtering different, you can delve into three distinct models of the cosmic web.

Just one shot from the gallery:


I’m not sure if the display is accurate enough for inter-galactic navigation but it is certainly going to give you ideas about more effective visualization.


Visualizing Data Loss From Search

Thursday, April 14th, 2016

I used searches for “duplicate detection” (3,854) and “coreference resolution” (3290) in “Ironically, Entity Resolution has many duplicate names” [Data Loss] to illustrate potential data loss in searches.

Here is a rough visualization of the information loss if you use only one of those terms:


If you search for “duplicate detection,” you miss all the articles shaded in blue.

If you search for “coreference resolution,” you miss all the articles shaded in yellow.

Suggestions for improving this visualization?

It is a visualization that could be performed on client’s data, using their search engine/database.

In order to identify the data loss they are suffering now from search across departments.

With the caveat that not all data loss is bad and/or worth avoiding.

Imaginary example (so far): What if you could demonstrate no overlapping of terminology for two vendors for the United States Army and the Air Force. That is no query terms for one returned useful results for the other.

That is a starting point for evaluating the use of topic maps.

While the divergence in terminologies is a given, the next question is: What is the downside to that divergence? What capability is lost due to that divergence?

Assuming you can identify such a capacity, the next question is to evaluate the cost of reducing and/or eliminating that divergence versus the claimed benefit.

I assume the most relevant terms are going to be those internal to customers and/or potential customers.

Interest in working this up into a client prospecting/topic map marketing tool?

Separately I want to note my discovery (you probably already knew about it) of VennDIS: a JavaFX-based Venn and Euler diagram software to generate publication quality figures. Download here. (Apologies, the publication itself if firewalled.)

The export defaults to 800 x 800 resolution. If you need something smaller, edit the resulting image in Gimp.

It’s a testimony to the software that I was able to produce a useful image in less than a day. Kudos to the software!

NSA Grade – Network Visualization with Gephi

Sunday, April 10th, 2016

Network Visualization with Gephi by Katya Ognyanova.

It’s not possible to cover Gephi in sixteen (16) pages but you will wear out more than one printed copy of these sixteen (16) pages as you become experienced with Gephi.

This version is from a Gephi workshop at Sunbelt 2016.

Katya‘s homepage offers a wealth of network visualization posts and extensive use of R.

Follow her at @Ognyanova.

PS: Gephi equals or exceeds visualization capabilities in use by the NSA, depending upon your skill as an analyst and the quality of the available data.

Python Code + Data + Visualization (Little to No Prose)

Tuesday, April 5th, 2016

Up and Down the Python Data and Web Visualization Stack

Using the “USGS dataset listing every wind turbine in the United States:” this notebook walks you through data analysis and visualization with only code and visualizations.

That’s it.

Aside from very few comments, there is no prose in this notebook at all.

You will either hate it or be rushing off to do a similar notebook on a topic of interest to you.

Looking forward to seeing the results of those choices!

Wind/Weather Maps

Sunday, April 3rd, 2016

A Twitter thread started by Data Science Renee mentioned these three wind map resources:

Wind Map


EarthWindMap Select “earth” for a menu of settings and controls.


Windyty Perhaps the most full featured of the three wind maps. Numerous controls that are not captured in the screenshot. Including webcams.


Suggestions of other real time visualizations of weather data?

Leaving you to answer the question:

What other data would you tie to weather conditions/locations? Perhaps more importantly, why?

WordsEye [Subject Identity Properties]

Tuesday, March 29th, 2016


A site that enables you to “type a picture.” What? To illustrate:

A [mod] ox is a couple of feet in front of the [hay] wall. It is cloudy. The ground is shiny grass. The huge hamburger is on the ox. An enormous gold chicken is behind the wall…

Results in:


The site is in a close beta test but you can apply for an account.

I mention “subject identity properties” in the title because the words we use to identify subjects, are properties of subjects, just like any other properties we attribute to them.

Unfortunately, words are viewed by different people as identifying different subjects and the different words as identifying the same subjects.

The WordsEye technology can illustrates the fragility of using a single word to identify a subject of conversation.

Or that multiple identifications have the same subject, with side by side images that converge on a common image.

Imagine that in conjunction with 3-D molecular images for example.

I first saw this in a tweet by Alyona Medelyan.

Nebula Bliss

Monday, March 28th, 2016

Nebula Bliss

Visually impressive 3-D modeling of six different nebula.

I did not tag this with astroinformatics as it is a highly imaginative but non-scientific visualization.



The image is a screen capture from the Butterfly Nebula visualization.

Kodály, String Quartet No. 1, 3rd movement

Sunday, March 27th, 2016

From the webpage:

Scherzo (3rd movement) of Zoltán Kodály’s first string quartet, performed by the Alexander String Quartet, accompanied by a graphical score.


Q: Where can I get this recording?
A: You complete album is available here:

Q: Who are the performers?
A: The Alexander String Quartet comprises Zakarias Grafilo and Frederick Lifsitz, violins, Paul Yarbrough, viola, and Sandy Wilson, violoncello. You can learn more about the group here:

Q: What do the colors mean?
A: Each pitch class (C, C-sharp, D, etc.) has its own color, arranged according to the “circle of fifths” so that changes in tonality can be seen; this system is described in more detail here:

In the first version of this video …… … the colors are applied to a conventional bar-graph score.

In the second version …… … the “staff” is the 12 pitch classes, arranged in circle-of-fifths order.

Q: Could you please do a video of _______?
A: Please read this:

If you want to see a data visualization with 26+ million views on YouTube, check out Stephen Malinowski’s YouTube channel.

Don’t miss Stephen Malinowski’s website. Select “site map” for a better idea of what you will find at the site.

Exoplanet Visualization

Wednesday, March 9th, 2016

Exoplanet Visualization

You can consider this remarkable eye-candy and/or as a challenge to your visualization skills.

Either way, you owe it to yourself to see this display of exoplanet data.

Quite remarkable.

Pay close attention because there are more planets than the ones near the center that catch your eye.

I first saw this in a tweet by MapD.

So You Want To Visualize Data? [Nathan Yau’s Toolbox]

Tuesday, March 8th, 2016

What I Use to Visualize Data by Nathan Yau.

From the post:

“What tool should I learn? What’s the best?” I hesitate to answer, because I use what works best for me, which isn’t necessarily the best for someone else or the “best” overall.

If you’re familiar with a software set already, it might be better to work off of what you know, because if you can draw shapes based on numbers, you can visualize data. After all, this guy uses Excel to paint scenery.

It’s much more important to just get started already. Work with as much data as you can.

Nevertheless, this is the set of tools I use in 2016, which converged to a handful of things over the years. It looks different from 2009, and will probably look different in 2020. I break it down by place in my workflow.

As Nathan says up front, these may not be the best tools for you but it is a great starting place. Add and subtract from this set as you develop your own workflow and habits.


PS: Nathan Yau tweeted a few hours later: “Forgot to include this:”


Network Measures of the United States Code

Saturday, March 5th, 2016

Network Measures of the United States Code by Alexander Lyte, Dr. David Slater, Shaun Michel.


The U.S. Code represents the codification of the laws of the United States. While it is a well-organized and curated corpus of documents, the legal text remains nearly impenetrable for non-lawyers. In this paper, we treat the U.S. Code as a citation network and explore its complexity using traditional network metrics. We find interesting topical patterns emerge from the citation structure and begin to interpret network metrics in the context of the legal corpus. This approach has potential for determining policy dependency and robustness, as well as modeling of future policies.​

The citation network is quite impressive:


I have inquired about an interactive version of the network but no response as of yet.