Tag Archives: language

Maps Preview

A short update today; we have been working on intelligent mapping for Brunel 1.0 (due in January) and since it’s a subject many people are interested in, we thought we’d put up a “work in progress” video showing how things are progressing. It’s a rough video, so you get to see my inability to type accurately as well as some rough transitions. Showing the video at full resolution is recommended.

Usual disclaimers apply: this is planned for v1.0 in January, we expect it to work as described, but no guarantees — Enjoy!

Brunel: Open Source Visualization Language

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BRUNEL is a high-level language that describes visualizations in terms of composable actions. It drives a visualization engine (d3) that performs the actual rendering and interactivity. It provides a language that is as simple as possible to describe a wide variety of potential charts, and to allow them to be used in Java, Javascript, python and R systems that want to deliver web-based interactive visualizations.


At the end of the article are a list of resources, but first, some examples. The dataset I am using for these is a set of data taken from BoardGameGeek which I processed to create a data set describing the top 2000 games listed as of Spring 2015. Each chart below is a fully interactive visualization running in its own frame. I’ve added the brunel description for each chart below each image as a caption, so you can go to the Builder anytime and copy the command into the edit box to try out new things.

data('sample:BGG Top 2000 Games.csv') bubble color(rating) size(voters) sort(rating) label(title) tooltip(title, #all) legends(none) style('* {font-size: 7pt}') top(rating:100)

This shows the top 100 games, with a tooltip view for details on the games. They are packed together in a layout where the location has no strong meaning
— the goal is to show as much data in as small a space as possible!
In the builder, you can change the number in top(rating:100) to show the top 1000, 2000 … or show the bottom 100. You could also add x(numplayers) to divide up the groups by recommended number of players

data('sample:BGG Top 2000 Games.csv') line x(published) y(categories) color(categories) size(voters:200) opacity(#selection) sort(categories) top(published:1900) sum(voters) legends(none) | data('sample:BGG Top 2000 Games.csv') bar y(voters) stack polar color(playerage) label(playerage) sum(voters) legends(none) at(15, 60, 40, 90) interaction(select:mouseover)

This example shows some live interactive features; hover over the pie chart to update the main chart. The main chart shows the number of people voting for games in different categories over time, and the pie chart shows the recommended minimum age to enjoy a game. So when you hover over ‘6’, for example, you can see that there have been no good sci-fi games for younger players in the last 10 years. Use the mouse to pan and zoom the chart (drag to pan, double-click to zoom).

data('sample:BGG Top 2000 Games.csv') treemap x(designer, mechanics) color(rating) size(#count) label(published) tooltip(#all, title) mean(rating) min(published) list(title:50) legends(none)

Head to the Builder Site to modify this. You could try:

  • change the list of fields in x(…) — reorder then or use fields like ‘numplayers’, ‘language’
  • remove the ‘legends(none)’ command to show a legend
  • change size to ‘voters’ — and add a ‘sum(voters)’ command to show the total number of voters rather than just counts for each treemap tile

Do you want to know more?

Follow links below; gallery and cookbook examples will take you to the Brunel Builder Site where you can create your own visualizations and grab some Javascript code to embed them in your web pages … which is exactly how I built the above examples!

Comics and Visualization

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Understanding Comics book cover; Scott McCloudComics and Visualization

Although this book is over a decade old now (and Scott has a number of later books that follow on from this one), this is still a highly valuable book to read, getting great review from famous artists as a fundamental resource for comic book writers. I read this from the perspective of a visualization expert, and found a number of interesting points in the book, especially the earlier sections. He defines comics as “juxtaposed pictorial and other images in deliberate sequence, intended to covey information and/or to produce an aesthetic response in the viewer (p.9)”, which, to my mind, allows many visualizations to fits his definition! The concept of small multiples, when presented in a “deliberate order” such as via a trellis display, fits particularly well into this definition, so I was encouraged to read on. Some highlights of the book, from my point of view:

  • The use of simpler icons / symbols to make depictions of reality more universal; that argument resonates more strongly with me than Tukey’s data-ink concept. I feel more convinced by the argument that additional detail is bad when it makes it harder for us to understand the high-level picture because it draws us too much into the physicality of the shapes being used.
  • McCloud presents a triangular space, the vertices of which are “reality”, “language” and “the picture plane” into which comic styles can be placed. I think there is also value in looking at various styles of visualization and seeing where they fit in. Treemaps, for example, have more “realistic” versions using cushions, while keeping the same structure. Scientific, geographic or fluid display visualizations are more realistic than, say, statistical graphics.
  • Less is More” applied to the number of intermediate representations used — this argues that for visualizations of, say, a process evolving over time, we should not simply slice at even times, but instead look for important features we want to show, and show fewer frames.
  • Lots of good stuff on how time is perceived when displayed at a sequence.
  • Can Emotions be Visible?” is the motivating question for chapter five — I would be very curious to see if we could apply his ideas to visualizations — maybe people like pie charts because they seem warm, serene and quiet, whereas a line chart with gridlines is rational, conservative and dynamic?

As an aside, I included a comic in my book on Visualizing Time, more as a whimsy than anything else, but I’m glad that I have at least a tenuous link with Scott McClouds’s highly recommended book! comics

Vega: A New Grammar-Based Specification for Visualizations

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I’m a big fan of using languages for visualization rather than canned chart types. I’ve been working with the Grammar of Graphics approach for a number of years within SPSS and now IBM, and my book “Visualizing Time” is composed 95% of Grammar-based visualizations. It’s pretty safe to say it’s my preferred approach.

Protovis (the forerunner of D3, to a great extent) was built on Grammar approach; Bostock and Heer’s 2009 article (on Heer’s site at http://hci.stanford.edu/jheer/files/2009-Protovis-InfoVis.pdf) gives a very good statement of the benefits of the Grammar-based approach as opposed to the “Chart Type” approach:

The main drawback of [the chart type] approach is that it requires a small, closed system. If the desired chart type is not supported, or the desired visual parameter is not exposed in the interface, no recourse is available to the user and either the visualization design must be compromised or another tool adopted. Given the high cost of switching tools, and the iterative nature of visualization design, frequent compromise is likely.

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From the Vaults: How to Speak Visualization

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In English, we use many different words to describe the same basic objects. In one survey, researchers Dieth and Orton explored which words were used for the place where a farmer might keep his cow, depending on where the speaker resided in England. The results include words like byreshipponmistallcow-stablecow-housecow-shedneat-house or beast-house. We see the same situation in visualization, where a two-dimensional chart with data displayed as a collection of points, using one variable for the horizontal axis and one for the vertical, is variously called ascatterplot, a scatter diagram, a scatter graph, a 2D dotplot or even a star field.

There have been a number of attempts to form taxonomies, or categorizations, of visualizations. Most software packages for creating graphics, such as Microsoft Excel focus on the type of graphical element used to display the data and then sub-classify from that. This has one immediate problem in that plots with multiple elements are hard to classify (should we classify a chart with a bars and points as a bar chart, with point additions, or instead classify it as a point char, with bars added?). Other authors have started with the dimensionality of the data (one-dimensional, two-dimensional, etc.) and used that as a basic classification criterion, but that has similar problems.

Visualizations are too numerous, too diverse and too exciting to fit well into a taxonomy that divides and subdivides. In contrast to the evolution of animals and plants, which did occur essentially in a tree-like manner, with branches splitting and sub-splitting, information visualization techniques have been invented more by a compositional approach. We take a polar coordinate system, combine it with bars, and achieve a Rose diagram. We put a network in 3D. We addtexture, shape and size mappings to all the above. We split it into panels. This is why a traditional taxonomy of information visualization is doomed to be unsatisfying. It is based on a false analogy with biology and denies the basic process by which visualizations have been created: composition.

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