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U.S. Congressional District choropleths made easy

2014 October 20
by Ben Jones

At the Tableau Public blog, we’ve chosen to focus on political data visualizations during the month of October, since election day in the United States is right around the corner. We’re using the hashtag #VizTheVote to collect our posts and to encourage others to share their thoughts on an aspect of our world that is rich with data (or at least should be) and is also ripe for visualization.

In this blog post, I’m going to show you how to take advantage of a seldom-used mapping feature in Tableau Public 8.2: built-in U.S. Congressional District shapes. First, let’s look at a viz showing the 113th House of Representatives by either age or tenure, then I’ll go into detail about how it was made:

Step 1: Get the Data
If you look at the Wikipedia page showing the “List of current members of the United States House of Representatives by age“, it looks like this:


I copy and pasted this table into Excel and added a column indicating which party each politician belongs to. Step 1 done.

Step 2: Structure the Data
This table is great, but notice the first column – “District”. It combines the state and the congressional district number into one geographic field. In order for Tableau to recognize the congressional district and apply the correct shape, these two fields need to be separated into a “District” column and a “State” column. I did this in Excel using “Text to Columns”. Here’s an image of the final Excel spreadsheet I used to build the viz:


Just the number itself suffices to automatically draw congressional district shapes in Tableau, but there are a few other variations that will also work, as shown in the Geographic Role table below:


Step 3: Visualize the Data
Connect a new Tableau workbook to this spreadsheet and make sure the geographic role for District is set to “Congressional District” (right click on the District pill). Then, do the following:

  1. Double click on “Latitude (generated)” (goes to Rows) and “Longitude (generated)” (goes to Columns)
  2. Drag both “District” and “State” to the Detail shelf
  3. Change the Marks type from Automatic to Filled Map
  4. Drag “Age” to the Color shelf (Age is a calculated field calculating the DATEDIFF between today and the age of birth)

Here’s an image of the map that shows the age of each member of the House of Representatives, with darker colors indicating older reps:


Step 4: Create the Dashboard
This last part is Tableau 101 and a maybe a little bit of 201. I won’t go into detail about how to create the additional Sheets and combine them on a single Dashboard, as I go into detail on how to do this in Chapters 13 and 14 of my book Communicating Data with Tableau.

Interesting to notice a few things: that congressional districts are split on a coastal vs land-locked basis, that some members of the House are quite old and have hung on to their seat upwards of 5 or 6 decades (John Dingell, Michigan 12). Mostly, though, I hope you notice that creating choropleths of congressional districts in Tableau is quite easy.

For more data at a Congressional District level, check out the U.S. Census Bureau “American Fact Finder” table, or use this CSV I downloaded from the Census site that is ready to import directly into Tableau.

Thanks for stopping by,

How to Make Small Multiple Maps in Tableau

2014 September 6
by Ben Jones

I’m a big fan of small multiples in data viz, and I’m somewhat of a “Maphead” as well. Naturally, combining the two together results in a visualization that I’d vouch for almost any time. Kyle Kim of the LA Times just published a stunning series of 192 maps showing drought levels in California by week, going back to January 4, 2011. Small Multiple Maps can take up a lot of space, but they’re very effective at showing change over both time and geography. Judge for yourself.

I look at a lot of Tableau Public visualizations, but I don’t see a lot of “small multiple maps” out there. It’s not that they don’t exist, they’re just rare. They’re actually pretty easy to make, so I thought I’d show you one and walk you through how to create one for yourself. Here’s a small multiples map showing FEMA declared disasters, by county, since 1953:

If you want to follow this brief tutorial, first download this Excel file of FEMA disasters.

How to Make a Small Multiples Map in Tableau

There are at least two different ways you can create small multiples maps in Tableau. One way is to create a bunch of individual maps as Sheets and drag and drop them all onto a single Dashboard. The other way is to create a single Sheet with a grid of small maps. This blog post covers the second method, which has the advantage that the “OpenStreetMaps” attribution only occurs once in the bottom left corner, instead of once for each multiple.

Step 1: Create a basic map

I started by creating a basic choropleth map of continental US counties. I double clicked on the county data field (Declared County/Area) and then dragged “Number of Records” to the Color Shelf. I filtered out the states and territories not in the “lower 48″, I changed the Color to red, set country shape borders to “None”, and edited the Map Options to only show the coastline and borders:

Step 2: Create a “Row Number” and “Column Number” Calculated Field

There are 22 different “Incident Types” (so, plenty of material for Hollywood), but for this project I wanted to create a 3X3 grid, so I needed to identify the top 9 Incident Types. From a simple bar chart showing counts of Incident Type over the full date range, I found that (in descending order of frequency) Severe Storm(s), Flood, Hurricane, Snow, Fire, Severe Ice Storm, Tornado, Drought and Coastal Storm were the ones to include.

I wanted to put each of the 9 top Incident Types in its own box on the 3X3 grid starting with the least frequent type of the 9 (Coastal Storm) in the top left and working my way down to the most frequent (Severe Storm) in the bottom right. Each of the nine then would have a Row Number (1-3) and a Column Number (1-3). I created two new Calculated Fields (right click in the Dimensions or Measures area and select “Create Calculated Field”) to place each in its proper location:
rownumbercalc    columnnumbercalc

Step 3: Use “Row Number” and “Column Number” to create the grid

Now that the grid location fields are created, I just needed to drag “Row Number” to the Row Shelf and “Column Number” to the Column Shelf, and change both from SUM to a Dimension. When I used a Quick Filter to only include the 9 top fields, I had my small multiples view:

Step 4: Formatting

The rest is mostly clean-up, really. Hiding the Row and Column Headers, customizing the Tooltips, adding a date Quick Filter, and placing the small multiples map on a Dashboard. In the Dashboard, the titles for the 9 boxes are actually 9 very similar Sheets with Incident Type and Number of Records added as Text and filtered to just one of the nine incident types.

What do you think? Easy to make, right? Pretty effective as well, wouldn’t you say?

I’d love to hear your thoughts, and thanks for stopping by,

PS. Coastal Storms seems to be occurring in rather… non-coastal areas in the country. Not entirely sure why, but I’m guessing it’s a misclassification by FEMA. If anyone knows the story, I’d love to know.

Mapping the World’s Rivers

2014 August 25
by Ben Jones

“All the rivers run into the sea, yet the sea is not full; to the place from which the rivers come, there they return again.” Ecclesiastes 1:7

It was purely coincidental that during #MappingMonth a Tableau Public author reached out to me and asked me if it was possible to create a map with rivers as interactive polylines. He was in the process of gathering coordinates manually from Google Maps, and he felt there had to be a better way. I knew he was right – if we could find a data set with latitude and longitude coordinates for each river, then we could use the Path shelf to draw each river as a line on a world map.

What, exactly, is the use-case for a map of the world’s rivers? I admit I don’t quite know, but it was an interesting challenge, and certainly made for a fun and educational project for my two sons to help me with. You gotta get creative to make sure they learn something during the summer.

After Tableau mapping guru Allan Walker pointed me in the direction of, here’s what we were able to create (see below for a brief tutorial, and a #MappingMonth surprise):

How to Map the World’s Rivers

Step 1: Get the Shapefile

To start with, I had to find the Shapefiles for all of the world’s rivers. At least the big ones. As I mentioned, Allan Walker pointed me in the direction of’s 1:10m Physical Vectors, and uber map geek Nathaniel Kelso helped me find the files to download (he also runs a github account with links to download every NaturalEarthData download file). This resource is truly amazing – it has shapefiles for coastline, oceans, reefs, glaciated areas, and a few more – all freely available. I downloaded a zip file of rivers and lakes centerlines. Step 1 complete.

Step 2: Convert the Shapefile to CSV

This step used to be arduous and time consuming until Alteryx published a Shapefile to Polygon Converter to their Analytics Gallery. It’s a web app that requires a free login, and allows you to take that zip file you just downloaded and turn it into a CSV or a TDE (Tableau Data Extract). Most people are familiar with CSV, so let’s follow that option. Here is the CSV file that the Alteryx converter created for me. Here’s what the CSV file looks like – in particular, note the fields “Polygon ID”, “Subpolygon ID” and “Point ID”. They will play an important role in step 3:

Step 3: Connect Tableau to the CSV and create a Map

Now that you’ve got your CSV, it’s a fairly easy step to use it to create a map in Tableau. Start by connecting Tableau to this CSV, and then do the following in a new Sheet:

  1. Double click Latitude (goes to Rows) and double click Longitude (goes to Columns)
  2. Change Marks from Automatic to Lines
  3. Drag Polygon ID and Sub Polygon ID to the Detail Shelf
  4. Drag Point ID to the Path Shelf

You should now have the basic map of the rivers of the world, and your screen should look something like this:

To complete the viz, I colored the rivers by Scalerank, added two Quick Filters (Scalerank and river Name), formatted the Tooltips, and added the map along with a histogram of Scalerank to a dashboard. I asked my son Aaron to pick the title font, and he picked Brush Script MT because he said he thought the letters looked “rivery”. I couldn’t argue with that, so we made a PNG with transparency and added it as an image (because Brush Script MT isn’t a safe web font).

Now here’s to you, Mr. Robinson

I said I had a surprise, and here it is. I’ve been playing around with (read: obsessing about) different map projections lately. I figured out how to convert the latitude and longitude coordinates into x, y values of the Robinson projection, a projection that the National Geographic Society used from 1988 to 1998, before ditching it in favor of the Winkel tripel. I won’t get into too much detail here, but suffice it to say, the Robinson is a pseudocylindrical projection that’s really only suitable for creating thematic maps of the entire world. Compare it with other projections using this handy summary image. More to come on this soon, but for now, here is the rivers dashboard in the Robinson projection:

Notice that Greenland doesn’t loom as large as it does in the Mercator projection, which distorts it’s size quite a lot (it’s actually 1/8 the surface area of South America). Also notice, however, that the Robinson projection “curves” inward at both poles (latitude lines get shorter as you move away from the equator) – this means that if you were to zoom in to the street level in, say, Finland, streets that cross at a right angle in the real world wouldn’t appear to on the map. That’s what you get with Mercator in return for some area distortion. Every map has its pros and cons.

If you’re interested in building a Robinson projection yourself, here are the equations to make the conversion within Tableau. I recommend either drawing the coastlines and graticules yourself, or finding a good Robinson map image and adding it as a Background Image, fixing the position carefully. Here is the map image I used. It works fairly well when zoomed out to show the entire world, but I hid the Zoom controls since it really doesn’t work well when zoomed in.

Thanks for stopping by,

From GPS to Viz: Hiking Washington’s Trails

2014 August 8
by Ben Jones

Since moving to the Seattle area in early 2013, we’ve been doing our best as a family to tromp our way through the lush, scenic trails around us, guided by a helpful little orange book entitled “Best Hikes with Kids: Western Washington & the Cascades“. At first, my gentle suggestion (read: stubborn insistence) to hit the trails was met with some light resistance (read: outright mutiny) from my two iPad-wielding boys, but not so much any more, I’m happy to say.

Being a data guy, I wanted to track our every step through the Pacific Northwest, so I downloaded an app for my iPhone called Backpacker GPS Trails Pro. It’s great – it tracks our coordinates and lets us capture photos or video along the way, among other things. The default dashboard on is nice and all, but, well – I told you I was a data guy – I wanted to make my own.

First, I’ll show you what I made to track our treks, and then I’ll show you how I managed to go from GPS to viz in 7 steps.

How to go from GPS to Viz – 7 Steps

STEP 1: RECORD the trip & SYNC the app

This is the best part. Get out there, enjoy the trail, and make sure to hit the start button on the app. Here are some screen shots from a recent trip we took:


STEP 2: DOWNLOAD the data in .gpx format

First, I had to log in to my account on You may have a different GPS app, which is fine, but just make sure it’s one that allows you to download your data. If your app’s site lets you get the data in spreadsheet form, all the better. Mine didn’t, so I had to first get the .gpx file. Here’s a screenshot of the download page:

STEP 3: CONVERT the .gpx file to .txt

Next, I had to get the data into a text file, which was quite easy to do once I found a useful site called GPS Visualizer. It’s free to use (they accept donations), and you just indicate that you want a Plain text output, choose the .gpx file from your Download folder, check the boxes to add estimated fields, and Convert. Here’s how that looks:

STEP 4: CLEAN UP the data spreadsheet

This step involves opening the .txt file from step 3, getting rid of any header rows, moving the multimedia files to the bottom of the list, combining multiple .txt files into one spreadsheet and giving each its own unique hike name. Here is a screenshot showing the original .txt file and the fully formatted spreadsheet:


STEP 5: CONNECT Tableau to the cleaned up .txt file

Open Tableau Desktop (or Tableau Public), and click “Connect to Data”, select Microsoft Excel, navigate to your hike spreadsheet, drag the sheet into the middle area, and then click “Go to Worksheet”.

STEP 6: CREATE your viz

Use Tableau’s UI to drag and drop your data fields onto the canvas and create Sheets, Dashboards and Stories. I used a few advanced features in this workbook, including:

  • A Custom WMS (only works with Tableau Desktop) from USGS – US Topographic Basemap. Click Map > Background Maps > WMS Server, and enter:
  • A web page object on the Dashboard that dynamically links to each hosted photo on based on the URL column in my data. Note that I also changed the size of the photos since the large images took a long time for the Trimble Outdoors servers to load. Smaller photos were obtainable using a URL parameter (“?size=Size265x180″). Drag a web page object onto the dashboard, click OK, and then select Dashboard > Actions > Add Action > URL and fill out the dialog box as follows:

STEP 7: PUBLISH to Tableau Public & EMBED in your website

Can’t get much easier. Click Server > Tableau Public > Save to Web as… (or in Tableau Public, File > Save to Web as) and copy and paste the embed code into your CMS.

The Last Leg

This was a fun personal project that I made for my boys, so I took some extra steps to add design elements to the dashboard. I was shooting for a hand-made / trail map / scrap book feel, hence the hand-written font instructions, compass image, photo corner tacks, tally mark image, etc. The mountain shape cut-away at the top of the viz is actually from a photo of the Olympics here in Washington, so I tried to stay true to the territory with each design element.

Let me know if you make good use of this tutorial, and if you have any other questions about how I made it.

Thanks for stopping by, and happy trails!

Story Types: A Thought-Starter

2014 July 31
by Ben Jones

{Note: a version this blog post was also published on the Tableau Public blog as part of the Storytelling Month series}

Every data set contains a myriad of stories. I’m using the word “story” in a liberal way here, not necessarily in the “bedtime story” kind of way, or even the “headline news story” kind of way. By “story”, I simply mean a sequence of data-driven statements that progressively explain the world we live in.

With even simple data sets, these types of data stories abound, some more interesting than others. Whenever I run workshops along with my Tableau Public teammates, we’re amazed at how each group, given the exact same data set, comes up with unique insights.

Earlier this month the UN celebrated World Population Day – a day to “raise awareness of global population issues” according to its Wikipedia page. I decided to play a game and see how many different data stories I could tell with the a simple spreadsheet of population, birth rates and death rates for every country since 1960 as obtained from the World Bank’s online data repository.

I came up with six simple “types”: 1) change over time, 2) drill-down, 3) contrast, 4) intersections, 5) different factors, 6) outliers and trends. Use the tabs across the top to see the different stories, and use the tiles within each story to read each story point:

I ended this experiment with a feeling that I was just scratching the surface, and that there are many more data stories to be found and told from even this simple data set on world population.

I encourage you to consider these six story points types as thought-starters for whatever data set you are working on. Ultimately your data will have its own story, and it will likely be a combination of these building block story types and others that are out there. Also, help me out by downloading the workbook and see how many more you can tell. Leave a comment below with your version, or tweet me a link to it.

Thanks for stopping by,

Six Principles of Communicating Data: A Checklist

2014 July 20
by Ben Jones

In a section of the first chapter of Communicating Data with Tableau (O’Reilly, 2014) I lay out six principles of communicating data: 1) know your goal, 2) use the right data, 3) select suitable visualizations, 4) design for aesthetics, 5) choose an effective medium and channel, and 6) check the results.

These principles address more than the visualization step alone, they address the whole process from crafting a message to delivering it and actually affecting another person or group of people in a meaningful way. They involve self-awareness (what am I trying to accomplish here?) as well as empathy (how will this impact my audience?). It’s not just a “numbers game”; it’s also about words, images and emotions.

I converted these six principles into a simple checklist that I could use to remind myself of all the important ingredients that go into a successful communication effort, including many that I often gloss over or forget entirely. It’s yours as a pdf to download, use the interactive version below, or view it as its own tab. I hope you find it useful.

Download the PDF file .

I don’t claim that this checklist is either exhaustive or revolutionary. There are many other principles that could be articulated, and most of them are common sense. I’d love to hear yours. If there’s anything sophisticated in these six, it’s principle #3, which is based on the work of Jock Mackinlay, my esteemed colleague here at Tableau.

We’re privileged to live in a world that comes after the previous half century, in which the work of pioneering researchers like Jock and others (Schneiderman, Card and Bertin to name a few) established the foundation of effective visual encoding of numerical information – or how we interpret charts and graphs.

Let me know what you think by leaving a comment below, and thanks for stopping by,

Now Available: Communicating Data with Tableau

2014 June 16
by Ben Jones

CDWT_CoverI’m excited to announce that my first book, Communicating Data with Tableau, has been published by O’Reilly Media and is now available to purchase in ebook or print (in full color) at the O’Reilly online store or Amazon. Many thanks to my editor Julie Steele for working with me throughout the past year of writing, and to my family – my wife Sarah and my two boys Aaron and Simon – for dealing with my insane sleeping hours and sporadic moodiness over the past twelve months.

Of course a million thanks to all of the ingenious and incredibly generous members of the data visualization community – Tableau users and employees in particular. You’ve taught me much of what I included in this book.

What is it about?

This book is my attempt to show 1) how to communicate data well, and 2) how to use Tableau to do so. It’s not intended to be a comprehensive Tableau user manual, so not every feature is covered (for that type of resource, I’d recommend Tableau Your Data! by Dan Murray, as well as the helpful online tutorials available at the Tableau Software website).

Who is it for?

I wrote this book for anyone who needs to get a quantitative message across to an audience – analysts, journalists, engineers, marketers, students and researchers. Anyone with a modern browser can view and interact with the example projects that have been published to Tableau Public (free application available here), and readers will need Tableau Desktop 8.1 or 8.2 (free 14-day trial available here) to open the accompanying Tableau workbook files. There aren’t any examples that deal with the features that are new to version 8.2, which was not available to the public when the book went to print earlier this month. Expect a revision with 8.2 examples to follow later in the year or early next year.

How is it Organized?

The book is organized into 14 chapters that each deal with a different aspect of communicating data. After covering general principles and the Tableau user interface in the first two chapters, the book touches on both traditional as well as creative examples of communicating data: simple numerical comparisons, rates, ratios proportions and percentages, central tendency and variation, multiple variables, time series data, positional data, and combining multiple visualizations in dashboards.

In What Style is it Written?

While there are occasional philosophical musings (mostly in chapter 1) and even a fictional interlude into the world of “Chesslandia” in chapter 7, the book is primarily practical in nature. In 334 pages there are over 45 examples and loads of useful tips and tricks. If you’ve read my blog posts at this site, you’ll find the tone familiar. Some of the content is pulled from blog posts I’ve written over the past three years, but most of it is brand new.

For example, the book culminates with this multi-Sheet, multi-Dashboard workbook about the global expansion of the internet, as captured by World Bank data:


What was it like writing it?

Wow, it was at times elating, at times excruciating. It took many times longer to write than I originally scheduled (no surprise there), and the review and production processes, while affording me with many learning opportunities, were not exactly painless. As I mentioned, I felt very lucky throughout the writing process that O’Reilly and Julie Steele were willing to work with me, a first-time author. I’m sure many of my multiple “new deadline resolution” emails made Julie chuckle, but I never felt rushed or pressured to write anything other than the book I wanted to write. I can’t say how much I appreciate that.

Since my day job is as Sr. Manager of Tableau Public, and considering my blog regularly features Tableau Public “vizzes”, it’s a testament to the sheer joy of the software (and my love for working with data) that I didn’t O.D. on everything somewhere along the way. By the way, this book wasn’t sponsored in any official way by Tableau Software the company – it’s my personal project entirely. That being said, I credit my colleagues at Tableau, and none more so than my VP Ellie Fields along with the entire Tableau Public team, with providing me moral support and inspiration all along the way. Also, a special thanks to Andy Cotgreave for saying a few words on the back cover. Andy, you’re a class act, and I look forward to returning the favor. Without a doubt, I’ve found my tribe.

I’d like to hear from you!

If you buy it and read it, first, thanks for considering what I had to say. It’s a great honor to me that you would devote your time to hearing me out. Second, please let me know what you think! Email me (benjones at dataremixed dot com) or tweet me (@DataRemixed), and if you write an online review, I’ll be forever in your debt for the input and feedback – both positive and constructive. I fully intend to write more books in the future, so the more I hear about what worked for you and what didn’t work for you, the better my next books will be.

Thanks for stopping by,

In Defense of Intuition

2014 June 15
by Ben Jones

Intuition is under fire

Two months ago I saw a television commercial for Business Intelligence software, and in the commercial a customer being interviewed had the following to say:

“We used to use intuition; now we use analytics.”

In other words, we’re being asked to believe that the business owner was able to make progress by replacing decision-making using intuition with decision-making using analytics.

The statement didn’t sit well with me, so I immediately tweeted the following:

Working in the BI industry, I’ve heard similar attacks on human intuition from many different sides, and I don’t agree with it at all. I had a chance to say more about my objection to this notion at the eyeo festival last week in a brief talk entitled “Intuition Still Matters: Using Tableau to Think with Your Data”. You can see the slides to this presentation here.

In this blog post I’d like to explain why I feel that in a world awash with data, human intuition is actually more valuable than ever. Briefly, human intuition is the spark plug that makes the analytics engine run.

Intuition wasn’t always a byword

Contrast the commercial’s negative attitude toward human intuition with Albert Einstein’s rather glowing appraisal:


Without a doubt, it would be hard to come up with a more positive statement about intuition than this. So which is it? Is human intuition a faulty and antiquated decision-making tool, in dire need of replacement with something better, or is it the only valuable thing there is?

Before we go any further, we should define the terms.

What is intuition?

Oxford dictionary defines intuition as follows:

“The ability to understand something immediately, without the need for conscious reasoning”

It comes from the Latin root word intuērī, mean to look at or gaze upon. Thus the etymology of the word links it with the human visual system. Sight and intuition both occur instantaneously and effortlessly. Both can also mislead, of which more later. With intuition as with sight, the awareness comes before any logical explanation.

The link between intuition and sight is often a very literal one. In social situations, we intuitively sense other people’s emotions when we first lay eyes on their facial expressions:

Fig 1. Basic facial expressions (source)

And with abstract representations of data, we spot the marks that have certain unusual attributes in an intuitive way – that is, we notice them without having to think about it. We call these attributes “preattentive”. Noticing them doesn’t take effort – it’s as if it happens to us. Here are two examples: what do you immediately notice about them?

Fig 2. Two preattentive attributes (source)

Similarly, we can feel a compelling sense of confidence about what’s going to happen in the future, and what we should do about it. This is what is commonly meant when someone says a person has a great intuition about a specific field.

Intuition is commonly contrasted with reason, “the power of the mind to think, understand, and form judgments by a process of logic.” Logic, in turn, involves “strict principles of validity”. And analytics is “information resulting from the systematic analysis of data or statistics.”

To make the best decisions in business and in life, we need to be adept at many different forms of thinking, including intuition, and we need to know how to incorporate many different types of inputs, including numerical data and statistics (analytics). Intuition and analytics don’t have to be seen as mutually exclusive at all. In fact, they can be viewed as complementary.

Let me give some examples of how intuition provides the spark for the analytical process.

Five Reasons Why Intuition Still Matters

  1. Knowing what to measure in the first place

    Any process has an almost infinite number of variables that could be tracked and analyzed. On which should we spend our time? Knowing where to start can be a problem, especially if we know very little about the subject we’re dealing with.

    One school of thought goes something like this: collect data on everything and let an algorithm tell you which to pay attention to.

    Sorry, I don’t buy it.

    • First, not even the NSA collects data on “everything”. I guarantee you a filter has been applied to narrow the set of inputs. God may have counted every hair on your head, but I seriously doubt anyone else has.
    • Second, while data mining algorithms can discover notable patterns in huge data sets, only human intuition can discern between the useful patterns and the useless ones. They get their very “usefulness” from our goals and values.
  2. Knowing what the data is telling us (and what it’s not telling us)

    Once we pick data to collect and metrics to analyze, what do the numbers tell us? We talked about preattentive attributes briefly – via data visualization, our intuition can be put to good use interpreting the important 1’s and 0’s in the databases we’ve meticulously built.

    Using intuition in this way isn’t a perfect process though. Just as we might recoil from a garden hose that our instincts tell us is a snake, we can see signals in data that aren’t really there. Alternately, we can miss really important signals that are there. Just because intuition doesn’t work perfectly, though, doesn’t mean it should be discarded. We just need to hone our intuition for working with numbers, and we need to distrust it somewhat.

  3. Knowing where to look next

    Jonas Salk, the American medical researcher who developed the first polio vaccine, had the following to say about intuition in his book Anatomy of Reality: Merging of Intuition and Reason:

    Fig 3. From Jonas Salk’s Anatomy of Reality: Merging of Intuition and Reason

    He made a discovery that has saved the lives of countless people in the world, and he chalked up an important part of his success to intuition. Often the best outcome of an interaction with data is that we sense another, even better question to ask. And the process iterates. The realization of the next place to look can form in our mind like an intuitive spark. The light bulb analogy applies.

  4. Knowing when to stop looking and take action

    For many types of questions or problems, we could continue to search for a solution ad nauseum. Think of a chess game. What’s the “best move” to make at a given point in the game? Russian chess Grandmaster Garry Kasparov knew something about this question, and here’s how he understood it, as stated in his book How Life Imitates Chess:

    Fig 4. From Kasparov’s How Life Imitates Chess

    There comes a point in time when it’s best to stop analyzing and make a move. Knowing when we’ve arrived at this point is a function of intuition. If we don’t have this intuitive switch, we can suffer from “analysis paralysis”, and then we go nowhere. We’ve all been there.

  5. Knowing what how to get our message across

    A key part of the data discovery process is communicating our findings with others. We can use our intuition to choose the best message, channel, venue, visualization types, aesthetic elements, timing, tone, pace, etc. If we have a deep understanding of our audience, we will intuitively know what will get through to them, and what will fall on deaf ears. When we get it right, it can be a wonder to behold. Think Hans Rosling.

    Crafting the communication is a creative process, and human intuition will need to be tapped to do it well.


For the reasons outlined above, I don’t believe that human intuition will ever be rendered obsolete. No matter how smart our algorithms get, no matter how sophisticated our tools or methods, the intuitive “spark” in the human mind will always be the key element in our thoughts, in our decisions and in our discoveries. Data and analytics can fuel these sparks, and they can provide a way to make sure we’re headed in the right direction, but they can’t replace human intuition. Not the way I understand it, anyway.

I don’t think the creators of the commercial would disagree with this point of view, so it likely comes down to semantics. Maybe the business owner in the commercial should have said: “We used to rely on intuition alone, now we combine it with analytics to make even better decisions.” Slightly less snappy, I know. But at least intuition doesn’t get thrown under the bus.

Dimension Line Charts: a (slight) variation on arrow charts

2014 April 18
by Ben Jones

Arrow charts are an effective way of showing how values changed from one point in time to another (like slopegraphs), and they have been touted by Naomi Robbins of NBR Graphs. They have also been created using Tableau before by zen master Joe Mako. I really like arrow charts – as a mechanical engineer, I understand the language that they’re speaking.

But there’s a way that they can speak even more clearly to me. It’s more of an accent, really.

Here’s a version of what I’ll call a “dimension line chart” (“dimension” as in GD&T, not Tableau’s dimensions and measures) that I made to show which NBA players improved the most over last season, and which players’ performance regressed the most:

How is it different from traditional arrow charts?
It’s a very subtle change that has four parts to it:

  1. There are “extension lines” added via reference lines to show the starting and stopping points of each line
  2. The arrowheads are custom shapes with tips that end at the reference line instead of just beyond it
  3. The direction (+ or -) and the magnitude of the change is shown as a dimension in the middle of the line
  4. I added a light row banding to make it easier to see which arrow applies to which player

That’s pretty much it. It’s a slight dialect of the language of arrow charts, really. Translation: it’s just a fancy arrow chart.

Here’s what the traditional arrow chart version looks like:

How do the changes help?
I always felt that traditional arrow charts seemed to imply movement beyond the end of the arrow, like a flow diagram (showing direction of wind, magnetic field, or water flow) or a traffic sign telling me which way to go, not where to stop. But, literally speaking, the data stops at the very end of the arrow. And with Tableau, if you use a default filled shape for the arrowhead, the data technically stops in the middle of the arrowhead, since that’s the center of the shape (more a detail about Tableau, not arrow charts themselves):


Using arrowhead shapes that end in the middle rather than the edges of the file corrects this small inaccuracy:


Nitpicky? A little, maybe. Click here to get the four centered arrowhead png files I created for this project.

What inspired this variation?
I enjoyed my drafting class in high school, and I went on to study mechanical engineering in college. Drafters and mechanical engineers draw diagrams of physical objects and indicate their dimensions so that machinists can make them in the real world.

There’s a correlation to what we’re attempting to do with data visualizations: we’re communicating the relative sizes of measurable quantities. Instead of feet or inches, the units can be dollars or population or whatever.

We’re giving a “blue print” to our audience and asking them to build an understanding in their mind.

Here’s an example of a technical drawing that the dimension line chart draws from:


Issues and Opportunities for Improvement
One weakness of this variation is that it doesn’t handle slight deltas very well. If the reference lines are very close together, the arrow doesn’t look very much like an arrow at all. If we remove the rank filter and look farther down the list, here’s what we get:


Let me know what you think, I’d like to hear your opinion. If you can think of a way to improve dimension line charts, leave a comment. Some ideas I have are to give the numerical values a white box background to break up the dimension lines and to change the styling of the arrowheads to be in line with GD&T standards. Also, I’ll be doing a “how-to” write-up on the Tableau Public blog as part of #TableauTipsMonth, so be on the lookout for that in the next few days.

Thanks for stopping by,

Earthquakes, Los Angeles, and Being Shallow

2014 March 29
by Ben Jones

Last night I was analyzing earthquake data from the USGS Earthquake Hazards Program. When I woke up this morning, I was assaulted with tweets about the 5.1 magnitude earthquake in La Habra. I spent most of my life in L.A. Coincidence? I think not. Okay, yes, it was absolutely a coincidence.

{A tangential rant: Obviously I’m conflating correlation and causation, but as dumb as that line of reasoning is, it underlies a huge chunk of human thinking – superstition, astrology, plenty of religious thought, ENTIRE BRANCHES OF MEDICINE. In a word: pseudoscience. Not good, people.}

Back to earthquakes: I was updating a viz about the history of recorded earthquakes around the world since 1900. Here it is:

It clearly shows the clustering of earthquakes along trenches and fault lines, a fact that we take for granted now but was not known by humanity prior to the 1960s.

It also shows what seems to be a dramatic rise in earthquakes around the world. Of course what we are looking at here is recorded earthquakes, not actual earthquakes. A minor point? Not really. The number and type of seismographs changed throughout the course of the 20th century. Simply: they got better, and more were installed. So this data set can’t really answer the question: “Are actual earthquakes increasing?” It’s only really the number of recorded 6.0 – 6.9 magnitude earthquakes that increased. We got better at detecting and measuring earthquakes in this range and below. It’s not easy to miss a 8.0.

Los Angeles
All the talk today is about earthquakes and Los Angeles. Here are the 6.0+ earthquakes in the Los Angeles area since 1900:

I remember the 1994 Northridge earthquake very well. It was pretty incredible. For all the talk of “data storytelling“, the little orange dot on the map doesn’t really tell the story of the Northridge earthquake, or any of the other ones for that matter. That’s good perspective for all of us.

I’m not saying data can’t give a human side of the story – I believe it can (I help run a conference about it). But if you want to know how people felt during an earthquake, you’d probably have to talk to them, and they’d each say something different. Data is great, but it doesn’t tell the whole story.

“Wait, the earthquake in Los Angeles was shallow…?” I mean, come on, what a total set-up, right? Even I had to love the tweets about the supposed relationship between the depth of the earthquake and the residents of L.A.

Serisouly, though, what about earthquakes, and frequency of magnitude and depth? These dot plots show how common larger earthquakes are, and how far below the surface of the earth they occur:

The moral of the story? I guess it’s not just in Los Angeles where you’ll find shallow ones that cause a relatively big stir…

Thanks for reading,