Hat Tip: Friend of the Blog Matt Crespi, who linked us to here, who linked to the original here.

We created a guide to highlight good and poor areas of resource production to better help you pick intersections to build your settlements in Settlers of Catan.

We assume that you set up Catan as per the game instructions, and that you arrange the tokens in alphabetical order starting with the “A” token at the top of the board and work your way inward in a clockwise pattern, skipping the desert. Looking at your board, determine where the desert hex is and refer to that chart below to see your board’s map.

An Intersection Score is determined by adding up the expected number of resources you will receive from all of the hexes for a given token based on all the possible combinations from the roll of two dice. For example, an “8″ is expected to be rolled 5 times for every 36 rolls, while a “12″ is only expected 1 time for every 36 rolls. So, an intersection with a “5″, an “8″, and a “11″ would expect to produce 4, 5, and 2 resources for 36 rolls, giving it a total score of 11.

Different Good and Poor scales are applied to the Intersections depending on how many hexes touch it. 3-Hex Intersections are statistically going to produce more resources than 1-Hex Intersections, so different ranges are used and can be seen in the chart below.

This guide is only meant to highlight the statistical probabilities of resource production at any given intersection. We understand that gameplay and the placement of resources is of course more complex given things like the types of resources on the board, the harbors you are looking to obtain, and the use of the robber.

What’s interesting is how there are definitely “good” and “poor” areas that exist on the board where there are concentrations of higher probability numbers.  We couldn’t display this on any typical graph, but instead we use a modified geospatial layout of Catan to convey the information.  We could have used a heat map with strong gradations of color, but by defining definite ranges with strong opposing colors, you can see basic ranges and get an idea of where to build.

Click through to after the jump to see the graphs for each of the configurations.

We’ve copied in each of the board layouts here, but if you want to be able to zoom in more, we suggest looking at one of our two PDF documents:

Resources -> One Grid Per Page

Resources -> Five Grids Per Page

The picture comes from Andrew Abela, who hosts a blog called Extreme Presentation.

I think it’s a great baseline, and while I don’t agree 100% on everything (like the use of pie charts), I think this covers most every situation you’ll come up against in chart design.

This highlights the problem when people assume causation when it comes to trends in our societal data.

Click through to the article to see all the graphs.

Of course, XKCD is no stranger to graphs and interesting visualizations.

Visitors

We had a “soft launch” towards the end of October with a few hits, but we really took off in November, starting with a post about my Halloween Candy experiences this year, which was shared out by Geekadelphia.

Red bars represent days with new posts, and Blue bars are days without new posts.

We thought an interesting stat to look at would be the number of pageviews per visitor.  On days where the diamond is above 1, it represents where on average people clicked on something else within the site beyond just their initial entry page.  The highest day for that was 11/10/2011, when Drew had his post on Scrabble.

All in all, we’re very happy that in our first month we had 737 visits and 1,049 pageviews.  Thank you!

Visits by Post

Posts:

(266) – my halloween candy in graphs
(222) – site index
(161) – most valuable cities in ticket to ride
(85) – what word should i play
(62) – top 10 most popular cities in north america
(50) – most populous city by state
(33) – better than watching the detroit lions
(23) – geotagging visualization of philadelphia
(18) – twitter on 11-11-11
(12) – graphs we love: espn’s stats info twitter
(9) – graphlink: invisible bread
(4) – nfl championships per year
(1) – stanley cup 2011 sparklines
(1) – welcome to graphgraph
(102) – category, tag, & author pages (we kept this separate to keep the focus on individual posts or the site index)

Twitter, Facebook & Google Reader

Right now that chart is pretty barren.  However in future months we’ll see a line chart (with hopefully a lot of growth).

Facebook – 5 Likes -> Click here to like us
Twitter – 6 Followers > Click here to follow us
Google Reader – 7 Subscribers -> Click here to access the RSS feed and add us to your favorite reader

Thanks again to everyone for making this first month a success!

From the article:

We found that six degrees actually overstates the number of links between typical pairs of users: While 99.6% of all pairs of users are connected by paths with 5 degrees (6 hops), 92% are connected by only four degrees (5 hops). And as Facebook has grown over the years, representing an ever larger fraction of the global population, it has become steadily more connected. The average distance in 2008 was 5.28 hops, while now it is 4.74.

Very fascinating findings!  Read the full article for more information and some additional charts that tie everything together.  And while you’re there, how about you go ahead and “Like” us on Facebook?  Awesome!  Thanks.

But then I had some #RealTalk, and I’ve been enlightened in the error of my ways, and I want to pass on what I’ve learned to show people why pie charts aren’t the best choice for visualization.  For my day job, part of my work involves creating visualizations out of business data for our customers.  I picked up a copy of “Information Dashboard Design” a book by Stephen Few of Perceptual Edge.  If you’re at all interested in data visualization, I highly recommend his books, and on this site we attempt to use a lot of the principles in creating the visualizations we present to you.

But speaking specifically of Pie Charts, here’s why they’re a bad choice for your and your data:

It’s Hard To Do Comparisons

With a pie chart, the size of the angle determines the proportion on the data.  Everything adds up to a nice cool, crisp 100%.  But what if you want to know the exact numbers?  Well, you’re going to need data labels attached to your data, which can take up space and be cumbersome.

Look at these pie charts.  Can you tell me the exact value of each of the slices?  Can you order the colors from largest to smallest in each chart?

Sure, you can probably do it, but it will take a bit of work.

Now, let’s take a look at the same data, but with bar charts instead.  Same questions: Can you tell me the exact value of each of the slices?  Can you order the colors from largest to smallest in each chart?

Holy cow!  Wasn’t that much easier to figure out?  A bar charts makes better use of the space AND makes it easier for your to get more accuracy in understanding the numbers and doing comparisons to the other splits in your data.

Hat Tip to Wikipedia for the images.

Colors Don’t Mean Anything

The above examples uses colors to connect the data between the pie chart and the bar chart.  However, in a pie chart, you NEED color to differentiate the slices, and quite often you’ll have a legend that tells you what each color represents.

We’re going to look at some examples using data of World Population, with data provided via Google and the World Bank.

Is the color giving you any extra information?  No.  It’s only giving you a roadmap to see what color represents which region, and the colors being used don’t actually represent the regions in any way.

Now, let’s look at the same data in a Bar Chart.

One color.  You don’t need all the colors on the chart.  In fact, this now enables you to smartly use color to deliver more information into the same amount of pixel space.

We’ve been able to not only deliver the same information as the pie chart, but we’ve introduced a second piece of information to the same pixel space.

Not Much Information

Let’s look at the pie chart again and draw some conclusions from the image.

What we can conclude:

• East Asia & South Asia are bigger than the other regions
• North America is relatively smaller than the other regions
• X is [bigger/smaller/about the same size] as Y

We can only conclude RELATIVE sizes.  If we want more information like exact numerics (or even approximate numerics), we need data labels attached to the charts.

Let’s go back to our bar chart:

• East Asia has about 2.2 billion people
• North America has about 300 million people
• South Asia has about 1 billion more people than South America
• North America is about half the size of South America
• East Asia & South Asia are bigger than the other regions
• North America is relatively smaller than the other regions
• X has about Z amount of units
• X has Z [more/less] units than Y
• X is [bigger/smaller/about the same size] as Y

You can not only see comparisons, but with the addition of the axis along the side, you can start to get a grasp on the actual numerics behind the data.  It’s also a lot simpler to actually do comparisons to one another and get a good grasp on the differences between your data points.

Too Many Data Points

Let’s take a look at the World Bank data again, this time with a focus on country.

Yikes.  This is another pitfall with this type of visualization: Too many data points!  There’s no way you’re going to be able to draw any good conclusions from this chart.

So let’s take a look at the bar chart version:

Well, let’s be honest.  This might be a slight improvement, but it’s not much better because it’s hard to tell what the different countries are given that you can’t read all the labels.

So at this point, you need to think about what you’re trying to show with your visualization.  Is it relative population?  Is it something else to do with countries?  Always be thinking about the QUESTIONS you’re trying to answer, and pick the appropriate visualization for the job.

A scatter chart is a good way to throw lots and lots of data onto the screen and still effectively draw conclusions.  If you need accurate information about exact numerics in a large data set, then you might want to use a regular ol’ table, and include a column that has the percentage of that member against the total.  However, a scatter plot will help you see relationships in your data.

Let’s go back to our World Bank data.  It’s a very rich data source that looks at information across all cuts.  By taking a look at multiple metrics on a scatter chart, we can draw out some interesting data.

Here’s a chart plotting out Life Expectancy vs. GDP by country, grouped by region:

• Size of the bubble gives a relative indication of population.  This is much better than the sizes of pie slices in a pie chart.
• Colors actually mean something here, because each color represents a region as opposed to an individual data point.  You can see that Sub-Sarahan countries sit on the bottom left, East Asian Countries in the middle, and predominantly European, Central Asian, and North American countries on the right.
• We’ve added in two new metrics in addition to just population.  We can see GDP Per Capita and Life Expectancy as it relates to population and can draw a correlation that the richer nations enjoy a longer life expectancy.

Now, while I may have created the visualization above myself, I can not take credit for the idea.  It came from Hans Rosling and a program on BBC Four, a clip of which I share with you below.

Try doing all that with a pie chart.

When Pie Charts Are Appropriate

Pie charts are appropriate in some cases.

When making a joke involving something circular:

When referencing actual pies, like this chart I made featuring our leftover pumpkin pie:

Conclusions

Remember that when you’re displaying information, you have a limited amount of space to get your points across, especially if you’re designing visualizations on a computer with a limited pixel space.  It’s all about increasing the amount of information you get and utilizing every pixel as efficiently as possible, therefore increasing the information utility.

Pie charts are bad for many many reasons, and if you’re designing a visualization, think about alternatives that could get your data across cleaner and more accurately.

The above video was posted on the official Twitter blog and is an interesting use of geospacial visualization over time.

A few things you can gain from the video:

• You can see where Twitter has it’s main influence based on where the 1′s are.
• The initial wave at 11:11AM in the morning sweeps across the whole globe, but the secondary wave at 11:11PM only really makes a splash in North and Central America. My presumption is because on those continents they use the AM/PM designation vs. other nations that use the 24-hour clock.

Hat Tip to @HashTagOnions for the link.

Fine. You want graphs, you’ll get graphs.

How about a graph from the Bureau of Travel Statistics about the travel patterns during the week of thanksgiving?

Or maybe one graphing turkey consumption around the world?

How about a graph from a Gallup poll taken in Turkey?

You wanted original content? Too bad. I’m eating turkey.