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.
Every month we’re going to attempt to do a meta-review of the site, using graphs of course.
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!
Facebook has a great post up from their Data team about relationships between people, and more specifically concluding that the majority of Facebook users are only four degrees of separation away from anyone else on the social networking site.
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.
Let’s start this off with some honesty. I used to love pie charts. I thought they were great, just like the way I used to think Comic Sans was the best font ever.
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?
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.