First, a quick overview of some technical bits. The user can select a range of dates from the map interface, and a new set of markers is rendered after an AJAX query to the backend PHP code, which requests the data from our database and aggregates it at the zip code and state level. The decision to do this two level aggregation was based on the performance hit that would be taken in trying to render on the order of 7000 icons for a year’s worth of data on a nationwide map. The MarkerManager class (which used to be part of the core Google Maps API) is used to display one set of icons when zoomed inside of what might be called “state level” and another set when zoomed out further. This adds a bit of extra complexity to our colormap choice.

There are a number of ways that this data could have been mapped into visual symbols. The size or shape of the glyphs could be related to the number of admissions, for example. The method that we are using maps the number of admissions to color (when this mapping is stored, it is called a colormap). The particular mapping that we use is important, because some mappings are better than others for certain tasks. For the markers on the map, we use a colormap that smoothly transitions from a blue at the low end to a red at the high end. The particular choice of blue to red leverages our cultural understanding of blue as “cold” and red as “hot” (note that sometimes colormap choice depends on cultural interpretation), a metaphor which works well as a representation of low vs. high admission rates. The combination of these two choices allows us to understand the general trends in the data without needing to refer back to the legend frequently. The drawback is that it is a bit more difficult to compare the value of two individual markers than it would be if we had picked a colormap of more distinct hues such as {red, orange, yellow, green, blue, purple}. We tried to make it easier for the viewer to make comparisons by limiting the number of colors in our legend.

The next important choice is how exactly to map the number of admissions to a particular color. To do this well, an understanding of the distribution of the data is necessary. Intuitively, we would expect that there will be high admissions numbers for the zip codes closest to the museum, and the trend will be toward increasingly lower admissions from more distant zip codes, and likely there will be many zip codes with very low, but non-zero admissions numbers. The best way to look at this objectively is with a histogram. I will take a number of approaches with the data from the year 2008, for which the highest raw admission count for a zip code was 12,375 (during the aggregation, 9 digit zip codes are merged into 5 digit zip codes, resulting in slightly higher numbers in some instances), out of 6806 zip codes.

This first histogram segments the zip codes into buckets in a linear fashion, with each bucket representing 10% of 12,375. As you can see, the vast majority of the zip codes end up in the first bucket. If we used 10 colors with this linear mapping, almost all of the glyphs would be blue. It would be great for identifying and comparing the few zip codes that have very high admission, but a lot of subtlety in the big picture would be lost.

This histogram also uses a linear segmentation, but with an upper limit (or clamp). Any zip code with 10 admissions or more are put into the red bucket. This would be fine for comparing admissions from distant zip codes, but we wouldn’t be able to distinguish visually between admissions from zip codes around Indianapolis.

This is where the beauty of logarithms comes to the rescue. Let’s use our example to walk through the process. First we determine the logarithm of our maximum number, 12375, in base 2 (working in base 2 is very efficient for computers). The result is 13.59. The highest bucket represents 12375 in the real world, and 13.59 in exponent-land. Each bucket represents a tenth of 13.57 in exponent-land, just as in our first histogram each bucket represented a tenth of 12375. We then simply derive the numbers that each bucket represents in the real world; 2 to the 1.359th power equals 3 (when represented as a whole number), for example. These whole numbers in the real world are then used to put the zip codes in the proper bins.

The result is that we have teased out more meaning to bestow upon our colors. There are still thousands of zip codes in those blue buckets, but they are the ones that are scattered across the nation on our map, so here we are aided by spatial distribution. In the red zone, there are dozens of zip codes in each bucket, and this is just what we want to be able to analyze the subtleties around Indianapolis where the icons are more spatially dense. It also turns out that this trend is pretty much independent of the date range, and the colormap still works out well at the state level.

On the actual map we use eight icons rather than ten to reduce clutter in the legend, but the algorithm is the same.

You might notice that we use a different type of colormap in the demographic windows. We actually tried using a smooth colormap for this data for the more linear demographics (Income, Age, etc.). The problem was that it was then very difficult to visually relate a particular shade of blue or green to a particular age or income level, which would almost certainly lead to eye strain and headaches. So here we used a more rainbow-like colormap to make that analysis easier.

In summary, colormap selection is a complex process involving aspects of mathematics, design, and the nature of the dataset. It’s one of those areas where art and science come together and the best practices of each can lead to successful communication of concepts and beautiful representations of data.

You can find a lot more graphs from the survey results in the rest of this post, but I thought it would be interested to just pull out a few interesting stats for you up front.

Stats Quick-Hit:

• 480 People took the survey between 12/22/2009 and 2/9/2009 (our web traffic during that time included 113,000 unique visitors and 450,000 pages served)
• Almost 90% of people who took the survey were satisfied or very satisfied with their experience on imamuseum.org
• The average visitor is Female (67%) and between 25-34 year old.
• More than 55% of website visitors use Facebook! Guess keeping the IMA’s facebook page flush with content is a good idea!
• Sadly 51% of survey-takers Never Comment on Blogs (c’mon… poke us a little, we don’t bite!)
• Survey-takers were overwhelmingly Caucasian. Our real attendance through the doors of the museum is different than this, so it seems like we need to do a better job of reaching some different audiences.

We’ll be contacting the lucky winner of the iPod touch this week and sending out the love to them!  Thanks to everyone for helping us with the survey!  We’ll be back in touch as we put together a new set of user personas to use for some upcoming web work.

In the mean time, enjoy sifting through our data for us.  If you see anything interesting here, be sure to be one of those 49% who do comment on blogs, and Let Us Know!  We’re happy to answer any questions you ask!

As numbers twirl around my head like 10-year old Chinese gymnasts (they are definitely NOT 16 that’s for sure), I thought I’d share with you some of my favorites. You can find a lot of these stats on the IMA’s dashboard, but some of the numbers are not accessible to the public…until now. In the Museum’s ongoing dedication to full transparency, here’s IMA by the numbers:

Full-time Employees: 197

Average Daily Blog Readers: 220

To Live Forever Exhibition Attendance through Aug. 11: 18,254

IMA Members: 10,454

Pieces of Glass in the Efroymson Entrance Pavilion: 225

Days until To Live Forever Closes: 24

Tickets Sold to Summer Nights Film Rocky Horror Picture Show: 714

Percentage of Web visitors from Indiana: 52%

IMA Curators: 12

Visitors to IMA, July 2008: 47,548

Cubic Yards of Concrete poured for 2005 renovation: 25,000

Square feet of IMA building: 492,000

Nugget Factory productions on YouTube: 100

Women named Pam on Staff: 5

Designers named Matt on Staff: 3 (2 graphic & 1 web)

Days until Power and Glory: Court Arts from the Ming Dynasty opens: 73

Number of Views to IMA’s first-ever YouTube video: 3,593

Number of days I’ve worked at the IMA: 1,354

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