Normalizing Geographic Data

Geographical data has always been fascinating to me.  Probably because maps are fascinating.  Overlaying a map with data to create a heat map provides an interesting perspective to behavior and markets based on location.  There is a beautiful map of Walmart’s growth over time on FlowingData.  This graphic provides a lot of insight into Walmart's growth, but this information can be misleading.  The concept of normalization can provide some clarity.

Normalization is a concept that allows two metrics with different dimensions to be compared using a common base.  While shopping we may have the choice between 100 paper plates for 2.99, or 250 plates for $5.99.  Simple division tells us that the first option is $.03 per plate and the second option is $.024 per plate.  We normalized the price to the unit (plates) in order to compare the two offerings using a common unit.  

The issue with geographic based data is that heat maps are usually created using a standard map of the United States (or the world, specific state,...) but these maps depict area.  Since populations are not distributed uniformly across areas, it rarely makes sense to compare metrics using acreage as the common unit.  

A common example of this is with election data.  We all see the results roll in and the data is presented with red and blue shadings of states to indicate which way the voters in that state voted.  

The 2012 election results are pictured above.  At first glance the heat map would indicate the red candidate is the favorite.  But unless votes are assigned to acres, this doesn’t make since.  Populations are often congregated near coasts, but in this map Wyoming with less than 800k people appears to carry more weight than say Maryland with just under 6MM people.  Normalizing the data by the underlying population can provide more insight into the actual results.

Here the map (actually a cartogram) is morphed so that the area encompassed by each state boundary reflects the population of each state, not the geographical area.  This modified map indicates the blue candidate won a large percentage of the vote.  Of course we know this to be true, but this graphic displays the data in a more appropriate context.  Similarly we could modify the map to reflect electoral college votes, but population is a proxy for that.

So what does this have to do with web analytics?  Web Analytics is often about sales and markets.  Google Analytics, Adobe, Tableau and the host of other platforms make it very easy to display your data on a geographic heat map.  But don't let the temptation to make something eye catching cause you to create something that can mislead your intended audience.  Let’s say we have 100k conversions in California, and 100k conversions in Wyoming.  On a standard heat map, these two states would have the same color shading indicating they are equal.  Yet if we normalize over population, we would see that we have a much larger presence in Wyoming, and have really just barely tapped California.  The first approach might cause us to choose the same strategy for California and Wyoming, but the second would cause us to choose very different strategies.  This would hopefully be obvious when we look at these two states, but if we expand the segmentation to zip code this might not be as clear.

Even if we do not have the software necessary to morph maps by population (or the number of customers in our target segment) we can normalize the metrics we display by population.  Population data is readily available on the Census Bureau website.  Using percentages of the total population, instead of measured values (sales, views,...), is one easy way to accomplish this.

Other applications of this concept might be more product specific.  For instance, if you are selling skis, on a heat map of sales you might notice western mountain states carry a greater load of the total sales.  This makes sense intuitively.  But if you were to acquire data that depicted the populations of avid skiers by zip code, you might find significant marketing opportunities outside of what was expected.  For instance Texas might be under represented compared to Colorado, but the skiing population is smaller.  Normalizing might allow you to see that conversions could actually be better on Texas based consumers than Colorado based consumers.  This could allow you to target this demographic with greater precision.

Getting back to the Walmart graphic in the first paragraph, you might have noticed a lot of under representation in the west.  Take another look at Nevada.  There are no stores anywhere in the middle of the state.  Hopefully by now you realize that normalizing this data would show that Walmart strategically positioned their stores in population centers, and there is very little population in the middle of the state.  

Resources -
http://www.esri.com/news/arcuser/1000/files/normalize.pdf
http://www-personal.umich.edu/~mejn/election/2012/
http://adam.webanalyticsdemystified.com/2010/04/13/comparison-reports/
http://semphonic.blogs.com/semangel/2010/01/tactics-in-web-analytics-visitor-segmentation.html

Crank the volume: Your blog with Infographics

This is your blog without Infographics:

Figure 1 - Few pictures or icons to look at...my eyes are tired. Text puts me to sleep.

This is your blog with Infographics:

Figure 2 - Infographics make pretty much anything interesting! Really. No. Really.

So now that you clearly see Infographics are really cool, so how do you go about creating them, or sourcing them from other authors? Keep reading. Here’s how it’s done:

1. Visit a site like visual.ly and check out what they have. There are tons of existing Infographics that you can source or you can connect with a content creator and have your Infographic custom-made.
2. Thinking graphically can get you way ahead of the game. You can also use office tools like PowerPoint to create your own Infographic, but there are some limitations. 
3. You can overdo the Infographics, so make sure you keep your design tenets and guiding principles top of mind, and keep those colors and text in check.
4. The key behind Infographics is they communicate detailed subjects very quickly and easily because they effectively combine graphics, icons, bullets or key points, typography (word art) and white space into all into a consumable format.

Guy Kawasaki is a master of Infographics. He uses his blog and site to communicate quickly and effectively on all kinds of topics. He links his tweets to his Infographics. It’s a fantastic model for using the medium. Click here to see a cool example. 

Besides Guy’s blog, there are a few other blogs and content sources that use Infographics effectively, like TechCrunch, Gizmodo, and Fast Company. A quick Google search can point you to many more.  The point is, if you’re not using Infographics in your blog, you’re missing out on the ultimate ‘interesting’ factor.  

In my research, I ran across a tool called Visualize.me that makes an Infographic for your resume and it can pull the data straight from LinkedIn. That’s cool! But, use with caution, it’s pretty edgy for most employers and recruiters.  Here's my personal version. 

The Medium is the Message

Data is great. Looking at it quickly, and knowing exactly what it says is priceless.

After you have defined the business purposes and you know what metrics you’re looking for, take the time to build a dashboard. The investment will be well worth it.

Imagine if your data consisted of a plain table with a time variable and amount of sales. You could present this in a nice excel table, but certainly you would present the time series data as a line graph. Instantly the audience would receive this information and visually identify trends or local maxima and minima. Visual presentation creates instant recognition of how the underlying data behaves in a way a plain table can not. Similarly dashboards combine multiple metrics and compile them into a simple visual reporting format that communicates more efficiently than pages and pages of boring tables. Dashboards are especially helpful when presenting data to managers who are interested in only what is most important to a decision.

There are three keys to keep in mind when choosing the appropriate representation of your data. First, what is underlying data? This seems obvious but there are many examples of poor choices of presented data. With regards to the image below, a response for one candidate did not prevent the respondent from also responding for another candidates. Answers were not mutually exclusive. In this example the survey was not done correctly, but it should be obvious that data represented in a pie chart should be percentages of the total, and the total equals 100%.

Secondly, keep it simple. The point of a graphic is to summarize. The graphic is not a narrative. When you look at your speedometer, you don’t want a narrative about how you slowed down when you passed the bicyclist and sped up at the yellow light. You want to know current speed in relation to acceptable norms. Speed dials are a great way to look at current KPI’s in the context of desired ranges.

You don’t need to know anything else about this product, but sales are not where they should be. Sales are close, but can be improved upon. The graphic instantly communicates what is vital. It answers the how are we doing questions at an aggregate level. To answer why questions more granularly a deeper exploration is necessary.

Lastly, know your audience. If you are the end user of your dashboard, then things are easy. But if someone else will be using your dashboard to monitor the state of things, make sure it is clear what is going on. Use titles that are informative, label your axis or metrics. Color is a great way to distinguish between categories. As indicated above red, yellow and green instantly convey what is good and bad when used appropriately.

Dashboarding is a great opportunity to add value by combining creativity and analytical savvy. A good practice is to gain feedback from peers and see if they can quickly interpret what you are trying to communicate. If not go back to the drawing board.

There is a growing host of software that assists in creating dashboards. Google analytics has a built in dashboarding feature. Also there is Tableau, Infocaptor, and Intelliview to name a few.

Here are a couple of articles and blogs that have helped me think of new ways to present data:

 http://flowingdata.com/ - (This might be my favorite)
http://www.information-management.com/news/10001129-1.html - (Best Practices) http://fivethirtyeight.blogs.nytimes.com/ - (Not about dashboarding, but good example of effective use)