Visual Analytics: Is Location a Distracting Character in Your Dataviz?
In my high school Performance Art class, our teacher, Mr. Kennedy, was constantly reminding us that when you have characters on stage the focus must remain on the central action. If you have non-speaking role then you cannot draw attention to yourself. For instance, you wouldn’t tap your foot, play with your hair or wave to the audience members. This concept can translate into a data visualization as well – especially with geospatial data. If the location isn’t the main character, then it might just be a distraction – much like Riley the Cat who was supposed to be a curious bystander.
Reviewing Geospatial Objects in SAS Visual Analytics
Before using SAS Visual Analytics, I only had PROC GMAP available to produce what I would call some fairly uninspired data visualizations. SAS Visual Analytics changed my capabilities with its 3 geospatial objects: Geo Bubble, Geo Coordinate, and Geo Region.
Each geospatial object has advantages over the others depending on what takeaway you want to provide the viewer. This proceeding graphic shows an overview so you have an idea of how they are different. In this four-part geospatial series – we will cover when to use geospatial objects and I will talk about each one and explain when it makes sense to use each.
Is Location Really a Part of the Story?
Earlier when I was talking about being on stage – I was alluding to the fact that when you start doing something that draws attention to your character – the audience might not realize you are not really part of the story. Is the same with geospatial objects, since SAS Visual Analytics makes it so easy to draw a map you might think it is part of the dataviz.
It’s up to you to review your data story and decide if location is really part of your story. Is geography a character – does it have a role or is this just something cool you can do? Let’s talk about when it makes sense and when it doesn’t.
Location as a the Main Character
If you want to show that your customers live close to your stores that’s a great reason because it helps the viewer understand the geographic distribution – it’s part of a story. In the following example, the map shows that more in-store purchases were from those who lived closer to the store. [This is from my SGF2015 paper about Tactical Marketing.] The bubbles plot ZIP codes and the size shows revenue. Maybe you can imagine this story explains where you want to spend advertising dollars. The geography is part of the story because the customer’s location matters.
Location as a Distracting Extra
Author Dona Wong (Wall Street Guide to Information Graphics) suggests that there are times when geography is not part of the story so it doesn’t make sense to force it to be. In her example, she shows two sales regions (Texas vs New Jersey) where sales were higher New Jersey than Texas. The problem is that regions are so disproportionate in size that comparing the total sales is not helpful and did not lead to any conclusion except that the Texas store was not generating as many sales. The story was about revenue not about where it was being generated.
I’ve recreated the example in the following graphic. You can see her point – we don’t know why Texas has so little revenue compared to the northeastern states. I can think of reasons such as there are less stores in Texas, the store only sells snow supplies, or maybe the store just opened. I think Dona Wong’s point was that the map doesn’t really explain anything and only invites confusion. What do we want the viewer’s takeaway to be? We have stores in 3 states but no one shops at the one in Texas?
Do Weather Events Seem Location Based?
For this blog series, I pulled the storm events data from the National Climatic Data Center website. This database contains US storm events (like tornadoes, thunderstorms) since 1950. In most cases there are other facts such as number of deaths or injuries and the estimated property damage. The tornadoes are rated by their intensity on the Fujita Scale from F0 to F5 with F5 being the most harsh. In 2007, the enhanced Fujita Scale was introduced and tornadoes were categorized as EF0-EF5.
Is Your Data Ready?
The storm event data already had useable geographic data – there was US state name and FIPS codes for state and county. Nearly all of the tornadoes had latitude and longitude values for where the tornado started and ended. The state and latitude and longitude can be imported to SAS Visual Analytics without an issue. Here’s an example of how the data look in SAS Enterprise Guide.
SAS Visual Analytics 7.1 and later accepts geographical data items as either name, ISO 2-Letter codes, ISO Numeric Codes, or SAS Map ID values. This varies based on what element you are using country, region, and state/province values are most widely accepted. When you assign a data item to Geography these are your choices. [Refer to the SAS documentation for your release for more details.]
Here’s an example from the SAS Support of how SAS Visual Analytics expects to receive that geographic data. You can use the SAS/GRAPH MAPSGFK library to join your data to a SAS dataset that has this values. For instance, I joined the storm event data with the MAPSGFK.US_ALL dataset by the state FIPS codes to get abbreviated state names.
You can also build a custom data item based on the latitude and longitude and it’s really easy! We will talk more about that in Part 2 of this series called Get to the Point.
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