To do that we go down the encoding options and see that symbol is the second best option and we therefore encode that information by adding a triangle to distinguish them from the circles. Now the question I often get is, “shouldn’t I also use color to indicate which store is which?” For that we need to think about perception and ask ourselves if it’s easy to distinguish a yellow triangle from a yellow circle. That answer often depends on the amount of data, which in this case is probably above the limit of easy recognition. Instead it’s probably easier to find the stores and look at the color of the cluster they are located in.
After this, you could also add on size etc. Hopefully this gives you a good example of how to combine the different encoding options when using points.
One of the issues with just using points is that it can rarely show you the relationship or comparison of quantitative values for three or more variables. For that we can use a representation often called a glyph or an icon. In this example, we are using a glyph to represent the position of our stores and adding the info on which zip codes are within close distance to each store. So, by using the line encoding, we can for each zip code draw a line to every store that is within 15 min drive to see what options the people living there have.
As you can see, most zip codes only have one store as an option, but if we start zooming in on some areas we can see that some zip codes have lines to two or more stores. Now this would be hard to represent if we just used an area layer and colored it by the store as we would sometimes have multiple colors in a zip code. That’s when we can start thinking about using a glyph.
The next step after glyphs is to represent additional data values on a location to start doing comparison, composition etc. on a location to find insights. One of the more usual methods is to place a pie chart on a point. In the example below we’re looking at house prices in the different areas of Sweden and using the pie chart to see the difference in housing categories. What we’ve done then is to add size to the pie chart to show off how many sales are happening in each area. This allows us to both compare the different segments as well as see which area has more sales.
This method can also be used with other charts such as bar and line chart, just make sure the charts don’t overlap each other. It’s preferable to avoid using this method when you have many small areas.
As you can see, when you have location data mapped to a point there are many methods to represent that point! But whatever you do, make sure to avoid some of the crazier methods, such as Chernoff faces in a map.
Check out some of the GeoAnalytics examples yourself at http://www.qlik.com/us/products/qlik-geoanalytics.