HEX MAP IN TABLEAU

Over the last few years, we’ve seen more and more maps that use hexagons. They have become “cool”. Why is that? Well, hexagons and other regularly shaped features allow you to normalize geography for thematic mapping rather than be constrained to using irregular shaped polygons created from a political process (for example, county boundaries, census tracts, zip codes, etc.). And this is VERY useful because of the massive disparity in some of these shapes.

For this article, I will speak only about the United States where hexagons are utilized to represent each state, but just about any shape could be used. Hex maps are simply tilemaps using hexagons. Hex maps have become the standard when visualizing data where the sizing of the geographical region is unimportant. One of the major problems with a standard map is that most of the Western states are larger in area and give the impression of more visual weight, when in fact Eastern states are typically more populous. For example, Montana has an area of 147,164 square miles and population of 1.05 million where New Jersey has one-sixteenth of the area of Montana (8,729 square miles), but nearly nine times the people (9.01 million). When using a standard map, however, the sheer size of Montana causes it to carry far more “visual weight” than the more populous New Jersey. A hex map removes that artificial nature of more weight being placed on larger states. There are other disadvantages of standard maps as well. First, the states in the Northeast are very difficult to see on a standard map because of their small geographical area. Because they appear so small on the map, it can be difficult to select or hover over these states and nearly impossible to place any types of labels. A hex map eliminates that issue because all states appear the same size. Simply put, hexagons are good for visualization because they nest together perfectly and look good. The linear patterns in the hexagons are not as apparent and the shapes “softer”, making them more attractive when you are able to see the shape outline. 

Assigning data to hexagons is pretty easy. You just need more detailed data than the scale of your hexagons and then you aggregate that data into your hexagons. You simply overlay your source data (for example, a set of points representing bridge locations) and a layer of hexagons, and then summarize the values that intersect your hexagons. This is very easy for point data, as you take all the points within a single hexagon and specify how you want to aggregate each field in the point data (maximum, minimum, average, count, etc.). If your data is represented as lines or polygons you can also overlay the data, but you should be aware that you are introducing some interpolated data into your results. Depending on the extent of your source data, you may want to do this aggregation at a couple of different scales. Then you can create multi-scale hexagons that turn on as you zoom in or out on the map, giving your end-users a more dynamic experience. They only see data appropriate at the given scale and never struggle to discern the data.

Business Applications :

Now, let us consider an example of comparison of Sales of a Pharma firm across the United States. The states with a darker blue shade are clearly the most selling ones while the lighter ones yield a much lesser sales.

Similarly, of Profit comparison across the United States. The states with a darker shade are clearly the most profitable ones while the lighter ones yield a much lesser profit.

The above hexagon map provides a clear insight and gives the dashboard a better visual appeal too. Hovering on any of these tiles will also display a tooltip with additional details about the state. In this case, it is the sales amount of the respective products.

For example, from the above map if we think that the neighbouring states of the highly profitable ones stand a high chance of profitability in the future, then it might not be the right kind of inference. As you can see, the states are not placed in their respective geographical positions. Some of the states have their positions adjusted in such a way that the size of all tiles are equal.

Bibliography:

1)    Visual BI(Feb 2018). Enhancing Insightful Data Visualizations using Maps in Tableau, Retrieved from

https://visualbi.com/blogs/tableau/enhancing-insightful-data-visualizations-maps-tableau/

2)   Kevin Flerlage(Nov 2018). What the Hex? (a brief history of the hex), Retrieved from

https://www.flerlagetwins.com/2018/11/what-hex-brief-history-of-hex_68.html

3)   Thematic Mapping with Hexagons(April 2015), Retrieved from

https://www.esri.com/about/newsroom/insider/thematic-mapping-with-hexagons/

4)    Sales data of USA-Hex Map on Tableau, Retrieved from

https://prod-apnortheast-a.online.tableau.com/#/site/anandkshirsagar/workbooks/136984?:origin=card_share_link