Editor’s note: Data Map Discovery is an occasional series that aims to help you learn how to use maps of all varieties to improve your data analysis. Tableau Researcher Sarah Battersby will showcase various types of mapping visualizations and outline how to build them in Tableau. You’ll learn how to answer specific questions with spatial data, learn when maps should and shouldn’t be used, and get detailed tutorials on how to do more with your data maps.
Point distribution maps are a great way to understand the spatial distribution patterns of your data. In order to make sense of these types of patterns, you’ll need to have enough space between the individual point marks so that you can clearly see where one cluster of data starts and another ends. What can you learn when you can’t even see the base map because you have so much data?
A great way to start simplifying the visual representation of complex point data is to spatially aggregate the points into polygon regions so that you can look at groups of data instead of the individual points.
For instance, consider this super simplified dataset of taxi cab pick-up locations in Manhattan. What can we learn from the dataset in the left-hand visualization? Even though I’ve trimmed the dataset down from the original 175 million records to a mere million records, the main takeaway that I see is that there are a lot of taxis in Manhattan! If we aggregate the data into polygon regions, we eliminate the problem of overlapping point marks and make it much easier to see the spatial variation in the right-hand visualization.
So, how can you do this? There are two basic ways that you might want to aggregate or bin your data:
- Using regular polygon bins like squares or hexagons allows you to explore the distribution of the data in regions that are the same size and shape on the map, making it easy to directly compare them to each other.
- Using irregular polygon bins, such as census tracts, countries, provinces, or sales territories, allows you to explore the distribution of the data in regions that have meaning to your analysis. A great example of this is if you want to know how many customers and what the average purchase amount is in each of your sales territories.
Let’s take a look at these ways to bin your data, their pros and cons, and how you can do this with your data in Tableau.