9. Symbol Maps

Overview

Symbol maps provide much more granularity than fillable maps we showed in a previous lesson. Learn how to create symbol maps in this lesson.

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Summary

Symbol maps

- Used for plotting individual locations rather than regions
- More flexible than fillable maps as both color and size can be applied to categorize data points
- Typically rely on columns such as City and Zipcode to plot individual data points

Map layers

- Tableau contains mapping layers that can be added to your visualization
- These layers (e.g. Place names) can provide great geographical context for users
- Always experiment with mapping layers when using symbol maps

Geocoding addresses

- To practice geocoding addresses, a number of online tools are available
http://www.gpsvisualizer.com/geocode is one of these tools, relying on the Google Maps API
 

Transcript

As we saw in the previous lesson, field maps are pretty easy to create in Tableau because Tableau can easily identify the columns such as state, province, or county, to dynamically create these maps for you without worrying too much about geolocation. However, sometimes you'll want more granularity than just comparing large geographic regions. And for this type of visualization, you'll need to use symbol maps. In this particular case, I want to view the customer base in the context of the tri-state area.

So instead of using state as my level of detail, I'm going to use postcode.

When I do this, Tableau immediately realizes that a field map is not possible. And instead, automatically switches to a symbol map. However, the shape is still a field map.

Let's switch this to a circle.

And now we have all of our customers on a map that we can examine in more granular detail.

I'm going to color code each customer by salesperson.

I'm going to size each dot by annual revenue.

With these changes in place, let's now zoom in on the tri-state area and see if we can do some analysis. So I'll simply zoom into this area and let's zoom in a little further, With our map now created, we can actually compare the performance of salespeople within this particular region. So let's select Palacios who tends to be a high performer and compare him against, say, Gilman.

And so you can see how much more customers Palacios has than Gilman in this area. Sometimes if you want to go even more granular, you may want to include some additional map layers to make the map a little easier to read. To do this, let's go up to the map drop down and go to map layers.

And while a couple of these are selected automatically, I'm going to select streets and highways and I'm also going to select place names. And again, this gives my map a lot more context. So again, if I'm very focused on the New York area, I can select my zoom function, zoom in on this area.

And as you can see, my place names include Brooklyn, Manhattan, Yonkers, and White Plains.

As we did with the XY scatter plot, it's nice to list either selected customers or salespeople on the right-hand side that can interact with our visualization. Off-camera, I've created a new dashboard which includes our map of the tri-state area and the revenue for each salesperson.

If I want to compare performance by salesperson on the right-hand side of this map, I simply select all of these individual customers.

And in a very nice interactive feature, my chart updates on the right-hand side. And as you can see, Palacios has more than doubled the revenue of the nearest salesperson in this part of my map. Now, it's all very well and good having the postcode for every single customer in the US and enabling Tableau to do all the heavy lifting around geocoding.

But what happens if you don't have postcodes for example in a country like Ireland? Well, in this scenario, Tableau won't be able to generate the geocoordinates automatically for you and you will need to use some geocoding software such as Google Maps, and these software programs simply convert an address into a value for latitude and longitude.

Latitude and longitude can then be uploaded into Tableau which can then generate the map for you.

If I go back to my sheet, you'll see that Tableau actually autogenerates latitude and longitude from postcode. And when you have these columns in your dataset, they'll simply appear down here as a measure. There are many other options you can explore with maps including connecting different points on a map to create routes, layering custom background images behind maps, and even generating your own custom boundary areas with shape files. All of these advanced mapping options will be covered in a later course on advanced visualizations. For now, if you can recreate the field maps and symbol maps that I have shown you in these last two lessons, you should be able to really impress your manager and your clients with these excellent visualizations.

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Tableau Essentials
Creating Visualizations in Tableau

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