Customizing your symbol map

After importing the data for your symbol map, you can now choose the data for size and color among many other options on the refine, annotate and design tabs in step 3: Visualize. In this tutorial, we'll walk you through all these steps to create the following map:

Refine: Select axes

In the Select axes panel, you have the option to tell Datawrapper which columns you want to have represented as the size of the shapes and their colors. You can leave them empty if you want to, or can just choose to define one of them. For our map, we'll choose columns to define both, size and color. We want the circles differently sized depending on the population numbers, and we want them to be differently colored depending on the change since 2011.

Refine: Appearance

In the Appearance panel, you can decide on the overall look of the map:

  • Symbol shape: Here you can decide which geometric shape your symbol should have. Squares, Diamonds, and Hexagons are great options for clustering, as we'll see later. Triangles (up and down) can be used to show a development (e.g. a city with a shrinking population). And markers are especially great if you don't choose a column for "size"; meaning if all your symbols are exactly the same size. 
  • Maximum size: This option defines how large the symbols should appear on the size. You can try around a bit to find a good solution: Too small, and it's hard to see them. Too big, and they're overlapping each other too much. 
  • Map padding (%): With this slider, you can decide how much whitespace you want surrounding your map. 
  • Make map zoomable: That makes...well, the map zoomable. You will see that this feature has been enabled if your map gets a grey minus & plus button at its top-right corner.

Refine: Symbol Colors

In the Symbol colors panel, you can choose which column you want to apply color symbology to. If you didn't choose a data column for the color, you'll see a simple color picker. Click on it to define which color all your symbols should have.  If you did choose a data column for the color, you'll be able to control a color palette:

Color Picker: Here you can choose between seven different color palettes. The four palettes at the top are great if your values go from low to high (eg. population, unemployment rates, life expectancies). The three palettes at the bottom are useful when the extremes in the data are most interesting. E.g. if you want to compare your regions with a national/global average, and want to draw attention to the regions that are the furthest away from this average. 

Colors menu: 

  • With Reverse, you can turn around the color palette you chose.
  • Discrete turns the gradient into specific colors that are visually clearly separated from each other. 
  • With Import, you can import your own color palette in form of multiple hex codes (e.g. '#ffffe0', '#ffe0a9', '#ffbe84'). With Export, you can export your current color palette in the same format. That's useful if you want to create multiple maps with the same color palette: Export it from one map and import it to your other maps.

Stops: This feature will (simply speaking) increase the contrast of your maps; especially if your data has a lot of outliers. The number of stops is the number of (equally big) parts on our color palette which cover the same amount of our values – and because that's not intuitive at all, we wrote an extra article that explains color palettes (incl. stops) in more detail.

For our map, we choose a circle with a maximum size of around 35. We want the reader to quickly identify the cities that shrank since 2010, and colors are perfect for that: All circles with a "change" value smaller than 0 will be shown in red; all other values in green. 

Refine: Clustering/binning

If you have too many points on your map, especially in one area, the map quickly becomes chaotic. Your reader will have a hard time drawing conclusions from such a packed map. The clustering feature exists to help out with such a problem. By default, it is turned off. But once you activate it (with choosing either "cluster overlapping symbols" or hexagonal binning"), you'll find that this is a powerful tool on its own. 

So what does clustering do? It puts multiple map points in "bins"; it clusters them. For example, we have lots of smaller cities in the Los Angeles area (41, to be exact). The hexagonal binning sums them all up in one hexagon. 

By default, your symbols will be circles. Choose "hexagon" as symbol shape in the "Appearance" panel to create a hexagon map.

Which color options do we have for these hexagons? Clustering is not made (yet) to visualize the number of points in one hexagon. But we can use the "Color" feature in the Symbol Colors to sum up variables that we have for all our location points. 

Imagine we map all Walmart stores in the US and set the size of each store to show their yearly revenue. Because there are thousands of stores, it's hard to see the pattern in the data. There would be many tiny symbols; some slightly bigger, some slightly smaller. But that's a perfect use case for the Clustering feature. When we cluster our Walmart store map, we can tell Datawrapper that it should summarise the yearly revenue in the bins. A hexagon on this map has a darker color if the revenue of ALL the Walmart stores in this hexagon is unusually high.

We can't sum up all data like this. Our population-in-biggest-US-cities map is not made to be summed up like this since we only show the biggest cities – and not all of them. The data we cluster and summarise needs to be complete (all Walmart stores, all cities, etc.). 

We're planning to write an article to explain this tool better. For now, the best way to learn more about this panel is to play around with it (try to zoom in and out your map, while doing so). 

Refine: Map key

Datawrapper will automatically create a map key for you, and locate it at the bottom-right of your map. The Map key feature lets you customize this key:

  • Title: Here you can define what will stand above your map key. Pro tip: HTML works here, so a <br> will result in a line break. 
  • Automatically generate legend: This option is checked by default. If you uncheck it, you get the option to create a custom map key. We'll tell you everything about that feature in this extra article. For our map, we also build a custom key:
  • Number format: If you imported percentages without the %-sign or have giant numbers that you rather see written as "30M" instead of "30.000.000", then this feature helps you to fix that. 
  • Position: Here you can move the map key to any corner of your map. That's often a purely aesthetic decision, depending on where your map gives you space. 

Refine: Tooltips

A click on "Customize Tooltips" will open a pop-up window, in which you can find the columns you imported as blue buttons on the top-right corner. Click on them to integrate them in your tooltips. Here too, we have an article that explains how to add tooltips to your map and points out good practices.

Annotate: Describe chart

If you've created a Datawrapper chart or map before, you already know this feature. Here you can give your map a title, a description, add notes and a source:

  • We recommend choosing a title that highlights what's interesting about the map – the one key statement that you want your reader to remember about this  map  E.g. "Unemployment highest in the south"
  • The description should have as much information about the data as possible: What do we see exactly? E.g. "Unemployment rates in % in all US states, 2016"
  • Think of notes as footnotes, where you want to clarify any abnormalities about your data. E.g. "California unemployment rates from Jan and Feb 2016 not included in the calculation."
  • The source name will give your readers information about how trustworthy your data is. Does it come from a government institution or another trustworthy organization? The source URL lets your reader dig even deeper and have a look at the underlying data. Whenever possible, make sure that you provide source name and source URL for your data to increase transparency. E.g. US Bureau of Labour Statistics, August 2017.


In this last tab, you can decide in  which  layout your chart should be published. Should it come with the Datawrapper layout or in the custom design of your organization? 

You can also change the Output Locale for your map. This affects the language of the attribution in the bottom left of your map and defines decimal and thousand separators as well as translation of month and weekday names.

Lastly, you can enable  Social Sharing here. If you do that, the share buttons for Facebook, LinkedIn and Twitter will appear on the top-right corner of your map. 


Once we have worked through Refine, Annotate and Design in step 3: Visualize, we can now proceed to step 4: Publish & Embed.  The best way to use a Datawrapper chart is by  embedding it directly on your website. To do that, click the big blue button that says "Publish chart". Then, copy & paste the embed code snippet into your website or CMS. 

You can also  download your chart in two formats. Users of all subscription plans have the option to download their chart as a PNG. Custom and Enterprise plan users also have the option to download their chart as a PDF or SVG. Click here for more information on the different pricing plans of Datawrapper.

You can find more detailed explanations about the color palette, tooltips and custom map keys in other articles.

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