Learning Datawrapper
Moving forward in my data visualization journey, I decided to explore the online tool Datawrapper, which allows users to upload data sets and create various graphs, charts, and maps. This week, my focus was on map visuals. Datawrapper offers three different types of maps that may suit your data, and I tried all of them, so you don’t have to.
Hi! My name is Kristin Ardese and I am a professional Graphic Designer and Marketing Strategist. I hope that by sharing some of my expertise, I can help offer valuable insights and build an engaging community.
Choropleth Map
The choropleth map displays data using color-coded regions, making it ideal for visualizing information such as unemployment rates, election results or population distribution. When I think of a choropleth map, my mind immediately goes to US Election results. Therefore, I chose to use US counties as the region map for the 2020 US election results dataset.
Adding my data was the next step. After uploading my Excel file to Datawrapper, I had to match my data with the corresponding areas on the map. This involved selecting the data columns that would be used to represent specific parts of the map. Your choices will vary depending on your dataset, so I recommend starting with one of Datawrapper’s practice slideshows to get familiar with the process.
With the dataset is matched and verified, it was time for the fun part— playing with the visuals! First, I had to choose the data column that my map would represent through color. For the election results I used the “Rep minus Dem” column. I opted for a blue to red palette, that illustrated the density of democratic versus republican votes. The “Visualize” section of Datawrapper offers several other options to experiment with, depending on the type of data you are visualizing.
Once the data is appropriately designed, you can proceed to publishing and embedding the map according to your specific needs.
Symbol Map
Next was the symbol map which is essentially a bubble chart placed on a map. This map will represent data through symbols that are sized and colored according to the dataset. Datawrapper suggests that this map works best for specific locations such as cities. To experiment with the symbol map, I used data describing historical world battles and their casualties. The steps for the symbol map were similar to those of the choropleth map. First, I had to choose my specific region, which, in this case, was not so specific. Because this was international based data, I opted for “world” as my map option.
After importing, matching, and verifying my data, I was ready to proceed with visualization. Users can choose between several different symbols including circles, squares and triangles. Following that, I had to select a column of data that would determine their size. I decided on including a size legend that would show how many casualties there were in a battle based on their mapped circle size. Next, I had to choose the colors for each circle or battle. Finally, all that was left to do before publishing was to add a title and description. The final result was visually appealing and relatively easy-to-understand visualization. However, in the future, I might consider using less data and smaller regions, to make the color matching process easier.
Locator Map
The Locator Map was the last option remaining for me to try, and I decided to have some fun with it. Unlike the previous maps, data was not required, as it relies on markers and points. For my own amusement, I chose to use places that I frequently visit near my university. After importing my desired addresses, all that remained was to select the symbols and colors for each point. The result was a responsive location-based visualization that is both simple and easy to navigate.
Final Comments:
Through each map visualization, I had the opportunity to sharpen my skills in data interpretation, design, and storytelling. While none of the maps I created were flawless, they provided valuable insights into the practice of digital mapping. Practice truly does make better, and I am excited to include Datawrapper as another valuable addition to my design toolbox.
