Representing digital data by hand is only one option when it comes to data visualization. Another is to use a digital data visualization tool, such as DataWrapper.
DataWrapper lets users create charts, maps, and tables. Using it, I was able to create examples of the three different types of maps it can create. This includes choropleth, symbol, and locator maps. Each of these map types will be explained, and an example will be given. The chosen topic of each map is based on my own interests.
A choropleth map uses color to represent data across regions. For example, I created a choropleth map of the world using data from Google Trends to show which regions had the highest search rate for the term “LGBTQ.” While the original data covered more regions, I simplified it by focusing on the top ten regions that searched for the term most. I chose a choropleth for this topic because it clearly shows which regions search for the term most. However, the colored regions themselves do not provide enough information. That is why I made the map interactive, so that when the viewer hovers their mouse over each region, they can see exactly how many searches for the term “LGBTQ” were recorded in each highlighted region.
A symbol map uses symbols sized and colored according to the data. For example, I created a world symbol map that shows symbols for all areas with higher favor toward same-sex marriage. The data used on the map was from Pew Research. I chose a symbol map for this topic because it was a simple way to pinpoint where favor for same-sex marriage is high. The symbol I chose was small spikes, because even at a distance, this made the different areas easy to see. The map is interactive, so the user can also zoom in to see the map better. To provide context, above is a short explanation of the map, and underneath is a key with a percentage of the country’s favor toward same-sex marriage along with the coordinating color to be as clear as possible.
A locator map uses markers to show where something is located or occurred. For example, I created a locator map of the US that shows the locations of all the states with the highest levels of Bigfoot sightings, based on data from Kaggle. I chose this map type for this topic because I thought that location marks on a map would be a good way to represent this data. While the original data included sightings from nearly all 50 states, for simplicity, the map focuses on the top five states with the highest number of sightings. The number of sightings for each state is also provided beneath the map graphic. Each of the five states has a number in a circle on the map; the numbers count down the states with the most sightings, starting with Washington, which has the most. Above the map is a short explanation of what the map is, and below the map is a key that also shares the exact number of sightings for each state and explains what the map is.
By themselves, each of these map types would not make sense. Each one needs more information and context. Providing explanations and keys for each of these maps gives the necessary information that can’t be conveyed by the map alone. While this could be done traditionally with a pen and paper, using digital tools like DataWrapper makes it much easier.