Maps are one of the most common data visualization forms we use today. Used to highlight geographical regions in data, like “countries/regions, states, counties, or postal codes,” maps help quickly identify the area that is being reported on and sometimes incorporate specific details.
Although once performed by hand, creating maps can be time-consuming and tedious, as they involve complex features and elements, such as scale and symbols. Therefore, to assist with processing data tied to geographical regions and account for the complexities, powerful tools such as Excel, Tableau, Datawrapper, and the like have been created. Datawrapper is a user-friendly, open-source web tool that you can use to create beginner to advanced interactive charts.
My first impression of this tool was that it is very user-friendly with a simple interface. After a few years of experience creating and displaying charts and graphs between Microsoft, Apple, and Adobe software, and with a little experience in Tableau, I was relieved to begin utilizing a tool designed with non-technical people in mind. Using Datawrapper’s 4-step process, I was able to create visually appealing charts, graphs, and maps swiftly– and it was an enjoyable experience!
Population Over Time
My first task was to create a chart to accurately depict the change in the population of three popular African countries, Nigeria, Ghana, and Cameroon, over the past 70 years. My primary inclination was to utilize a line chart to indicate change over time. This process was fairly simple.
To experiment further, I attempted to create a table to display the same information. This quickly became more lengthy and involved. due to the way tables display information compared to line charts, creating a table required a reorganization of my data. After reorganizing the data, I learned how to use sparklines to summarize the overall trend. Next, I inserted an additional column to indicate the difference in growth from 1950 to 2020 for all three countries. This resulted in a much more informational table.
Finally, I tried my hand at creating a map. However, the data I collected highlighted non-geographical patterns and supported analysis more than observation. In this case, I returned to the original data I collected (before cleaning) and cleaned again, seeking the 10 most populous countries in Africa in 2020, which could be displayed using a symbol map.
A symbol map is a great map for representing specific locations within a larger region. To make the symbols vary in size, I inserted a new column containing the 2020 populations of all ten countries and sized my marker shapes based on the data in that column. My map is responsive and interactive, communicating the population information through size and leaving the numbers out. Overall, these three visualization methods helped me further understand the importance of pairing the appropriate visualization with the dataset and details you are working with.
Maximum Temperature in a Year
Next, I moved on to creating another chart detailing the maximum temperature of the month in 2021 at the National Arboretum in Washington, DC. I decided to use another table to visualize this information. Although I wanted to focus on the day with the highest temperature per month, I found that the table required me to revisit the original data and add the data covering every day of the month back in. This made the table more versatile and highly effective, as it houses a line chart showing temperature patterns over time, the maximum temperature of the month, and the specific day of the month when the maximum temperature occurred. Additionally, I was able to incorporate a heatmap to highlight the highest temperatures and the lowest temperatures in different colors. As a result, the viewer can quickly differentiate the temperatures. The result was a stunning chart that shares a vast amount of data in an easily understandable format.
For additional practice, the type of map I attempted to pair with this data was a locator map. Locator maps are perfect for displaying the places you are mentioning within an article. They don’t communicate specific data points and numbers like temperatures in °F. They simply state. “this is where it is located” or “this is where it happened.” Since the National Arboretum is located in Washington, D.C., I wanted to display the Arboretum within the context of its home district.
Creating this map was straightforward and fun to play around with. I chose a tree as the symbol of the location marker and included an inset map of the District of Columbia so viewers could reference the surrounding area. In future locator maps, I would like to experiment with adding in multiple locations, utilizing 3D buildings, and incorporating annotations and a key. I have found that locator maps can be helpful and insightful for viewers because they provide unique context and may be easier to absorb than a paragraph of writing.
Maryland Vehicle Theft Within Counties I Have Lived In
Lastly, I created a line graph depicting the number of motor vehicles reported stolen within three Maryland counties between 2010 and 2020. Using a choropleth map, I highlighted three different counties: Montgomery County, Prince George’s County, and Baltimore County. To support the story behind my data (in a perfect world), I would be able to select a specific year within the 10-year range and observe the color change over time, However, choropleth maps do not work well for displaying the correlation between values or observing subtle numeric differences. Therefore, I revisited the possibility of a line chart to display this data.
Altogether, while using Datawrapper I gained a greater understanding that not all data related to a specific area or region works well in a map visualization. Furthermore, some forms of visualization are effective when the data is comprehensive while others get by with simplified, general information. The key takeaway here is that data visualization requires thoughtfulness and intention. And it’s okay to experiment with various visualization approaches to find the most suitable and accurate one.
If you’re looking for a visualization tool, Datawrapper is a great place to start! It is user-friendly and there are numerous resources online to assist with any questions that may come up. Moving forward, I plan on using Datawrapper lots more.
Sola's Thoughts 