The Art of Transforming Data into Great Stories
What happened last week in the world of data visualization? This category provides you with remarkable visualizations – they can be remarkably beautiful, remarkably interactive or just remarkably interesting. Visualizations differ on so many levels, and so does their content. Let’s take a look at what week #4 brought us.
NYC Street Trees by Species
New York City is known for its Central Park. But that is by far not the only nature in the city. Jill Hubley created an interactive map plotting the location of trees across the city’s five boroughs. By providing not only the locations, but also the sort of tree, she demonstrates the richness of green spots in the Big Apple. This is also supported by a tree-filter according to the species.
Looking at the map NYC seems to be one big diverse forest. While Brooklyn is dominated by planet trees, Queens is ruled by sweetgums. Hubley created a visualization that is easy to understand and easy to interact with. The user can zoom into the city, select tree species and discover patterns by his own. The manual for the selection tool works smoothly. We’re missing a search field, allowing users to search for a specific street or neighborhood. While this could be an interesting addition, it’s just a little downside. The choice of color is more arguable as some of the tree species’ colors are not easy to detect due to a low contrast to the dark background. But all in all Hubley created a functional, appealing map about a very specific topic. Thumbs up!
Pattern in city and town names and their endings
How things speed up in the world of data visualization can be seen at this week’s recap. Moritz Stefaner published a visualization on his data blog ‘Truth and Beauty‘ displaying spatial patterns of German town and village names’ endings. Just a few days later, a similar visualization by Chris Roth popped up that is based on the original one. The main difference lies in the interactivity that is possible in the second version, whereas the original consists of static visualizations.
Credit goes to both authors for making their code public, stating where the data is coming from and drawing attention on possible weaknesses of their visualizations. Whereas the original maps are easly understandable, the actual interaction of Roth’s map underlies a bit of a learning curve. Roth introduced an option that allows the user to search for common cities’ endings, and he added the possibility to search also for mutual syllables. Additionally, he included a Swiss and Austrian map. Even though neither Roth’s nor Stefaner’s visualizations are groundbreaking, we wanted to stress out what kind of renewals and ideas can emerge thanks to a transparent handling of data, method and code.
In the air
And we’ve got more on spatial distribution. Our third example runs in line with the ‘First Worldwide Air Map‘. As the title already implies, the content of the map is about air quality across the globe.
The landing page of the site is based on a world map displaying the degree of air pollution around the world. Unfortunately, there are no country names displayed, which makes allocation of pollutions a bit more difficult, if you’re not so geologically versed. But for some cities, mostly based in Europe, the U.S. as well as India and China, more specific information is provided. Unfortunately, the indexes of the additional city information are not further explained, hence are a bit confusing. But in any way a symbolic cloud with a happy or a sad face gives a clear indication about the air condition.
The chosen visualizations leave some questions open, however, the map and figures still provide information that are useful for everyone who quickly wants to check on air condition on a global scale.