The visualization is divided into two sections: On the left, the interaction section allows the users to click on the circles (each of them representing a animal group); importers and exporters information will then be displayed on the bottom area. On the right side, more information is shown about the selection.
User can exploit a circle and go deeper into trade sub-groups. The visualization has been simplified to only show up to 3 depth levels. At any time, the user can go back by clicking on the breadcrumb controls on the top left corner.
Importers and Exporters information shown below the circles allows the user to see a bar chart of the top three exporters and importers in the selected category.
The right area shows the information about the current selection, including the animal name, scientific name, an explanation of the purpose of trade for the whole animal group and a donut chart with the total number of trades on 2013 and specific purposes of trade.
In certain cases, trade notes are displayed showing not the general purpose explanation but a specific one for the selected wildlife species as shown below.
The donut chart allows the user to understand why people are trading these animals all over the world. In certain cases, further explanations are shown below the donut chart.
Leather: Large and small manufactured leather products including belts, bike saddles, watch straps, furniture, suitcases, etc.
The data represents legal wildlife species trades reported annually to the UNEP World Conservation Monitoring Center (UNEP-WCMC) by CITES Parties. A CITES Party (currently 178 states) is regulated by a legal framework that tries to control international trade for threatened and potentially threatened species. Thus, each trade needs a permit issue to the Party Management Authority.
The database purpose is to identify where trade might adversely affect wild populations, evidence infractions and bolster creation of new policies.
Information about the database:
The visualization data is based on the species traded on the year 2013. The data excludes records that suggest units of measures to not mix distinct forms of measurements and just represent whole animals. For instance, 1kg of feathers may be collected from more than one animal. Data needed to be pre-processed to group animal classes by similarity to reduce the taxonomy complexity.
The data can be downloaded from the CITES Trade Database webpage as a CSV file. However, the website restricts the amount of records that can be downloaded on a single query so a public user can't download all the millions of records at once. There is no information publicly available that suggests in National Geographic obtained the records through another channel or the visualization team had to perform multiple queries to the online database. Finally, CITES provides a well- explained guide to using their database.
|Year||App.||Taxon||Class||Order||Family||Genus||Importer||Exporter||Importer reported quantity||Exporter reported quantity||Term||Unit||Purpose||Source|
The simplicity of the visualization suggests that its target audience is the General Public, with the objective of showing complex transactions made in legal wildlife trade and try to point the origins of illegal trafficking networks. Furthermore, understanding why these animals are traded can change the general public perception.
Based on Isabel Meirelle's approach to describe visualizations in the book Design for Information.
The visualization uses the area of the circles to represent the amount of trades in 2013 per wildlife group. Strictly speaking, the sizes don't match the real size they need to have. For instance, comparing Reptiles and Corals, Corals trades represent a 12.88% of the Reptiles trades. Thus, its radius should be 0.2155 inches instead of 0.8645 inches. However, if we apply the same concept while comparing Reptiles to Sea Cucumbers, the Sea Cucumbers radius would be 0.0004 inches making that element just too small.
When exploring sub-groups the same issue arise and it can be perceived that a threshold is being applied animal sub groups so under it, all of these groups have the same circle size (even though one circle may have 38 trades and other 1500 trades).
The visualization designers chose good colors for representing the groups. The dark green background gives and idea of ecology while the spatial positioning of the circles does not suggests any ordinal relationship between their colors. There is uniformity in the way the color is shared with the total trades donut graph, section title, bar charts and breadcrumbs. Furthermore, changes in saturation (and introducing a new pale green color), help the user differentiate areas of interest when selecting purposes. Overall, a good usage of color encoding.
The user needs to select or hover over a circle to know with what data he is dealing. Sometimes, he could deal with a lot of circles as shown on the image on the left side, so in order to find an specific animal, at worst, he has to go through all the circles.
Given the complexity of the whole complex operations involved in animal trading and existence of illegal trading, the numbers of the data set are just a good approximation to what is really happening. Even for this visualization, certain trades have been avoided if they were reported with specific units of measurement.
Further information like why numbers between and importer and exporter in a trade are different would be interesting so it could be displayed on the donut chart.
Also, the database does not mentions anything related to illegal trading.
Monkeys and apes represent the biggest group of mammals traded (aprox 1,360,311 in 2013). The main purpose of its trades is medical and scientific study specially for their cognitive and emotional capacity. It is interesting that Peru exorts almost all their Emperor tamarin monkeys to USA. On the other hand, USA is the biggest exporter of Rhesus macaque even though this animal mainly lives south east Asia.
Alligators and Crocodiles generates billion of dollars to the international commercial and fashion industries. Almost 8.5 millions trades were made on 2013.
Regarding to snakes, even though it is mentioned that their skin is used for medicine in Southeast Asia, the main importers are European countries (with around 83% of the trades).
The visualization is overall a good approach for understanding a bit of what's behind animal trading. However, their main target seems limited to General Public as more specialized audiences may need more tools to improve searches, relate taxonomies, an indicator of how threatened is a species is and so on. A good complementary visualization could focus on the flow of trading between countries.