Tracking Wildlife Trafficking
This is the final project for Visualization Technologies taught by Prof Dave Landry. We had to choose a topic of our interest, find a relevant dataset and create an interactive visualization for the same.
I was interested in exploring wildlife trafficking across the world and which countries can be held responsible. I am generally an animal lover, however, I got deeply interested in the topic after reading about in the book Poached by Rachel Love Nuwer.
I created two visualizations, the first one is a horizontal stacked chart which showed the top 10 importer and exporter countries. The second one is an interactive Sankey diagram which shows the distribution of which classes of flora or fauna are being most trafficked across the top 10 countries.
Look at the interactive website here.
CITES (the Convention on International Trade in Endangered Species of Wild Fauna and Flora, also known as the Washington Convention) is a multilateral treaty to protect endangered plants and animals. Its aim is to ensure that international trade in specimens of wild animals and plants does not threaten the survival of the species in the wild, and it accords varying degrees of protection to more than 35,000 species of animals and plants.
All legal international trade in CITES-protected species conducted in 2016 and 2017. The dataset is available on Kaggle here.
The dataset has 67162 rows with the following columns-
Appendix(I,II,III) Most Protected-least protected
Origin-Where the species comes from
Term(live, scales, horn)
Purpose(personal, zoo, breeding)
Source(taken from wild, born in captivity)
I started with sketching how the visualization will look like.
Data wrangling and development
I stated with using basic D3 references to create a Sankey diagram.
I was then tasked with massaging the dataset so that it meets the data structure of a Sankey diagram. A Sankey diagram needs a data-structure that looks like origin-destination-value. I achieved this in using R Studio. I grouped all the 'class' totals for origin countries and destination countries. I then sorted them to extract the top10 values for importers and exporters.
To make the visualization more meaningful, I added some trivia using the dataset. I accessed the taxon(specific name) and details like how and why are they trafficked.
Data Visualizations, Data Wrangling in R
R Studio, D3.js, github, Visual Studio, ggplot2, dplyr, Excel