The Global Footprint Network is an international non-profit organization that strives to build towards a sustainable future of society. This group of scientists and professionals utilize their data to help shape private sector decisions, public policies and educate humans to better understand our anthropogenic impact and how to reduce it. GFN is gracious enough to provide an Open Data Platform giving access to data points for over 200 countries and their Ecological Footprints. Based on the multi-line plot above, the world is not doing so great in terms of using more than it has. This project is conducted to visually compare the ecological impact of individual countries/regions and determine which resources are most detrimental.
I originally downloaded the dataset from Kaggle and also downloaded the Public Data Package to supplement my existing dataset. The Public Data Package also included PDFs describing in depth descriptions of the dataset, calculations involved and various definitions. My working dataset consisted of 188 countries. I also had access global calculations which consisted of 7 data points, each being a region of the world (i.e., Asia, Middle East...). Both datasets contained the same variables. In addition, Public Data Package included time series data for the overall world ecological footprint and biocapacity measurements from 1961-2012. Lastly, in order to create the world map, I utilized a mercator geojson file located here. However, some countries did not coorespond in both files so I painstakingly altered country names in the geojson file. The altered geojson file is located here.
In order to completely feel the impact of this project, it is important to understand some key definitions. These terms are also the specific variables that are utilized for the visualizations.
Global Hectares(gha): A hectare is a metric unit of square measure, equal to 100 ares (2.471 acres or 10,000 square meters). Thus, a global hectare is a biologically productive hectare where the resulting calculation takes into consideration the proportionality of capacity in the respective hectare. Doing this creates a unit of measurement to easily compare biocapacity and ecological footprints for different resources.
Ecological Footprint(gha per capita): area of land and water required for country to regenerate its needs from resources and absorb the waste from that consumption.
Population: (numeric) population of country in millions
GDP per Capita: (numeric with $) gross domestic product output per person
Biocapacity(gha per capita): An ecosystem’s capacity to regenerate what people demand from those sources. Provided to compare what the area of land can sustainably provide as opposed to what is being forced out of it. The columns are the same as Ecological Footprint excluding a Carbon Footprint column.
Biocapacity Deficit or Reserve: How well the country is doing (Total Biocapacity - Ecological Footprint).
Number of Earths Required: Number of Earth’s required if the world population lived the same way the respective country/region lives.
Number of Countries Required: Number of country/region’s Biocapacity is needed to breakeven with that country’s/region’s Ecological Footprint.
Cropland: Areas of land used to produce food and fibre for human consumption, feed for livestock, oil crops, and rubber.
Grazing Land: Areas of land used to raise livestock.
Forest Land: Areas of lumber, wood and timber products used per year.
Fishing Ground: Calculated based on estimates of the maximum sustainable catch for a variety of fish species.
Built-up Land: Area of land covered by human infrastructure
Carbon Footprint: Area of forest land required to sequester carbon dioxide emissions from burning fossil fuels
Note: The biocapacity and ecological footprint per capita is calculated for each land type. For example, there is a variable for Grazing-Land Biocapacity as well as Grazing-Land Footprint.
Below is a combination of interactive visualizations that make it easy to explore all the attributes of a specific country or region. The dashboard was created using D3.js and includes a geospatial world map, horizontal neg/pos bar chart, scatterplot with encoded coloring and a sideways grouped bar chart. Now, how does it work? To make things easy, we will call the lower three charts "c1", "c2" and "c3" from left to right and the map will just be "map".
The data does not lie. We are using much more than we have. The multi-line plot at the top of the page shows that in 1970, the world's ecological footprint starts to exceed its biocapacity and continues to do so at an alarming magnitude. Technology can only get us so far in increasing our biocapacity. North America has the largest biocapacity deficit with Latin America with a large reserve. Most all other regions operate with a deficit. Individual countries with a large deficit theoretically require multiple Earths to sustain a world where all countries had the same lifestyle. Not surprisingly, a country's GDP per Capita is highly correlated to the amount of Earths required. In other words, the footprint is largest where most of the money is at. For most all countries and regions, the ecological footprint, when broken down into land type categories, will most likely show that Forest-Land constitutes the largest proportion. Forest land accounts for not only lumber and timber areas, but also the area of forest land required to sequester carbon dioxide emissions. Evidently, CO2 emissions are detrimental to the earth due to its warming effects and the data does not lie when it explains that this Carbon Footprint is massive. As I read more into the repercussions of our actions on the planet, in addition to the political climate we are in right now, it is extremely important that people get their facts right and are given accurate data to draw opinions from. The Ecological Footprint that we have on this earth is real and people should understand that this land can not sustain human life as long as we continue to live this way.
Lance Fernando is a junior majoring in Data Science with a concentration in computational analytics. When he is not diving deep into data, Lance enjoys playing music and cycling. After graduation, he hopes to get accepted into the Master of Science in Analytics program at USF to continue his studies in Data Science.