People may not be as big as elephants, whales, or gigantic trees but they are still the most “space dominating” organisms on the face of the planet. What do I mean by “space dominating”? I hypothesize that humans tend to conquer over spaces so drastically that they greatly affect the quality of living for other organisms. Sometimes the affect is so great that species can go through drastic loss of numbers and possible extinction. I decided to investigate my idea further and use Finder! and Maker! to help me.
I investigated to see if I could find endangered species numbers by country and came across the website of the International Union for Conservation of Nature (IUCN). The IUCN regularly puts out their Red List which is an intense listing of the conservation status of plants and animals around the world. The list sets guidelines to evaluate the extinction risk of thousands of different plant and animal species. Once their list has been created they showcase their results to promote conservation across the planet. I created a dataset in Finder! of this list to show the number of threatened (critically endangered, endangered, and vulnerable) species by country in the world. The list is also broken up by number of mammals, reptiles, birds, fish, mollusks, other inverts, plants, and total threatened species. The map below shows the total number of threatened species by country. (click on images for larger views)
Now I wanted to find more data to test my original hypothesis (Are humans the most ‘space dominating’). I found human population data by country and also area of each country in square kilometers. With this data I then calculated population density’s for every country in the world. I then took the total number of threatened species by country and divided it by the area of the country. This gave me a value that I call my, ‘Threatened Species Density Rate’. I then decided to correlate this new Threatened Species Density with the Human Population Density. For my hypothesis to be correct I would need to see that countries with high population densities will have high Threatened Species Density Rates because humans have not allowed the animals living there to have the resources to properly survive. Below is the correlation analyzed in Maker!
The correlation above shows a slope of .64 which is somewhat high and shows that a somewhat high correlation is found in countries that have high threatened species density rates and high population densities. So my hypothesis can be seen as being somewhat accurate. Let’s take a look at this point shown in a map on Maker!
To really see this point illustrated lets zoom in on NW Africa on a map of Population Density and Threatened Species Density mapped out together (The purple polygons represent Threatened Species Density and the Green Dots represent Population Density)
Here we see countries with low population density (Algeria, Mali, Niger) have lower threatened species density. Compare these to the countries with high population densities (Liberia, Nigeria, Ghana)that have high threatened species densities.
It seems that if you keep animals and plants free from humans, they will likely be able to survive properly. Not all countries obey this rule, but the rate around the world is pretty high. It is also interesting that many of the countries that have low population densities have areas that are very desolate and unlivable by human standards. These places include deserts or ice fields where extreme temperatures and conditions don’t allow humans to live but allow specialized species to survive that can handle all the extreme conditions. Maybe one day if humans find ways to survive in such places causing population densities there to grow, we might then see an increase in the threatened species densities there as well. We will have to wait and see.
Welcome to the Esri DC Development Center blog. We write about features of our work on big data analytics, open platforms, and open data, what is new and exciting in the Esri and community, and general industry thought leadership and discussions of geospatial data visualization and analysis.
Please explore what we're working on and let us know if you have any questions or ideas!
- Locations of "Unique" ESTC imprints at Penn amfraas
- Aggregation of Zip Codes for the USA into USA Counties by State with FIPS Codes JoeHair
- Total Data 2008-2013 acrumpton
- test Zillow, New York Neighborhood Boundaries, New York, 2007 gregfelice
- Sav.ServiceArea fadingdust
- Neighborhoods - SF Assoc. of Realtors, San Francisco, 2008 ebcounts