Money to Burn| Better Together
Better Together
Money to Burn: a visualization of the geographical distribution of wealth and pollution
Summary
This is a tool to allow the visualization of the inequity of the distribution of the burden of pollution which tragically makes it easier for some to turn a blind eye to its effects.
How We Addressed This Challenge
We programmatically overlaid a map of pollution (concentration of nitrogen dioxide in the troposphere averaged over 2014) on a map of wealth (mean wealth per adult in each country for 2019 in US dollars) to show where each are concentrated to let the viewer come to conclusions on their own by seeing where concentrations of wealth and pollution are.
How We Developed This Project
The visualization tool was realized by coding in Python in Jupyter Notebook. A heat-map of world wealth distribution was created using matplotlib, geopandas, and world wealth data. NASA's global pollution image was overlaid on the world wealth heat-map to obtain the visual.
The major problem we faced was to generate a world wealth heat-map. Getting the geometry pattern values to generate a world map required some digging into matplotlib's ability to generate a world map and then a heat-map. The other hurdle that we faced was in trying to get the heat-map overlaid on NASA's global pollution map, which required some trial and error in figuring out the dimensions of the map.
The major achievement was when the heat-map of the world came together. It was a "hooray!" moment as that was the image we generated ourselves!
How We Used Space Agency Data in This Project
We used a NASA NO₂ map to show where concentrations of pollution are. Specifically, a global map showing the concentration of nitrogen dioxide in the troposphere as detected by the Ozone Monitoring Instrument aboard the Aura satellite, averaged over 2014.
Project Demo
https://www.facebook.com/100001717806356/videos/3566612990072593/
Data & Resources
https://www.nasa.gov/sites/default/files/thumbnails/image/15-233-global.jpg
https://en.wikipedia.org/wiki/List_of_countries_by_wealth_per_adult
https://matplotlib.org/users/license.html
https://pypi.org/project/image/
https://pypi.org/project/geopandas/#:~:text=geopandas%200.8.,1&text=GeoPandas%20is%20a%20project%20to,geographic%20data%20to%20pandas%20objects.&text=It%20combines%20the%20capabilities%20of,to%20multiple%20geometries%20to%20shapely.
https://pandas.pydata.org/
https://jupyter-notebook.readthedocs.io/en/stable/