As people interested in data processing and data, in general, we decided to proceed with this challenge to see what we can do with NASA data sources regarding pollution and air quality.

Gintautas - data engineer

Algirdas - visualizations

Dominykas - data transformations and algorithms

Ultimate project goal is to have a universal data framework and even some very scientific things built on top of it, like correlation coefficients calculation between any two features or time series forecasting/interpolation capabilities. Because IT MATTERS! Having quality framework you can extract value out of data so much faster (illustration of blended data sources on the map)

We quite quickly realized that our main sources of data are "AERONET" and "POWER Data Access Viewer" and did some data engineering magic described there https://github.com/Rayvid/nasa-data/blob/master/README.md

Correlation coefficients calculation API (our "data science" part of work): https://github.com/dominykastunaitis/nasa

Geo JSON helper API: https://github.com/Rayvid/nasa-geoinfo-api

Visualizations: https://github.com/Rayvid/nasa-data-visualisations