Our solution seeks to solve the lack of accessibility in information, caused by the difficulty of understanding and analyzing spatial data, in an instinctive way, of simple understanding. Where spatial data will already be associated with meaningful information for users, it will already be explained according to the local situation of each consulting user.
The solution will make all data analysis accessible, inclusive, where a high education or technical language will not be necessary to be able to interpret data meanings, so even a child can understand and read a data properly in his day by day
Thus becoming an exclusive application, with a very high level of data analysis, deep learning and a very low level of difficulty of understanding.
Governments will be able to predict actions against pandemics, in addition to knowing all the impact caused in their territory in real time, crossing with data from ministries and what is most important for the most accurate and cohesive decision making.
Our team was inspired to choose this challenge because we know that with a good use of data, we have achieved extraordinary results. Our approach was to use spatial data to monitor population movement through lights, NO2 and CO2 levels released in the ozone layer of planet Earth. We use NASA APIs, AWS, Jupyter Notebook, Kaggle and Pynton.