Away from the Crowd has received the following awards and nominations. Way to go!
Our project addresses the Human Factors Challenge.
We seek patterns between the COVID-19 spread and many Human Activity Based Datasets, such as the following:
Once established such pattern, we can predict future hotspots of the pandemic, as shown on our website: https://fightthepandemic.co/
When the Space Apps COVID-19 Hackathon was released, a group of engineering students from Rio de Janeiro was driven by the desire to make a difference and decided to accept the challenge.
After this, we decided the path we were choosing. We made a brainstorm session to decide our main problem. At this session, each member gave at least 3 ideas, and we choose the ones that made more sense for the group.
We decided that the lack of resources at hospitals was the biggest problem and we knew that a solution would be to perform a preventive approach with early lockdowns and resource allocation, for example.
Thinking about this, we came up with Fight the Pandemic, a platform that would use NASA’s Data to map the disease spread, helping governments and populations on its fight against the virus.
The team used population density, use of lights, transportation and touristic routes data to create the COVID-19 pattern code.
During the creation process of Fight the Pandemic the team used a lot of tools such as VS Code, to edit the code, CSS and HTML, to effectively code the website. For the spread model, QGIS platform and multiple python libraries were employed. All codes are available on GitHub.
Team “Away From The Crowd” used Canva, a graphic design platform, to create the Presentation Deck, and iMovie to create their Pitch.
In this hackathon, our team faced a lot of challenges, some of them were:
Among these difficulties, the most challenging one was our pattern code.
Besides all these problems, we need to celebrate our wins, and the bigger ones were:
To create the spread model, the team used data from:
2. SUS, Brazil Government’s Health Agency
To generate and treat data from NASA satellites:
1. GeoTIFF files extracted from NASA
2. Shape files from Brazilian Institute of Geography and Statistics