"Space apps challenge" is a contest of high expectations. For this reason, our team decided to work accordingly. Having as the main purpose "to identify patterns between human activity and COVID-19 cases and factors that could help predict hotspots of disease spread" we chose to tackle the challenge by using data from space-based assets before the pandemic in order to see the correlation between physical determinants and the real-time COVID-19 data, as we have described in our project slides. We identified as the first factor the NIGHT LIGHTS. We got advantage of the data provided by NOAA. Through the satellite image of light pollution we are pleased to spot the places with a high concentration of people and also socio-economic status differences across areas which emit less or more light. Furthermore, AIR QUALITY is the second factor we identified and is really important to us. Utilizing the data from NASA Nitrogen Dioxide, Carbon Monoxide and Ozone maps we succeeded in recognizing an important factor which leads to the air pollution so in the same time to respiratory problems. In these ways we make clear how NASA, ESA, JAXA, CNES and CSA datasets and resources can be used to face the challenge. Our project is rendered as a pioneering effort which tries to incorporate for the first time new ideas such as the consideration of light emission/pollution.
https://covidcoeus.wordpress.com/
Health is our most precious possession. To avoid COVID-19 or other viruses spread, humanity should know the mechanisms that predict hotspots of disease spread and also to identify patterns between population density and COVID-19 cases. Our team chose to be on this side of the virus treatment. On the side of prediction and recognition of those patterns that lead to pandemics. Moreover, as soon as the pandemic started, we saw a huge difference in the spread and severity in different nations, especially the difference between Asian and Westerns countries. Such a huge difference motivated us to find the possible factors which resulted in the whole world in the present situation. Are the factors purely human/social or does nature also play a significant role? This specific question fascinated us and we desired to figure it out with the aid of open data.
Our main goal was to make full use of the data provided by NASA, ESA, JAXA, CNES, and CSA. This is the reason why we chose to mainly include our project scientific map data from NASA. Another very important tool that we took into consideration was machine learning. In a world that computational technologies are constantly developing it was almost impossible not to take advantage of the helpful machine learning and data visualization. The first step to our effort was to find the appropriate data in order to decide on our idea.The span of January 2020 was taken into consideration for data. Then, after the consideration of the valuable information, we started “building” our proposal to the challenge. We concentrated on different atmospheric maps in order to see if air pollution plays a significant role in the virus spread and effects. Lastly, we thought about a feature that has never been included as a hotspot prediction in people’s thinking: Light Pollution. Python,OpenCV was used for detecting the brighter areas in the image,Google earth engine was used for data processing and visualization.
The truth is that we faced a lot of difficulties as a team in order to complete the project. One of them was the lack of direct cooperation and communication. Most of the members do not come from the same country or even continent, so it was quite difficult due to the different time zones to reach an agreement. Secondly, one big challenge for us was to find both innovative and new ideas that will provide a previously unknown method for identifying the patterns. On the contrary,even though we were from different regions,we enjoyed being in a team.We utilized the time difference as different shifts for different person to work.It was full of cultural diversity as well because we speak different languages.Even though we faced difficulties ,existed the spiritual pleasure and satisfaction that research and teamwork offered us. It is a great experience for all of us.
This is our website https://covidcoeus.wordpress.com/
1. Night light data
NASA: VIIRS nighttime imagery in Worldview
VIIRS Stray Light Corrected Nighttime Day/Night Band Composites Version 1 by NOAA : https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG?authuser=1
2. Air quality data