Our project is about detecting the regions and spaces that mostly affected by Covid-19 in terms of socio-economic conditions. To do that, night-light data can easily be used.
Using night-light data in our project might provide useful insights for disadvantaged regions and people during the pandemic. It may help to employ new policy tools based on remote sensing data to deal with problems such as economic support, unemployment, etc. Plus, socio-economic support decisions for disadvantaged regions and the people can be made by the remote sensing data. This will allow policymakers to be more efficient when allocating these supports at the end of the pandemic or during periods of re-emergence.
We were inspired by the capabilities of remote sensing methods in order to help people during the pandemic. Remote sensing methods can provide lots of insights when analyzing the economic impact of Covid-19. In the very beginning, we had to make a decision about the type of remotely produced data. After a short discussion, we decided to go through night-light data since there is already literature about the relationship between GDP levels and night lights.
To do that, first, we wanted to use the Daily VIIRS mosaics. It would be nice to visualize mostly affected regions based on daily data. We were thinking to visualize lights from the first occurrence of Covid-19 so far. But then we have experienced some problems. First, daily VIIRS mosaics are huge. We do not have enough time to download and process all of it. Since there was a time constraint, we thought to use google earth engine. But we realized that these VIIRS daily mosaics were not included bt the google earth engine. After a quick search, we found monthly mosaics which is also provided by NOAA. Although the time dimension of these composites does not cover all the Covid-19 pandemic (the recent composite that we could find is from Jan-2020), it was still performing well for those regions who got affected by the disease in the very first (Wuhan, etc).
To deal with the nighttime light data, we decided to use the GoogleEarth engine. First, we uploaded the Monthly Nightlight Data to the GoogleEarth engine from here: https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG
The composites that we have used were cloud masked and the radiance values have undergone the stray-light correction procedure. GoogleEarth engines use JavaScript codes, so we wrote our codes in that JavaScript language. After uploading nightlight data to the GoogleEarth engine, we split this data set into 3 different parts. The first data represented by the red pixels show the higher income level regions based on the Average DNB Radiance Values (avg_rad). Similarly, the green regions represent middle-income regions while the blue one shows low-income regions. Then, we started our data from Sep-2019 and ended it at Jan-2020. Thus, we could observe the change in colors of pixels for those who got affected by the Covid-19 mostly all over the world. For example, in Wuhan, it can easily be seen that the number of red pixels is going down while blue ones are increasing. That is, Wuhan's socio-economic conditions affected badly by the Covid-19. It is easy to do similar analyses as well for different parts of the world with the application that has been improved by our team for this hackathon.
https://ngdc.noaa.gov/eog/viirs/download_dnb_composites.html
https://ngdc.noaa.gov/eog/viirs/download_ut_mos.html
https://fad_bet.users.earthengine.app/view/lights-and-covid-19
https://code.earthengine.google.com/?scriptPath=Examples%3ADatasets%2FNOAA_VIIRS_DNB_MONTHLY_V1_VCMCFG
Román, M. O., & Stokes, E. C. (2015). Holidays in lights: Tracking cultural patterns in demand for energy services. Earth's future, 3(6), 182-205.
Zhao, M., Zhou, Y., Li, X., Cao, W., He, C., Yu, B., ... & Zhou, C. (2019). Applications of satellite remote sensing of nighttime light observations: Advances, challenges, and perspectives. Remote Sensing, 11(17), 1971.