Durham Dropouts - Space Apps Challenge

Durham Dropouts| Light the Path

Light the Path

The COVID-19 pandemic initiated changes in human population movements and activities around the world. Your challenge is to use Earth observations to explore how human activity and regional land-based human movement patterns may have shifted in response to COVID-19.

Analysing NO2 to evaluate and monitor quarantines.

Summary

We examine the change in NO2 levels over Spain mainly, but also UK; before, during and after the main outbreak. We then attempt to process and analyse the data in conjunction with COVID information to find out more about how people move before and during quarantine.

How We Addressed This Challenge

By looking at NO2 levels, and it's variations, we hope to learn more about how people move during quarantine and the possible implications and applications of this information. By comparing before/after images of NO2 emissions, we can estimate areas of high activity travel. We can also use the data in conjunction with other information (social, geographical, etc.) to help differentiate between essential and non-essential travel by area (by week if use averaging). We can therefore use this data to:

Investigate the effectiveness of different quarantine methods employed by different countries
Use information on a day-by-day basis to help inform decision makers on the effectiveness of quarantine rules
We can help prove quarantine works (despite the detractors) by comparing COVID-19 and NO2 data!
By improving upon our current work with NO2, and extending to other data types we originally intended to include (CO2, Night lights), we can get a more improve the accuracy and analysis of our findings

Using these points we are confident that our project's idea meets the principles of the challenge.

How We Developed This Project

We originally wanted to follow changes in migrant movements due to COVID-19. We then realised this was too difficult. We were then interested in night light data. We realised this also went way over our heads (weird formats, huge data, lack of experience). We basically didn't know how to access the data and how to handle it once we got it for most of the project. It was a mess. We were also focusing on NO2 data in parallel, as a backup, and also because why not. Since this was the only data we managed to handle, we decided to proceed with it.

We used Python and NodeJS to retrieve and process the data. We then layered our processed data over maps and created animations to help us visualise the changed in NO2. We had to take into account issues with cloud cover resulting in missing NO2 data, which was our main limiter in terms of data. But taking everything into account, we were still able to develop and plot decent graphs from this information with the time we were given, and come up with use-cases.

I think we all realised we weren't really suited for this project, but we enjoyed the challenge and learning process.