USING COVID19, OPEN DATA AND PYTHON PROGRAMMING AS AN OPPORTUNITY TO BRING GEOSCIENCE PROJECTS TO STUDENTS

The best way for students to learn and sustain interest in geosciences is to undertake practical projects. It allows them to merge their interests, be it in programming, big data, anthropogenic impacts, or policy advocacy with geosciences. On 11 March 2020, the World Health Organization declared Covid19 a pandemic. Countries around the world rushed to declare various states of emergencies. Canadian provinces also implemented some emergency measures including closure of schools to check the spread of Covid19. I used this opportunity to study the changes in seismic vibrations registered in Canada before, during, and after the lockdown using data from land-based seismic stations. I analyzed continuous seismic data for 6 Canadian cities: Calgary and Edmonton (Alberta), Montreal (Quebec), Ottawa and Toronto (Ontario) and Yellowknife (Northwest Territories). These cities represented the wide geographical spread of Canada. The source of data was seismic stations run by the Canadian National Seismograph Network (CNSN). Python and ObSpy libraries were used to convert raw data into probabilistic power spectral densities (PPSDs). The seismic vibrations in the PPSDs that fell between 4 Hz and 20 Hz were extracted and averaged for every two weeks period to determine the trend of seismic vibrations. The lockdown had an impact on seismic vibrations in almost all the cities I analyzed. Except for Ottawa, the seismic vibrations decreased between 14% - 44% with the biggest decrease in Yellowknife in the Northwest Territories. In the 3 densely packed cities with a population of over 1 million - Toronto, Montreal, and Calgary, the vibrations dropped by over 30%.

To enable other students to undertake similar projects for their cities, I created a comprehensive online training module using Jupyter notebooks available on github. In a couple of hours, students can learn about seismic vibrations, how to obtain datasets, and analyze and interpret them using Python. They can share their findings with local policymakers so that they become aware of the effectiveness of the lockdown imposed and are better prepared for lockdowns in the future. It shows that when we make data and technology accessible to all students can undertake curiosity-based learning.