EO Dashboard Hackathon

Community Space One | Storytelling in the COVID-19 Era

Storytelling in the COVID-19 Era

The longer human activities are changed due to the COVID-19 pandemic, the more visible, wider, and longer lasting the scope of environmental impacts may be. Your challenge is to tell a visual story about the impacts of the pandemic using Earth observation data and other complementary information.

When the buzz goes eerie silent in Asia's largest slum

Summary

Our story highlights the challenges of slum dwellers particularly in Dharavi during the pandemic. Slums display poverty. They display lower sections of society and, especially in India, are unhygienic. We bring out through our story the subtle connection between pollution in Dharavi which happens to be very close to the airport with the variable airplane restrictions during this time of uncertainty. Studies have shown a direct link between pollution and the mortality rate. We have tracked the migrant worker crisis. We also bring out the possible future positive impacts of this uncertain time.

How I Addressed This Challenge

We developed a visual story depicting the impact of COVID-19 on Dharavi. We feel that the wellbeing of slum dwellers should also be ensured especially during difficult times such as COVID-19. We feel that Dharavi, being the largest slum in Asia, is an important region that must not be overlooked. Dharavi has one of the highest population density in the world which makes maintaining social distancing impossible to maintain. Most of the people living in Dharavi work as daily wage workers and run small enterprises. These were the hardest hit occupations during COVID-19 which further compounded the COVID-19 crisis in Dharavi. The situation was further aggravated by pollution from the city and the adjacent airport. Our story analyses the overall dynamic in Dharavi and how these dynamics affected the spread of COVID-19 in the region.

How I Developed This Project

The plight of the slum dwellers who faced a lot of challenges during this pandemic and also the swift action taken in later phase of the lockdown that led to Dharavi being successful in COVID containment. This inspired us to share the story with others with analysis and visual presentation through Remote sensing. Our approach was more creative because this had to please and actually inform the end reader. We have tried to keep it the least textual as possible and more visual in order to allow easier absorption of the data.

We have used ESA Open Access hub to initially work with the Sentinel data. Then we used the API service via Google Earth Engine platform to access the Sentinel 5P NO2 data to find out the pollution. We have mainly used Java to code. Then we have deployed our data on a website along with the GIFs of the data imported.

Our foremost problem was as high school students we don't have knowledge of advanced programming. We first worked in python without much success then with google colaboratory again without success finally we used Java and google earth engine to import the data. We had one mentor guiding us through the journey but all of the work being done by us. Our achievement is actually getting things to work without knowledge in some areas here.

How I Used Space Agency Data in This Project

We used Sentinel 5P data for NO2 pollution graphing over a the pandemic to gauge the effects. Secondly we used Planet.com data for the Airport difference comparison GIF. The NO2 data was really the backbone of the whole story where it was used for comparison.

Project Demo

Demo Video

Website link

Earth Observing Dashboard Integration

Our solution is currently deployed on a website. As high school students, we might not have the level of expertise needed to make it highly professional but we have created a site with the data. Its just a story of a part of Mumbai so it can easily be integrated with the EO Dashboard when created with the Vue app which we are confident that if required to we will learn and develop along with some assistance.

We have one important source from where we imported our remote sensing data manually which can be automated if required.

This is the NO2 Mumbai import code we used.