The purpose of this challenge is to use remote-sensing data and satellite images to help form a better understanding of societal trends as affected by COVID-19. Your challenge is to discern how human activity in communities of minority, low-income, tribal, and indigenous populations have changed as a result of COVID-19.
The lifestyle of Low-income tribal communities is contrasting when compared to Urban Life. Likewise, the onset of a pandemic has affected the livelihood of these minorities across the world drastically and are often overlooked by the world. Dedicating more satellites over under-represented regions to obtain information such as changes in agricultural yields, exports/import transportation, domestic mobility, internet traffic, employability stats etc. These values can be analysed and compared to obtain behavioural traits and effectively manage the spread of the virus. Team Eco-StarX has sourced available data and created a model to further implement the solution onto the EO-Dashbaord
We have developed a comprehensive data table extracted from the EO dashboard and other renowned sources (ref.). Low-income countries like Sudan, Bangladesh, Togo, Tanzania, Malawi, State of Amazonas-Brazil(Vale do Jawari), Mentawai Islands(Indonesia), Palawan tribes (Philippines).
We feel that the above statistics provide an in-depth analysis of how these countries react and respond to a pandemic. More precise data we obtain, better solutions, and responses can be generated to minimize the impact on its people and the economy.
Our project mainly focuses on gathering various data related to low-income countries and tribal societies and how these countries were affected by the Covid-19 pandemic lockdowns. A comparison of data was also done for these countries for pre-pandemic and post-pandemic situations. It mainly gives us the information on air quality, GDP, CO2 emissions, Internet % of the population, Gini Index, Literacy rate, Population, Unemployment, total (% of the total labor force), Covid cases, Vaccination, etc. of these low income and tribal countries. Many of such countries' data were unavailable in the EO Dashboard. By gathering and showing these data on the dashboard, we can focus more on such countries where most of the facilities fail to reach this kind of situation. Using this data NGOs and organizations such as WHO can provide better amenities to these communities and help them overcome this situation.
We would like to initiate the analysis using specific parameters and hope to obtain more data from these low-income regions. Following this, it can be implemented in the EO dashboard and open-source websites for other government and private agencies to predict mass behaviors of the population.
We could notice that the low-income classified countries didn’t have response strategies to cope with the pandemic. So we wanted to analyze how the covid cases, vaccinations are related to other country parameters. So for this purpose, we obtained various data like GDP, CO2 emissions, Internet % of the population, Gini Index, Literacy rate, Population, Unemployment, total (% of the total labor force), Covid cases, Vaccinated, Testing to compare for the countries: Sudan, Bangladesh, Togo, Tanzania, Malawi, Brazil, Indonesia, Philipines. For analysis, we used Excel, Python, and SAS JMP to perform data analysis to obtain useful insights. Our main aim to understand the correlation of the country indicators with the Covid cases. For example, obtain the literacy rate and internet usage rate correlation with vaccination rate. In generic assumption, higher literacy and internet users must have a higher vaccination rate. And if the case is vice-versa in a country, we must highlight the scenario. Such analysis will help in government or relevant authorities to build better response strategies for the pandemic.
The impact of Covid 19 has been massive throughout the world. The COVID-19 global recession is the deepest since the end of World War II. Based on the graphs below, we can notice that despite the GDP forecast being 2.9%, 2020 witnessed a contraction of 3.5%.
The low-income countries are facing a dual hit, with health and economic crisis. During 2020, the onset of a pandemic, the low-income countries had fewer resources to protect themselves. Based on the published sources of data, we have collected the information on various metrics as follows:
Despite this factor, the low-income countries have managed to sustain their GDP at a constant rate.
AGRICULTURE
It can be evidently seen how agriculture has improved and deteriorated during 2020 all over the world. Mainly, in some parts of Africa and Australia, the agricultural sector is marked poor.
Around 255 million jobs were erased in 2020, due to pandemics. Based on world reports, approximately 95 million people are expected to have entered extreme poverty ranks. To understand the impact of Covid on every aspect of country growth, we have taken the following parameters:
1. CO2 emissions (million tonnes)
2. Internet (% of the population)
3. Gini Index
4. Literacy rate (%)
5. Population
6. Unemployment, total (% of the total labor force)
BANGLADESH
Based on the Trading Economics report, around 2 million educated people are added to the unemployed list every year. Despite the population, literacy rate, and Gini Index remaining constant across both the years, the unemployment rate has increased by 1.1%.
TOGO:
Togo is a great example of ensuring survival amidst the global recession. Despite the global financial meltdown, Togo has ensured ensuring growth and protecting jobs. The country has witnessed a small rise in its GDP. There has been job growth in the mining and agricultural sectors.
TANZANIA
The major issues while analyzing Tanzania reports were that, many of the published data were either outdated or unavailable. The country didn’t publish the covid cases and testing data. However, the World Bank report states that the tourism and international trade market was affected heavily due to travel bans during 2020.
MALAWI
Wholesale, retail, transport, and accommodation services were the worst hit in Malawi during the pandemic time. Education was also affected due to the sudden closure of schools and the country also didn’t witness any migration or increase in the internet user percentage.
SUDAN
Sudan government took measures to respond to the rise in Covid cases during March, with travel restrictions, closure of schools, lockdown, etc… 2020 had witnessed almost 3 times increase in internet users in Sudan. The enterprise survey results have shown heavy losses witnessed by them. There was inflation affecting the retail market. Family production units were also heavily affected by the crisis. About one-third of households were unable to perform normal farming activities during the outbreak. A substantial share of households experienced an income loss.
INTERNET USERS
Perhaps, due to lockdown leading to work from mechanisms and E-learning, countries like Bangladesh, Tanzania, Sudan have witnessed an increase in internet users during 2020.
The following is the data of Covid Cases, Testing, and Vaccination throughout the World.
For the Low-Income Countries, the covid cases recorded have been displayed via the heatmap for 2020 as follows:
CHALLENGES:
1. Many countries don’t have the facilities to acquire and publish data regarding covid 19. We could ascertain that countries like Tanzania have stopped publishing the covid cases, testing results during May 2020. The published reports also state that these countries have not been able to purchase the vaccines due to affordability issues.
2. We could also note that there were no accurate published reports of CO2 emissions to compare the mobility data with this indicator. Through this data, we wanted to ascertain if the lockdown in those countries helped curtail greenhouse gas emissions.
3. We wanted to analyze the impact on few tribal regions like
a. Mentawai Islands (Tribes)
b. Palawan- Tribes
c. Vale do Javari
However, these tribes are a part of bigger countries like Brazil, Indonesia, and the Philippines. The data obtained for the country as a whole is not representative of this particular region, as these regions are marked low income, while the country is faring well.
The parameters that we took into consideration such as C02 emissions, population density, air quality, Covid19 reports, etc. Most of the figures and data were used from the Nasa and Eo dashboard. Data concerning air quality was majorly taken from the Nasa satellites.
Presentation link: https://www.canva.com/design/DAEimJC8iBI/EzEQh5Bkq1XDpzirRfYyPw/view#3
Currently, the Earth Observing Dashboard has some data on Health, Agriculture, Air, Water for some countries. Even in those countries, it has only for few regions and not for all. Based on the data that we have collected, we could wish to integrate additional filters like Low income, High income, and Medium.
Also, our dashboard would focus more on low-income countries with additional parameters like:
1. CO2 emissions (million tonnes)
2. Internet (% of the population)
3. Gini Index
4. Literacy rate (%)
5. Population
6. Unemployment, total (% of the total labor force)
7. GDP
8. Mobility data
Apart from heatmap and CSV table, there will also be a display of correlation matrix and co-efficient table to show the relationship between the indicators and covid cases rise/decrease.
https://knoema.com/atlas
https://data.worldbank.org/indicator
https://www.iqair.com/air-quality-monitors/air-quality-app
https://www.internetworldstats.com/asia.htm#bd
#social impact , #low-income , #air quality , #waterquality #agriculture #covid analysis #data analysis #comparitive analysis #sudan #bangladesh #malawi #togo #tanzania
This project has been submitted for consideration during the Judging process.