ishikagiroti / data-analysis-on-impact-of-early-warning-system-on-natural-disasters-over-the-years

This project was made as a part of the 'NASA Space apps challenge-2021' where the challenge my team and I chose was 'Measuring the value of Earth Observations'.

Data Analysis Report on Impact of Early Warning System on Natural Disasters Over The Years

This project was made as a part of the 'NASA Space apps challenge-2021' where the challenge my team and I chose was 'Measuring the value of Earth Observations'.

We have used the datasets provided by the 'United Nations Office for the Coordination of Humanitarian Affairs' to do our data analysis which has given us a clear view and helped us in spreading awareness of how the early warning systems, the advancements in technologies and ways we use satellite data have helped save lives and decreased the impact of the disasters.

1) Data Analysis of casualities during Natural Disasters

Inference

• In figure 1.1, we can observe that all the years present in top 10 are amongst the 1900s, when the early warning facities were under-developed as compared to the recent years, i.e 2000s as shown in Figure 1.2.

2) Data Analysis of people injured during Natural Disasters

Inference

• In figure 2.1, we observe that 2004 is the highest year with the most number of injuries, this is because of the Indian Ocean earthquake and tsunami, making it one of the deadliest years in modern history.

• Similarly, the year 2010, despite of it being a recent year with more number of technologies that gave early warnings, the earthquake in Haiti and the Russian summer heat wave caused multiple injuries and fatalities, giving it a high rank in the list.

3) Data Analysis of damage caused due to Natural Disasters

Inference

• In figure 3.1, we observe that most of the damage costs are high in the 2000s due to more developed and mordern infrastucture which increases the total cost as compared to the 1900s. Another factor that effects the damage cost is the increasing population in the cities over the years.

4) Data Analysis of people left homeless due to Natural Disasters

Inference

• As seen in figure 4.1, the top 10 years are amongst the 1900s. One main reason of that are the poor infrastructure facitilies as compared to the mordern times. The infrastructure failed to withhold itself in the course of a natural disaster, leaving people without a home.

• Figure 4.2 indicates the lessening of homeless people due to natural disasters around the world in the modern years with better infrastructure facilities.

5) Data Analysis of people affected by during Natural Disasters

Inference

• In figure 5.1, 2002 is the highest in the list due to record-breaking rains, triggering devastating floods in Europe, destruction of homes across the Caribbean and life-threatening mudslides in India, Nepal and Bangladesh.

• Similarly, as mentioned in the analysis of the number of people injured due to natural disasters, the total amount of people affected in 2010 is high due to the earthquake in Haiti and the Russian summer heat wave.

• Following the year 2010, the graph declines wrt the amount of people being affected due to the development in early warning technologies.

Libraries used

• Pandas
• NumPy
• matplotlib.pyplot