Content Developer Associate at almaBetter
Natural disasters are growing at an alarming pace. Since 2016, we have witnessed several natural disasters, such as earthquakes, wildfires, etc., which resulted in Global Economic losses of billions of dollars.
Authorities have been working relentlessly to tackle these unfortunate realities and protect the people, especially those close to the equator.
Developing countries are expected to bear a tremendous toll from extreme weather disasters. As these countries are still evolving, they don’t have the facilities to withstand such disastrous events. Fortunately, these countries have a ray of hope with the advancements in technology, especially in Data Science. In this blog, we will discuss the role of Data Science in disaster management and how it can save millions of lives.
Data and analytics can significantly support relief and response initiatives for deprived communities. The possibilities offered by Data Science are endless. Data Analytics can do everything from utilizing satellite images and crowd-sourced mapping tools to predicting and preparing for disasters. Government and non-government organizations are leveraging Data Analytics as a critical technique to build early warning systems and aid relief methods in the aftermath of an unfortunate event.
A recent survey explored data and analytics to support economic and social development, including disaster management. The survey was conducted among Data Science professionals across the public, private, and NGO sectors to fetch the best results. This survey was crucial and proved that one in every three respondents estimates Data Analytics to be very effective in developing disaster management techniques. The other half expects substantial headway in amplifying early warning systems through such tools.
Several aid agencies are building dedicated project teams and resources to figure out how to best apply insights from data disaster planning and execute it efficiently. Intelligent use of data directly benefits preparedness and response effectiveness and assures that the people in tremendous need receive help.
On the other hand, several technological and global experts criticize the insights provided by disjointed data. According to these experts, disconnected data insights have hampered efforts to maximize the potential of these resolutions. For example, according to Carlos Castillo, a research professor at Barcelona’s Universitat Pompeu Fabra, “when there are information holes, it can lead to chaos in a crisis.”
Another critical issue is the lack of information which can cause delays in response times, which may bias aid distribution. Meaning more help will be provided to the communities where more data exists. As a result, the more vulnerable communities with little or no information might suffer from insufficient support.
The good news is that Big Data is a huge helping hand, and advancement is being made to collect comparable data from all societies. The application of data and analytics for responsive disaster management has evolved extensively in the past decade holding high hopes for the future. Another recent survey proved that pattern tracking would enable adequate climate resilience and disaster management planning over the next five years.
Some great examples include drone footage and satellite mapping, which are highly beneficial tools to identify which communities are in need immediately after an unfortunate event. Another helpful technique is hyper-local Data Analytics which identifies vulnerable population hotspots, such as the location of schools, hospitals, and elderly communities, to assist in moving the population to a safer place.
Natural disasters are inevitable; however, with advanced technologies such as Data Science, we can prevent or help out the communities in need after such unfortunate events. If you are interested in learning more about Data Science and its prospects to help out in managing disasters, then check out AlmaBetter’s Full Stack Data Science program. Our program offers the finest, industry-relevant, and reverse-engineered curriculum to aid students in staying ahead of the curve.