Disaster Management

KEY POINTS

·        Big data - Big data can be defined as the collection of data sets which are large and complex and beyond the capacity of the conventional processing system.

·        Efficient data collection, archiving and analytics are essential for effective disaster management.

·        Big data analytics will dramatically cut down on the number of people impacted by disasters.

·        Collecting data on most vulnerable populations can be used to pursue ‘risk-informed development’.

·        For instance, road infrastructure can be built by calculating the intensity of floods and determining the types of materials needed to construct durable roads.

·        Data also help identify the gaps and makes recommendations on where to allocate resources to mitigate risks from disasters.

·        For example, flood-resistant roads can only be constructed if governments consider and review data about flood risks.

·        With such information, they can allocate appropriate funds for better road construction.

·        Big data also provides a deeper understanding about how an economy is interconnected: how devastation of a rice crop by a disaster can trigger a chain impact across several industries and services, such as transportation, rice-trading, packaging and retail.

·        With such valuable information, governments can anticipate disasters and reduce risks through preventive measures such as early warning systems, safety drills, and resilient infrastructure.