Insights into Editorial: Role of Big Data In Disaster Management
India and South Korea recently signed agreements covering a broad range of areas, including cooperation in development of big data technologies for their diverse applications, like in disaster management. Big data can play a significant role in enhancing our capabilities to manage disasters.
Undoubtedly, the age of big data has opened new options for natural disaster management, primarily because of the varied possibilities it provides in visualizing, analysing, and predicting natural disasters.
From this perspective, big data has radically changed the ways through which human societies adopt natural disaster management strategies to reduce human suffering and economic losses.
In a world that is now heavily dependent on information technology, the prime objective of computer experts and policy makers is to make the best of big data by sourcing information from varied formats and storing it in ways that it can be effectively used during different stages of natural disaster management.
Benefits and Importance of Big Data Storage in Disaster Management:
Big data is defined as the technological paradigm that allows researchers to conduct an efficient analysis of vast quantities of data that is made available through the current practices.
It is the collection of scientific and engineering methods and tools that help in making the best of massive amounts of available data. Big data addresses not only storage issues, but also issues related to accessibility, distribution, analysis, and effective visual presentation of data and analysis.
More precisely, communication also entails understanding and monitoring the entire body of public and openly available communication such as messages and content that is publicly exchanged on social media.
In such situations, people may be exchanging messages in reporting their condition to their loved ones or making appeals for help. However, big data allows researchers to conduct a detailed analysis of all communications which provides valuable information that has a general validity for the population at large; such as information about a disease outbreak.
Global Institutions to study Disaster and Risks:
In 2015, the UNDP partnered with the Tohoku University and Fujitsu decided to create a Global Centre for Disaster Statistics (GCDS). The aim is to gather and crunch ‘big data’ to meet the ambitious targets of the Sendai Framework to reduce the risks from disasters.
Fujitsu’s cloud-based ecosystem captures data from a variety of sources, including unstructured sources like social media, high-resolution satellite imagery and drones.
Specialised technical institutions like the Tohoku University can crunch and analyse these data sets to provide insights for policymakers about the impacts of disasters. This includes helping to monitor recovery, focussing on early warning, and assessing resilience.
Other Areas that can able to reap benefit:
- Efficient Allocation of Resources: Big data generated from geo-informatics and remote sensing platforms help to identify the gaps and makes the recommendation on where to allocate resources to mitigate the risk. This includes helping to monitor recovery, focusing early warning and assessing resilience.
- Identification of Vulnerable: It can be used as most vulnerable population for natural disaster and data on these communities can be used to pursue ‘risk informed’ development.
- Connecting missing people with their Families: Companies like Google, Facebook are interested in helping communities during emergency situation by reducing recovery time.
Facebook’s ‘safety check service’ is an example of this noble initiative where they help to connect people to their loved ones during and after disaster in real time.
- Indirect impact of Disaster: It also provides aid in indirect disaster impact and its mitigation efforts, thereby allowing govt to anticipate indirect impact of disaster and reduce risks.
g.:- It can shows how devastation of rice crop by a disaster can adversely affects the several other sectors such as rice trading, packaging, retail, transport.
Big data has now become a crucial element of communication, which complements the conventional exchange of intentional and explicit messages; such as first responders talking over a voice connection; or an announcement of a text message through which warning is given to citizens faced with the threat of an approaching natural disaster.
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.
Big data has a significant role in all phases of disaster management. Big data from sensor networks, social media, and from other sources are available and shows its usefulness in disaster management already.
These big data help policy makers and first responders to come with quick and concrete decision on the number of people affected, type and nature of the damage and where to allocate the resource. Many natural hazard forecasting systems rely on big data.
Early warning system for tsunami, storm, forest fire, and flood can be more accurate and reliable from these huge volumes of data. Crowdsourcing, cyber infrastructure, and cloud computing approach can be used to get required information for emergency management by analysing big data.
Machine learning approach and parallel processing approach might save valuable processing time during an emergency. The big data archive can be helpful for model development and validation to ensure more efficient disaster management.
Despite having these challenges, research and data gathering on the usefulness of big data in disaster management are ongoing. The disaster managers and policy makers will get more confidence on the usefulness of big data in disaster management.