Srikanth
Due to the natural disaster in 2017, it was estimated that 10,000 people were killed and 95 million people were affected. It makes a record-breaking year in natural disaster worldwide. 2018 is also more or less equally calamitous. With the introduction of big data, it saves many lives. As the technology is getting smarter day by day scientist and officers are analysing once untapped information. Big data analytics not only helps to predict the disaster path but helps in pinpointed flooded areas, mapping and making arrangments. Agencies can prepare beforehand with the help of big data analytics. Each disaster provides an enormous amount of data which is used for forecasting future disasters. Combined with sensor, surveillance and satellite image data collection, large data analytics allows critical areas to be surveyed and assessed. For example, a highly flooded area is known with the benchmark for analysis of future floods.
Flood patterns are to be detected by Google through AI. In 2011 horn of Africa was hit by the worst drought which occurred in 60 years. In spite of repeated warning signs, no one took the step forward quickly. With big data approach aspects like prevention, response, recovery and preparedness will be addressed. With the help of personal technology and wearables data generated is used to handle emergencies better. The data collected from smartwatches and mobile phone apps or connected devices will be analysed to prioritise rescue and response. Let us take an example to understand the situation when there are abundant calls for 911 during disasters, callers are to be identified by dispatchers are used to take the decision based on the urgency of the issue.
Large data not only address storage problems, but also issues related to accessibility, distribution, analysis, and visual presentation of data and effective analysis. This is a collection of methods and scientific tools and techniques that help in utilizing the best from a large number of available data. Big data is defined as a technology paradigm that allows researchers to conduct an efficient analysis of the vast amount of data available through current practice. However, large data allows researchers to carry out detailed analyses of all communications that provide valuable information that has general validity for the population in general; like information about disease outbreaks. More precisely, communication also requires understanding and monitoring all public bodies and openly available communications such as messages and content that is exchanged publicly on social media.
Areas in which big data is beneficial:
1.Identification of vulnerable:
This can be used as the most vulnerable population for natural disasters and data on these communities can be used to pursue the development of ‘risk information’.
2. Allocation of resources efficiently:
Focusing early warnings, assessing resilience and monitor recovery will be included in this process. Geo-informatics and remote sensing platforms generate big data that will help to identify gaps which give the suggestion regarding the allocation of resources.
3. Missing people are connected with their families:
During emergency situations social media giants Facebook, Twitter are helping the people for recovery and thereby reduces the recovery time.’Safety Check Service’ on Facebook is helping a lot of people during disasters and thereby reduces the risk.
4.Indirect Impact of disaster:
It provides migration effort and provides aid for indirect disaster impact which allows government for anticipating the indirect impact of reducing risk. For example with the data of rice crop being damaged during disasters, the effect of it on many sectors is to be estimated.
The application of big data is not as easy as chief information officer’s job, it includes chief executing officer job and chief marketing officer’s job. It would be more interesting to see if we can use it before a disaster strikes, potentially avoiding, or at least reducing, the effects of a disaster. applying big data for disaster relief, it would be interesting to see if we should start applying the same principles, we can use current technology to provide different, more efficient things where disasters have hit.
Pinpointing The Location:
With the help of location technology, emergency staff can exactly pinpoint the location of the disaster. The technology is similar to navigation apps for having a valuable effect. The best example id the recent incident of carr fires in redding, calif, real-time maps are used by officials to point out the exact fire spreading locations. In the case of unfolding emergency situations, this location technology is used to locate the exact location with the help of smartphones and IOT devices. In U.s last year 10,000 people are saved on time with the help of this location technology. Data and mapping technology, for example, can show respondents if the location of the caller can be accessed by car, on foot, by boat or helicopter, and allows them to plan the best way to save them.
During Hurricane Harvey truck driver was struck in rising water, he was not accessible by car and hence has to be freed with boats. The data about the depth of the surrounding water is known using data, measurements and mapping technology which can’t be recognised with the naked eye and is almost impossible to know the street in the floods. It matters not only the quantity of data being produced by connected devices, all the data produced is to be analysed and managed properly. Right data management strategies will help the government to be prepared beforehand during the disasters.
Early warning signs of floods, tsunami, volcanoes will be more accurately known with the help of this huge amount of data. Cloud computing, cybersecurity also plays a vital role to get the required information for this emergency management.
