Johannesburg, 16 Oct 2017
With the US and the Caribbean reeling from the double blow of hurricanes Harvey and Irma, and Mexico recovering from a massive earthquake, the awareness of disaster management has been raised to a new level. What is worth noting here is the advantages that big data and advanced analytics can offer to emergency services before, during and after a natural disaster occurs.
Before a disaster occurs
According to Aneshan Ramaloo, Senior Business Solutions Manager at SAS, the effective use of big data and analytics to study data related to geography, population, mobile-device usage and more can help authorities discern underlying patterns and associations that will enable the relevant services to quickly react to floods, fires and other deadly scenarios.
"Emergency response agencies can even model potential disasters and their effects, to allow authorities to develop proactive plans for preventing this from occurring," he says.
This can be achieved by utilising data from synthetic global population with detailed demographics, family structures, travel patterns and activities. Such a construction is done in such a way as to mirror real census, social, transit and telecoms data patterns, meaning that authorities effectively build virtual cities that they can test various disaster management strategies on.
During a disaster
"The real power of analytics is that it can utilise big data derived from multiple sources, such as population demographics, weather patterns, flood zones, town planning and even cartography, which is then built into a disaster management strategy. Performing real-time advanced analytics on all of this information will enable authorities to provide quicker, more effective responses to areas even as the disaster may be occurring."
"The kind of responses I am talking about is, for example, directing rescue crews or fire fighters to those areas most in need. By having immediate access to all crisis data, analytics can help staff save lives. It can also be used for prescribing the kind of medicines, food and medical equipment that will be required in specific areas".
"Authorities can also use analytics to determine where both the safest and most optimal areas are to set up treatment centres. This would entail taking various factors into account such as traffic, road networks, nearby hospitals, closest supply centres and infected populations."
After a disaster
While it is impossible to prevent natural disasters from occurring, it is possible to utilise data derived from previous catastrophes to mitigate future ones. Ramaloo points out that information related to weather and climate, road, utility and environmental vulnerabilities can play a major role in helping emergency management services to plot the emergency strategies of tomorrow.
"This approach applies to people as well. Government can make use of data related to population subsets - such as elderly communities, infant and youth concentrations and areas where individuals need specific mobility support - to ensure that should a disaster strike, responders can apply resources to those locations."
With big data, it is even possible to understand how residents may react to a future catastrophe. Data scientists can extract data sets from the details captured by local mobile-network operators to comprehend how populations move in response to an emergency situation, such as floods.
"Of course, for data to be effective, it must be shareable. By bringing together various sources of data, this helps to create a kind of spatial data infrastructure, making the development of policies, protocols and ways to exchange information an ongoing priority, leading to the creation of new best-case scenarios."
"Analysing data like this can even prove useful in situations where no previous disasters have occurred. Such data can still provide authorities with information about how potential disasters may impact on a particular region. This, in turn, means that government can develop proactive plans, in order to be ready for any eventuality - in any area - no matter how unlikely," he concludes.
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