Computational Approach to Evaluate the Impact of Natural Disasters.

Although countries and regions prone to natural disasters can be mapped, they are often not equipped to prevent or respond comprehensively. Exogenous shocks like earthquakes, floods, landslides, and other natural occurrences often occur without any early warnings. Such disasters often have tragic consequences, affect massive populations, cause loss of life and leave lasting damage that requires years to repair. After a disaster, the first hours of the response are critical for saving lives. Emergency responders need to act very quickly to reach affected populations, as such, it is vital that they receive accurate and up-to-date information to help focus their limited resources.

Changes in human behavior.

At the time of a disaster or an emergency, people change behavior:Population mobility patterns,  consumption habits, our daily routines, change when we are in distress. Often, the instant reaction is to reach out to a safe zone, notify others who may be concerned or in danger, or turn to social media for information.  These changes can be observed directly throughout various social and technological platforms, from Twitter and Facebook to cell phone activity (see figure below). All these interactions leave digital breadcrumbs which researchers and organizations can utilize in order to:

– Create emergency alert systems: Giving first-responders the opportunity to, in near real-time, estimate the number of affected people, pinpoint the region of the anomalous behavior, and the possibility to infer which geographic regions are most affected.

– Monitoring the evolution of affected populations: Allowing relief organizations to understand how different regions are recovering after a shock, and in real-time monitor how fast people return to a pre-event behavior.

The work we do.

UNICEF builds mathematical models that detect such events in real-time, allowing first-responders to estimate the number of affected people and infer which geographic regions are most affected. Analysing data over longer periods, our methods allow governments and UNICEF field offices to understand how different communities are recovering after a shock, as well as monitor how fast people return to a pre-event behavior. Potentially identifying communities of interest that can benefit from receiving additional aid.

These mathematical models are available to country offices through the Magic Box platform.

Figure: Collective response to the March 10th 2015 earthquake in Colombia. The top panel shows the hourly number of calls in the region closest to the epicenter, exhibiting a clear spike right after the earthquake. Bottom left panel compares cellular activity in the two closest regions (light green circles) to the overall country, a few hours after the earthquake. Circles are scaled according to call frequencies, the larger a circle the more calls take place. The bottom right panel is a map of the most affected regions a few hours after the earthquake.

A closer look. 

UNICEF Innovation is partnering with the private sector to obtain information that can play a critical role in responding to a disaster in a country or a region. In partnership with Telefonica, we are using aggregated data from phone usage to build a platform that allows identifying – in almost real-time – areas affected by a drastic shock such as an earthquake, a flood or a landslide. Our research aims to answer the following questions:

– Evaluate the robustness and usability of telephone communication patterns to accurately infer the scale and location of individual disasters. Are changes in call activity enough or do we need to supplement the data with information from additional sources, such as aggregated patterns of human mobility?

– Build mathematical models that can distinguish between changes in communication patterns related to disasters and changes originating from non-emergencies such as: national holidays, music festivals and sporting events.

– Can we monitor the recovery of affected communities and track how fast they fall back into normal daily patterns? Potentially identifying communities of interest that can benefit from receiving additional help, or communities that can play an active role in supporting others.

. Next steps include improving and refining the methodologies and testing the usability of these insights and data in emergency prone areas.

A practical example: Colombia.  

Colombia is beset by regular natural disasters as well as humanitarian challenges emerging from the long-term conflict and current post-accord context. Real-time information on people’s movement, access to schools and other services, as well as local capacities, is critical to planning an emergency response. Together with Telefonica and Facebook, we are testing the possibilities to integrate these new datasets and insights, as well as the school mapping project, into the existing disaster preparedness and response systems operating in Colombia.


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Email us via: vsekara [at] unicef [dot] org

 

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