*webpage still being developed.
New sources, types, and methods of collecting big data and real-time information mean new – and faster – ways of analyzing the world. We now have an opportunity to harness these new sources of information for social good. Data can provide the global community, governments, and international organizations like UNICEF with critical insights into the needs of vulnerable populations, and where best to invest our scarce resources.
Data captures many different parts of human behavior, mobility, and environmental patterns. We can use data to shape responses to disasters, epidemics, and other challenges, by telling those involved in the response:
– Where to focus their limited resources;
– How people who are most at risk are thinking about a threat;
– What information to provide to affected populations;
– and ways to proactively inform vital work to protect vulnerable children. It also allows us to link this information to governments and other partners for real-time situational awareness and problem-solving.
At UNICEF, how we can use data to inform programmes and operations stems from four key considerations.
– What are the needs at a UNICEF Country Office level? Looking at which questions and issues we can address – and where real-time data can help – creates a programmatic grounding for the role of data.
– How can we work with the private sector to get access to the data that tells us what we need to know? Data from partners like Telefonica, Google, IBM and Amadeus can capture diverse and important parts of human behavior, as well as environmental patterns.
– How can we work with academic and technical partners to create models from this data (e.g. from epidemiology, mapping schools, information poverty and more)?
– Where can we apply these models in context? Working with UNICEF Country Offices (as well as internal and UN system partners like PAHO) allows us to affirm the utility of the data models, link them back to programmatic needs,make sure they are useful to address the issues and needs identified.
Magicbox in Action. Key Applications.
Using data on how people move (between countries or within them) to create risk maps for the spread of diseases. Pilot work in Colombia, Brazil, Sierra Leone, and DRC. More info here.
Using satellite imagery and other data to map schools and measure their connectivity as a tool for emergency preparedness, as well as advocacy and information access. Pilots of various types are underway in Liberia, Colombia, and Brazil. Find more here.
Using data on how people do, or don’t, use services like mobile networks after an emergency to understand how a community is rebuilding. Pilot work in Colombia. More info here.
Using satellite data and mobile phone data to understand indicators like household poverty, in real-time. This work is being piloted in Iraq. Find out more here.
Using data on how people do, or don’t, access information to set some minimum standards around the amount and type of information that children should have access to. Pilots of various types in Mozambique, Liberia, and Brazil. More info here.
UNICEF could apply Magic Box’s data integration capabilities to other needs and contexts like:
1. Tracking mass population displacements against existing health and nutrition facilities, so we can anticipate and pre-position critical supplies and personnel ahead of their arrival.
2. Mapping risks and conducting predictive analysis, and to provide invaluable contextual insights, particularly in cases where traditional data gathering is incapacitated
*These are just some of the applications being developed at present. But they are not the only ones – more areas are being explored and will be added on to the platform.
How it works.
Connecting real time data generated by private sector to the humanitarian response tools is a challenging task that has never been done before. It requires new type of infrastructure to allow these distant ecosystems to connect. With the help of partners, UNICEF’s Office of Innovation is developing a software platform that intended to use real-time data to inform life-saving humanitarian responses to emergency situations. This open-source platform ingests data from both public sources and from private sector partners, and generates insights based on methodologies and algorithms provided by our data science team. These insights are made available to the development and humanitarian partners through an API and user interfaces.
The first version of this platform was created during the 2014 Ebola crisis in West Africa, and a second version developed with Google in response to the Zika outbreak in 2015. Since then, it is being adapted to multiple applications and made available to open-source collaborators.Follow and contribute to our github repository (github.com/unicef/magicbox)
There are several solutions that address the use of a particular dataset and also solutions that address the problem but under proprietary software. The main differentiators of our solution are:
- Combination of different datasets from diverse providers for a more insightful result.
- Proactiveness. Many initiatives requiring data sharing are only established at the time of a crisis. Our approach is to partner with data providers in advance, to allow preparedness activities and ensure readiness at the time of an emergency
- Open source software and open methods published
- Partnership with academia and cutting-edge data scientists to develop the solution
- Our multidisciplinary team, composed by designers, engineers and scientists, complement UNICEF 70-year field expertise in order to tailor a product that fits real needs.
Magic Box is a collaborative platform, that can only be made possible by contributions of multiple partners that bring their data and expertise for public good. UNICEF’s Office of Innovation is actively looking for partners to build on our systems for real-time information collection, aggregation, and analysis. Exogenous shocks like population displacements, disease outbreaks, and natural disasters are sudden and often unpredictable, but we can be proactive by assembling what we do know (and have) ahead of time – rather than reactively negotiating data access in time-sensitive situations. This model also allows us to better address challenges outside of acute emergencies (like climate change or urbanization).
You can partner with us by providing data, expertise and financial contribution.