Three ways how data science can help to manage the refugee crisis
The refugee crisis has been one of the most prominent topics in world politics and media. Donald Trump, multi-billionaire and GOP frontrunner for the 2016 U.S. presidential election, called – in what was one of the most controversial moments of the campaign – for a complete shut-down of Muslims travelling to the U.S. due to security concerns. Even though it caused public outrage across the bench, the proposal appeared to resonate well with voters: in polls, Trump further enhanced his lead. Meanwhile, countries in Europe are calling for immigration limits as they feel overwhelmed with the number of refugees seeking asylum – more than one million in Germany last year alone. However, concrete solutions for the crisis have barely been proposed. Here are three ways data science could help to manage the crisis.
How to disperse refugees in Europe?
With the massive influx of refugees from countries like Syria or Afghanistan to the European Union, especially countries at the external border, like Hungary, are bearing a large burden. When the local situations rapidly worsened last October, Germany’s chancellor Angela Merkel allowed refugees to enter Germany due to humanitarian considerations. Local communities are overwhelmed with the amount of administrative, humanitarian and operational work involved in allocating the refugees to proper facilities. Across Europe, a debate has unleashed regarding solidarity among member countries: How should countries split the burden of refugees and how could they be dispersed among the different communities within the European Union?
This debate is highly politicized, with countries bartering and negotiating. Data science could contribute largely to introduce a more objective angle into the debate. How precisely? By combining a variety of different data sources about job availability, housing opportunities, available medical and educational supporting structures, community coherence and social considerations, data science can help to point towards those areas in the EU which might be uniquely positioned to accommodate a certain amount of refugees. This could serve as a basis for substantial discussions, allowing representatives to deal with the matter in a constructive way without overstretching the capacity of individual communities.
Refugees: Who, where and how many?
Among the main factors driving political debate in Europe as well as in the U.S. are fear and uncertainty. Uncertainty about how many refugees are still on their way from the crisis-ridden countries of the world to Europe or the U.S, and fear about potential terrorists, which could infiltrate the country. Once again, political debates are circling around ideological agendas and beliefs, oftentimes not even merely resembling the facts.
In this context, data science can serve as a mirror for arguments, checking against actual validity. Using advanced computational techniques and predictive algorithms, data science could assist in helping governments and municipalities to predict the quantity and the routes of refugees more accurately. In local refugee camps around Syria, UNHCR officials have actually provided refugees with ID cards, which subsequently allow various tracking options.
Extrapolating data from these sources, in addition to social media and twitter activity, could help to refine predictive accuracy and give officials a better sense of what to expect – and also who to expect. Because the first step towards handling the situation is always a better, data-driven understanding of the situation at hand.
How to improve? Leverage mobile penetration for bottom-up idea management
Most of the refugees have access to a smartphone. It is, being detached from the usual surroundings, an essential life line for maintaining communications with relatives and friends. It is, however, also a splendid tool to establish a solid stream of communication with officials and administration. Some notable efforts in this regard have been established by the United Nations. Refugees can use their smartphone to communicate operational shortcomings, poor conditions and the likes. Aggregating these data points and combining them with social media data (Twitter, Facebook, etc.) as well as assessments from local officials and non-profits, allows for developing an early alert system. Using text mining and natural language processing in this regard enables data scientists to distill a target system for improvement and quick wins. This allows the humanitarian aid and support operations to function more smoothly, while having a high degree of real-time responsiveness to changing local conditions.
While the refugee situation in Europe is reality, data science can help along two dimensions. First, dealing with the current situation in a more data-driven manner, allowing for pragmatic and manageable solutions in various regards, for example in on-the-ground operations and solidary dispersion across Europe. Secondly, data science can help to build better prediction models about the future influx of refugees, their quantity and their geographic routes, essentially enabling the allocation of resources in a more efficient and preemptive way. As for so many areas of application, the basics are given: there are vast real-time data sets, which could be integrated in order to lift knowledge potential. This could help to take out the politics, which is distracting the current debate, and allow for fact-based decisions. So that such outrageous policy proposals like Donald Trump’s and the likes can be effectively countered.