Integration is considered a complex, multi-dimensional, non-linear and long term dynamic process. The analysis of how social nets evolve through time is key to be able to quantitatively measure social segregation. According to UNHCR, integration can be achieved when refugee population enjoys the same rights and participates in local activities and groups. CDRs can help reconstruct the social graph and obtain indicators that measure how refugees communicate with other groups and locations with a certain level of refugee population. Homophily and diversity of interconnections were shown as good proxies of integration that have to be further enriched with other data. Thus, by labelling regions, the temporal evolution of the social nets can be monitored.
By integrating mobility and networks, it will be possible to discover factors of migrations at different scales of the settling population and also evaluating the impact of policies and financial programmes.
Can we imagine human-machine data-driven mechanisms that activate and optimize humanitarian action through real-time proxies? The implications for advocacy and global accountability and transparency are substantial. This way of working has great potential to overcome the limitations of current systems based in situ assessments carried out by humanitarian actors or governments. This vision requires innovation, both in technology and management.
The challenges are significant. First, establishing a common framework for accessing data real-time from different operators in several countries with heterogeneous legal frameworks. Currently, working groups (EC B2G, UN Privacy Group) are discussing about ethics, privacy and consequences for business of data sharing approaches to leverage private sector data for development and humanitarian applications. Second, ensuring refugees’ privacy and safety through technology and protocols. Third, developing methodologies to identify patterns of the population of interest among all the aggregated patterns of the whole population. While AI can help identifying behaviours provide data of a control population, this data is not available and the characterization has to be done through holistic approaches. Finally, transforming organizations to be data-driven, which requires creating new capacities.
In 2016 UNHCR, in collaboration with UNGP, approached how mobile phone data could be the basis for monitoring systems. A project undertaken in Senegal in synergy with the D4D Challenge promoted by Orange allowed obtaining the conclusions presented. Although this project was focused on Senegal, it introduces clues on how to scale up data-driven operations considering geographical, urban and social factors.
More recently, the international D4R Challenge made available aggregated and anonymized CDRs from Turkey by Türk Telecom. In this occasion, data contained labelled information about syrian refugee status, allowing characterizing refugee patterns. Being syrian migration in Turkey the biggest situation identified by UNHCR, this Challenge was a unique opportunity to understand how to improve humanitarian systems.
Overall, the indicators described are key to measure the impact of humanitarian help from individual, collective and systemic perspectives, as well as to help designing better policies for a more sustainable and inclusive development. Many efforts must still be done to leverage this technology to support refugees and hosting communities but it is an opportunity that we should not miss.