Integrated approaches to human migration / mobility in an era of rapid global change

Climate change in Africa could increase human mobility, including internal resettlements and international movements, yet the magnitude is disputable. Despite notable theories developed, there has been no unified conceptual framework capturing the complexity and contextual dependency of the climate-migration nexus. Empirically, the link between various climatic conditions and migration outcomes is contested. A better understanding of how climate risks may intersect with conflicts, poverty, and epidemics and how they drive human migration is needed. We also need human-centric approaches to understand people’s perceptions of and attitudes towards risks, and aspirations and capabilities to migrate.

Conventional data sources (e.g. official statistics, and surveys) are not sufficient for this, as they are typically annual or at a lower frequency, masking short-term mobility patterns. They also do not quantify individual, societal, and contextual factors well, which poses a severe constraint on incorporating human centric approaches into migration models. We therefore hypothesize that a holistic approach building additionally on earth observation satellite data, mobile phone call detail records, location data from Facebook/Meta, and statistics of specific search term usages can provide a more granular insight into locations and timing of internal and external migration with respect to extreme weather events. The study area is Senegal. Located in the Sahel, it is strongly affected by climate change. Moreover, anonymized and aggregated mobile phone call detail records (CDR) will be available there.

We employ CDR analysis to understand migration at various geographical and temporal scales, and very high resolution satellite data to quantify selected migration flows. To quantify potential climate change related drivers of migration, we map long-term climate data and -proxies from the MODIS/Landsat/Sentinel archive. Perceptions and aspirations towards migration will be extracted from survey data, social media and search term analysis. The insights gained from these data will be used to design quantitative prediction models.

The interdisciplinary approach employed by renowned experts in their respective field promises to deliver a holistic picture of the climate change – migration nexus, which is impossible to obtain using only a singular methodology.