Modelling internal migration balance in Ghana using remotely sensed drivers and census data

Kurzfassung

Migration in West Africa is driven by socio-economic, political, environmental and climatic factors. In Ghana, north-south migration has been linked to rainfall variability and soil degradation. This study uses Ghana’s 2021 census microdata to measure internal migration between districts and explores its drivers using remote sensing. The migration effectiveness index (MEI) measures the ability to attract, avoid or balance migration. It is the net to gross migration ratio and allows comparisons between districts. The MEI was calculated for recent migration based on residence five years earlier. We identified three million recent internal migrants in Ghana, representing 10% of the population. Aggregate values of remotely sensed data (derived from population and built-up classifications, night-time lights imagery, rainfall, burnt areas, land surface temperature data, vegetation indices, water and crop availability, and distance to coast) were calculated for the districts, including the mean for 2021 and the trend since 2000 (depending on availability). We assume that these remotely sensed drivers impact migration. Correlations between the MEI and the drivers show, on the one hand, that higher daytime and nighttime temperatures, consecutive dry days, distance to the coast, and more heavy rainfall days are associated with outmigration. On the other hand, higher dry season rainfall, greenness, increased number of rainy days, percentage of croplands and increased built-up density are associated with higher immigration. A multiple linear regression model was used to model MEI with a combination of several drivers. The model using aforementioned and other significant drivers can explain 46.1 % of the variance of MEI. Including additional socio-economic drivers from the census, such as the proportion of the male population and political and settlement migrants, improves the value to 54.8 %. This means that drivers derived from remote sensing data can support the study of internal migration and, despite some limitations, general internal migration trends can be modelled. In this study we show that the proxy of migration balance based on openly available remote sensing data allows for the development of migration policy and implementation. Especially in areas of infrequent censuses remote sensing supports the study of internal migration. Consequently, migration hotspots can be identified in a timely manner. Future research should evaluate the benefit of additional covariates, such as refugee camps, exposure to natural hazards, or information extracted from social media.