Modeling the Spatial Interplay Between Migration and Environmental Conditions

The measurement and modeling of migration patterns and potential drivers are often limited by the quality of spatial data at local levels. Our aim is to address these constraints by advantaging of consolidated techniques in geospatial regression and emerging machine-learning approaches to explore how selected contextual socio-economic and environmental factors are related to migration trends. We define a three-step empirical strategy. First, we use the Geographically-Weighted-Regression to model the spatial variation at local scales. Second, we adopt the Multiscale-Geographically-Weighted-Regression to focus on the spatial heterogeneity across geographical scales. Third, we adopt the Geographically-Weighted-Random-Forest-Regression to validate variations across multiple local models. To illustrate, we apply the proposed methodology to selected environmental factors and gridded estimates of net migration patterns in Ghana, between 1985 and 2014. By the comparison of results, we argue the models’ complementary explaining the contribution of each method to depict the spatial variability of migration environmental factor