20190123 17463200 1.0 TRUE Map Ethiopia human settlement urban rural population density built-up area land cover weighting We modelled the population distribution of Ethiopia using census data (aggregated at Kebele and Wereda level (for Somali region)) and ancillary data for the definition of the sub-classes (i.e. land cover data and information on topography (slope). A proportional multi-class weighting dasymetric approach was applied for each sub-class to come up with a total population per Kebele. For Kebeles without census information and for data gaps the total population was estimated. We did this by a visual comparison of the land cover patterns between the units without census data and the surrounding units with population information. We assumed that neighbouring units with a similar configuration of land cover classes will show similar population densities. The population of the units without data was thus estimated by averaging the population den 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