Mapping Poverty and Slums Using Multiple Methodologies in Accra, Ghana

Abstract

Providing housing to slum dwellers, protecting them from natural disasters and diseases, and connecting them to jobs and services through improved infrastructure are urgent policy issues in many Sub-Saharan African cities. Identifying the location and living conditions of slums is a critical step toward designing effective urban policies. By combining household survey data and census data with high spatial resolution satellite imagery and other geospatial data using multiple methodologies, including machine learning, we attempt to define slums quantitatively within the city of Accra. Within these defined slum areas, the patterns of monetary poverty are assessed. Poverty rates are estimated at the neighborhood level and indicate that living in slums is strongly correlated with higher monetary poverty. Poverty is more prevalent in communities in areas of lower elevation, which in Accra are generally flood-prone areas. However, the results also suggest that not all people living in slums are living in monetary poverty. These results have important policy implications and are crucial to how economic opportunities are generated in slums so that effective urban policies can be designed.

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