Combining population density, built up surface and nightlights to map spatial inequality: a case study in Malawi

High precision mapping of communities and their inequalities is imperative to alleviating existing social ills in developing countries and contributing towards the achievement of the United Nation’s Sustainable Development Goals (SDG) such as SDG 10 to ‘reduce inequalities’ (UN, 2020). In the lack of capacity for effective household surveying in developing countries lies the justification for the use of alternative methods to fill the data gap which prevails. As a politically independent source of data, Earth Observation imagery and derived geospatial products provide one solution. This study takes the combination of population density, ‘built-up’ surface and nightlights data to find five distinct clusters of communities in Malawi. These clusters correspond to geospatially disparate settlement types – for which the populations have been characterised against 20 dimensions of spatial inequality through correlation analysis with census data. The characterisation of each cluster has created five Malawian community typologies based on evidenced spatial inequality; Isolated Rural, Rural Node, Transitional Urban, Urban Access and Urban Core. Disaggregation of census data to near-household resolution through typologies has provided a level of detail which allows the representation of households otherwise unaccounted for in surveys or lost in survey data aggregation. Utilisation of these typologies could greatly aid the effective deployment of policy designed to mitigate against inequalities (SDG 10.B). Further work should be done to include more input datasets to enhance the characterisation of each cluster found and provide more detailed typologies.