Spatially explicit modelling of socio-spatial determinants of solid waste generation in Dar es Salaam’s informal settlements, Tanzania

Rapid urbanisation in African cities is intensifying solid waste challenges, especially in informal settlements where uneven services and socio-spatial heterogeneity complicate planning. This study quantifies the socio-spatial determinants of household solid waste generation in Mbagala Kuu Ward, a peri -urban informal settlement in Dar es Salaam, Tanzania, and translates results into an operational targeting tool for municipal decision-making. Household survey data (n = 115) were integrated with GIS-derived indicators of land use, population density and service accessibility and analysed using a Spatial Lag Gamma Generalised Linear Model with Moran’s Eigenvector Filtering to capture neighbourhood spillovers. Households generated an average of 5.94 kg day⁻1 (SD = 3.18), equivalent to 1.40 kg capita⁻1 day⁻1, of which only 47% was formally collected. Waste generation was spatially clustered (Moran’s I = 0.257, p < 0.001) and the spatial lag effect was positive (ρ = 0.241), indicating significant neighbourhood dependence. Higher education and income were associated with higher recorded waste, while greater distance to collection points was associated with reduced formal disposal (approximately 11% per additional 100 m). Commercial areas generated > 5 × more waste than residential areas, whereas urban agriculture and open spaces generated 40–––50% less. The model showed strong predictive performance (Pseudo-R2 = 0.79; MAE = 1.13 kg). To move from analysis to action, a Targeting Ratio assessment indicates that the highest-burden 20% of neighbourhood zones account for approximately 65% of total predicted waste, providing a practical basis for prioritising collection-point siting, routing and service frequency under constrained municipal resources. The findings support a shift from uniform service provision to neighbourhood-targeted waste planning, where infrastructure placement and collection schedules are aligned with spatially concentrated waste burdens and access constraints.