Abstract
UN-Habitat estimates that 51.3% of the urban population in sub-Saharan Africa (SSA) resided in slums in 2020, and future projections indicate continued growth. However, limited information on the spatial distribution and evolution of slums in the region underestimates the challenges they present. This study investigates the use of urban morphology to map slums in 95 cities across Nigeria, Kenya, Ghana, and Malawi. The approach employed an unsupervised classification and a tree-based clustering framework, integrating morphological and socio-economic indicators, as well as comprehensive sampling points for slums. Our findings indicate that morphological clusters with compact, small buildings are indicative of a high prevalence of slums, with an accuracy rate of 83.6%. Moreover, these morphological slum clusters exhibit significant correlations with socio-economic indicators, exhibiting lower GDP and wealth index compared to neighbouring clusters. Notably, larger and older slums demonstrate improved economic well-being and enhanced infrastructures services. Our findings underscore the potential of utilizing urban morphology to comprehend the diversity and dynamics of urban slums and socioeconomic development. These results provide a foundation for large-scale identification of slums and urban deprivation, offering support for targeted solutions to address the challenges associated with slums in developing countries.
| Original language | English |
|---|---|
| Article number | 104863 |
| Journal | Sustainable Cities and Society |
| Volume | 99 |
| DOIs | |
| Publication status | Published - Dec 2023 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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SDG 11 Sustainable Cities and Communities
Keywords
- Inadequate housing
- Informal settlements
- Poor facility access
- Population growth
- Urban deprivation
- Urban morphology
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