% of African Urban Population living in Slums

Data Source & Methodology

UN-Habitat Urban Indicators Database

Note - Data for the slum/informal settlements components of the indicator is computed from censuses and national household surveys such as the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS)

Definition of Urban population living in slums or informal settlements

Proportion of urban population living in slums or informal settlements. The estimates are based on the global methodology on household deprivations, where the inhabitants suffer one or more of the following ‘household deprivations’:

1. Lack of access to improved water services

2. Lack of access to improved sanitation facilities

3. Lack of sufficient living area

4. Lack of housing durability. Informal settlements are synonymous of slums with households/neighborhoods characterized by lack, or are cut off from formal basic services and city infrastructure.

Data Limitations

While UN-Habitat estimates 51-62% of SSA's urban population lives in informal settlements, experts acknowledge significant data gaps and methodological limitations mean the reality could be worse. The direction is clear even if exact numbers are uncertain.

1. Definition Inconsistencies

   1. Countries use different criteria to define "slums" and "informal settlements" (tenure vs. infrastructure vs. administrative)

   2. Makes international comparisons unreliable

   3. Household-level assessment misses collective settlement dynamics

2. Outdated Census Data

   1. Many African countries haven't conducted census in 20-30 years

   2. Population projections become increasingly unreliable over time

   3. Rapid urban growth outpaces data collection capacity

3. Significant Data Gaps

   1. UN-Habitat admits "significant data gap exists in relation to informal settlements" beyond slum definitions

   2. Only 2/7 of Africa's urban reality captured in formal data

   3. Missing: spatial clustering, shared infrastructure deficits, systemic vulnerabilities

4. Methodological Constraints

   1. Relies heavily on household surveys (DHS, MICS) which may miss transient/hard-to-reach populations

   2. Administrative boundaries don't match actual settlement patterns

   3. Census data in low/middle-income cities often "not reliable" for slum estimates

5. Population Estimation Problems

   1. Example: Kibera (Nairobi) estimates ranged from 200,000 to 1 million—5x variance

   2. Slum population often aggregated within larger administrative areas, causing "large diffusion in estimates"

   3. Only 1.1 billion classified as slum dwellers vs. 2.2 billion lacking safely managed water—major discrepancy

6. Limited Spatial Granularity

   1. Satellite imagery/AI mapping still labor-intensive and costly at scale

   2. Lack of diverse high-quality imagery

   3. Can't capture internal settlement dynamics or informal economies

7. Coverage Limitations

   1. Data "collection process was limited" in many regions (Arab states, parts of SSA)

   2. Some countries lack clear data or policies entirely

   3. Informal settlements often excluded from official urban statistics