MAUPP - Modelling and forecasting African Urban Population Patterns for vulnerability and health assessments

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Context and objectives

Africa's population is predicted to double over the next 40 years, driving exceptionally high urban expansion rates. Urbanization has profound social, environmental and epidemiological implications. The performance of urban expansion models largely depends on the quality and type of data available, which have so far been limited, and reduced the confidence and the applications of models for Africa. Satellite remote sensing offers an effective solution for mapping settlements and monitoring urbanization at different spatial and temporal scales. Moreover, remote sensing data have a great potential to map and predict intra-urban variations in population density.
Two major limitations exist in urban population distribution datasets:

  1.  The lack of urban expansion forecasts and
  2. The homogenous distribution of people within cities. The general objective of the project is therefore to improve the spatial understanding and forecast of urbanization and urban population distribution in Sub-saharan Africa (SSA) using remote sensing and spatial modelling.

The project addresses two specific objectives:
- Produce an urban expansion model at moderate spatial resolution for African cities.
- Understand and predict intra-urban variations in human population density in Africa.
The project aims to contribute to the AfriPop/WorldPop project (

The very-high resolution land-cover map derived from commercial satellite images. Legend classes: HB: High buildings (>10 m); MB: Medium buildings (5–10 m); LB: Low buildings (<5 m); SW: Swimming pools; AS: Artificial ground surfaces; BS: Bare soils; TR: Trees; LV: Low vegetation; WB: Inland waters.

The very-high resolution land-use map derived from the VHR land-cover map. Legend classes: AGRI: Agricultural vegetation; VEG: Natural vegetation; BARE: Bare soils; ACS: Non-residential built-up (administrative, commercial, services, etc.); PLAN: Planned residential built-up; DEPR: Deprived residential built-up.


Project outcome

Expected scientific results

• Automated methods sensing for urban detection in sub-Saharan Africa, combining different types of remote sensing data.
• Identification of geographical factors that control extension of SSA cities and help to understand their spatial structure. These, should in turn help us understanding how the structure of the cities is linked to the within-city populationdistribution.
• Identification of spatial factors that have a generalizable effect on urban growth SSA.
• Identification of the relationships between HRRS and VHRRS landscape attributes.


Expected products and services

• A robust method integrating SAR and optical data for delineating urban extents in Africa
• A database of land cover change to urban over the last 20 years across 30 cities in Africa
• A robust and advanced method integrating VHRRS stereo optical and SAR data for the land cover and land use mapping for African cities
• A database of land cover and land use for 3 cities
• High resolution forecasts of urban expansion across Africa in 2020-2030
• Intra-urban population density maps for 30 cities across Africa
• Automated procedure to integrate the two models into Africa-wide population datasets
• Map of intra-urban population densities for 3 selected African cities
• Trans-scale analysis between population densities derived from VHRRS and HR data
• Predicted Africa-wide population distribution datasets for 2020 and 2030 that include urban expansion forecasts
• Africa-wide population distribution datasets that better predict intra-urban population densities
• Dissemination of results through WorldPop website
• A survey that identifies the interested users and their requirements


WorldPop datasets for Africa are used by:
• policy makers to support development, health and planning,
• researchers in international organizations and agencies, such as the International Red Cross, World Bank, UN-FAO, UNDP, CDC, WFP, WWF, MSF, iMMAP, Population Council, Clinton Health Access Initiative, DFID, USGS, UNOCHA and MapAction.