Modeling and forecasting African Urban Population Patterns for vulnerability and health assessments
- Project Code: MAUPP
- Start Date: 2014
- End Date: 2018
- Reference: N/A
- Funding: STEREO III (BELSPO)
- Royal Military Academy Involvement: Partner
- Partners: Université Libre de Bruxelles, Royal Military Academy, University of Southampton
Context
The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates. Urbanization has profound social, environmental and epidemiological implications and makes spatial and quantitative estimations of urban change, population density and socio-economic characteristics valuable information for epidemiology and vulnerability assessment. 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 and allows the link to empirical observations with urban theory. Moreover, remote sensing data have a great potential to map and predict intra-urban variations in population density because they provide information on the morphology of different residential patterns that can be linked to different population densities and socio-economic parameters.
Objectives
The general objective of the project was to improve our spatial understanding, prediction and forecast of urbanization and urban population in Africa through the use of remote sensing and spatial modelling. The project addressed two specific objectives:
Produce an urban expansion model at moderate spatial resolution for African cities. This objective was be achieved through i) use of HRRS and VHRRS (both optical and radar) to delineate urban extent of a set of cities, compare the accuracy and limitations of the obtained features and optimize the method based on HRRS, ii) use of the best methods identified using HRRS to generate a database of land cover change to urban over the last 30 years across a large number of cities in Africa, iii) use of this database to build urban expansion models, evaluate their forecasting accuracy, and apply them to forecast the future distribution of the major urban extents in Africa. Understand and predict intra-urban variations in human population density in Africa. This was be achieved through i) the use of HRRS and VHRRS to predict human population density within the urban extent of a set of cities, compare the accuracy and limitations of the RS products, and optimize the method based on HRRS, ii) use of VHRRS to analyse and to understand the drivers of changes in human population density within cities; iii) use of the best methods based on HRRS data to generate a database of human population density within urban extent across a large number of cities in Africa.