Discusses GRID3’s work on collecting and analysing settlements data. GRID3’s settlements work has two areas of focus: creating a comprehensive settlement layer that enables a real-world picture of communities, and using building footprints, geospatial data layers, and machine learning algorithms to classify structures and local areas within settlements. The paper also discusses the applications of GRID3’s methods in Nigeria, the Democratic Republic of the Congo, and Zambia.
Center for International Earth Science Information Network; Flowminder Foundation; United Nations Population Fund; WorldPop, University of Southampton
National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty
Population estimates are critical for government services, development projects, and public health campaigns. Such data are typically obtained through a national population and housing census. However, population estimates can quickly become inaccurate in localized areas, particularly where migration or displacement has occurred.
Discusses GRID3’s work to support the harmonisation, production, and use of digitised legal/administrative units, operational units, and statistical areas. The paper focuses on case studies in Nigeria, the Democratic Republic of Congo, and Zambia.
Classifying settlement types from multi-scale spatial patterns of building footprints
Urban settlements and urbanised populations continue to grow rapidly and much of this transition is occurring in less developed countries. Remote sensing techniques are now often applied to monitor urbanisation and changes in settlement patterns. In particular, increasing availability of very high resolution imagery (<1 m spatial resolution) and computing power is enabling complete sets of settlement data in the form of building footprints to be extracted from imagery.