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Mapping and Classifying Settlement Locations

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.

Authors Center for International Earth Science Information Network; Flowminder Foundation; United Nations Population Fund; WorldPop, University of Southampton
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Piloting the GMT in Nigeria: Insights from Kano and Kaduna

The Geospatial Microplanning Toolkit (GMT) is a mobile application that allows health staff to record, edit, and visualize key spatial data and features via an interactive user interface. The GMT was developed to address three issues affecting health planning and […]

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Microplanning: A promising approach to identify and reach zero-dose children in Democratic Republic of Congo (DRC)

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GRID3 Impact Report 2017-2022

This impact report provides an overview of GRID3’s activities and successes during the program’s first phase, between 2017 and 2022. It highlights key milestones, achievements, and selected use cases from the Democratic Republic of the Congo, Nigeria, and Zambia. The […]

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