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Mapping and Classifying Settlement Locations

Center for International Earth Science Information Network; Flowminder Foundation; United Nations Population Fund; WorldPop, University of Southampton

“Mapping and Classifying Settlement Locations” discusses GRID3’s work on collecting and analyzing 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.

GRID3 works with countries to generate, validate and use geospatial data on population, settlements, infrastructure, and subnational boundaries. For more information, see https://grid3.org/.

Keywords: area-level classification; building footprints; comprehensive settlement layer; extent; intra-settlement categorisation; machine learning; polygon layer; point layer; settlement; settlement data; settlement layer; settlement mapping; settlement point; ; GRID3; database schema; geospatial data; neighbourhood classification; open-source; health zones; participatory cartography; GIS; vaccination; immunisation; census; micro-plans; CIESIN; UNFPA; Flowminder; WorldPop; probability model; areal; built-up areas; small settlements; hamlets; hamlet areas; polio; Africa

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More About This Work

Academic Units
Center for International Earth Science Information Network
Series
GRID3
Published Here
April 8, 2020

Notes

Copyright ©️2020. The Trustees of Columbia University in the City of New York. This document is licensed under the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0).