Table of Contents
Dataset Papers in Science
Volume 2014, Article ID 172182, 7 pages
Dataset Paper

Mapping the Slums of Dhaka from 2006 to 2010

1Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 West 168th Street, Room 517, New York, NY 10032, USA
2Department of Tropical Medicine, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, New Orleans, LA 70112, USA
3Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
4Department of Public Health Medicine, School of Public Health, University of Bielefeld, P.O. Box 100131, 33501 Bielefeld, Germany

Received 30 September 2013; Accepted 4 March 2014; Published 25 June 2014

Academic Editors: S.-Y. Ho and C. Saurina

Copyright © 2014 Oliver Gruebner et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. Rapid urban growth in low and middle income countries is frequently characterized by informal developments. The resulting social segregation and slums show disparities in health outcomes for the populations of the world’s megacities. To address these challenges, information on the spatial distribution of slums is necessary, yet the data are rarely available. The goal of this study was to use a remote sensing based approach to map urban slums in Dhaka, the second fastest growing megacity in the world. Methods. Slums were mapped through the visual interpretation of Quickbird satellite imagery between the years 2006 and 2010. Ancillary references included the 2005 census and mapping of slums, Google Earth, and geolocated photographs. The 2006 slums were first delineated and filtered in GIS to avoid small, isolated slums. For 2010, changes to the 2006 slums were defined over the latter’s polygons to retain border consistency. Conclusions. The dataset presented here can be considered a stepping stone for further research on slums and urban expansion in Dhaka. The slum distribution dataset is useful to be pooled with other data to reveal trends of informal settlement growth for local health policy advice in Dhaka.