A high resolution spatial population database of Somalia for disease risk mapping

BackgroundMillions of Somali have been deprived of basic health services due to the unstable political situation of their country. Attempts are being made to reconstruct the health sector, in particular to estimate the extent of infectious disease burden. However, any approach that requires the use of modelled disease rates requires reasonable information on population distribution. In a low-income country such as Somalia, population data are lacking, are of poor quality, or become outdated rapidly. Modelling methods are therefore needed for the production of contemporary and spatially detailed population data.ResultsHere land cover information derived from satellite imagery and existing settlement point datasets were used for the spatial reallocation of populations within census units. We used simple and semi-automated methods that can be implemented with free image processing software to produce an easily updatable gridded population dataset at 100 × 100 meters spatial resolution. The 2010 population dataset was matched to administrative population totals projected by the UN. Comparison tests between the new dataset and existing population datasets revealed important differences in population size distributions, and in population at risk of malaria estimates. These differences are particularly important in more densely populated areas and strongly depend on the settlement data used in the modelling approach.ConclusionsThe results show that it is possible to produce detailed, contemporary and easily updatable settlement and population distribution datasets of Somalia using existing data. The 2010 population dataset produced is freely available as a product of the AfriPop Project and can be downloaded from: http://www.afripop.org.

[1]  A. Tatem,et al.  The accuracy of human population maps for public health application , 2005, Tropical medicine & international health : TM & IH.

[2]  J. E. Dobson,et al.  LandScan: A Global Population Database for Estimating Populations at Risk , 2000 .

[3]  P. Bocquier.  WORLD URBANIZATION PROSPECTS: AN ALTERNATIVE TO THE UN MODEL OF PROJECTION COMPATIBLE WITH URBAN TRANSITION THEORY 1 , 2005 .

[4]  Budhendra L. Bhaduri,et al.  Metrics for the comparative analysis of geospatial datasets with applications to high-resolution grid-based population data , 2007 .

[5]  A. Tatem,et al.  High Resolution Population Maps for Low Income Nations: Combining Land Cover and Census in East Africa , 2007, PloS one.

[6]  David L. Smith,et al.  A World Malaria Map: Plasmodium falciparum Endemicity in 2007 , 2009, PLoS medicine.

[7]  A. Tatem,et al.  Assessing the accuracy of satellite derived global and national urban maps in Kenya. , 2005, Remote sensing of environment.

[8]  Uwe Deichmann,et al.  A Review of Spatial Population Database Design and Modeling , 1996 .

[9]  Catherine Linard,et al.  Assessing the use of global land cover data for guiding large area population distribution modelling , 2010, GeoJournal.

[10]  Simon I Hay,et al.  Spatial prediction of Plasmodium falciparum prevalence in Somalia , 2008, Malaria Journal.

[11]  J. E. Dobson,et al.  LandScan: Locating People is What Matters , 2002 .

[12]  A. Tatem,et al.  Defining approaches to settlement mapping for public health management in Kenya using medium spatial resolution satellite imagery. , 2004, Remote sensing of environment.

[13]  United Nations.  OFFICE FOR THE COORDINATION OF HUMANITARIAN AFFAIRS , 2000 .

[14]  F. Pozzi,et al.  Mapping global urban and rural population distributions , 2006 .

[15]  Deborah Balk,et al.  The Distribution of People and the Dimension of Place: Methodologies to Improve the Global Estimation of Urban Extents , 2004 .