A high resolution spatial population database of Somalia for disease risk mapping.
BACKGROUND: Millions 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 u...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
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2010
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| _version_ | 1826306646031204352 |
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| author | Linard, C Alegana, V Noor, A Snow, R Tatem, A |
| author_facet | Linard, C Alegana, V Noor, A Snow, R Tatem, A |
| author_sort | Linard, C |
| collection | OXFORD |
| description | BACKGROUND: Millions 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 In a low-income country such as Somalia, population data are lacking, are of poor quality, or become outdated rapidly.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. RESULTS: Here 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. CONCLUSIONS: The 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. |
| first_indexed | 2024-03-07T06:51:07Z |
| format | Journal article |
| id | oxford-uuid:fc9846ea-85ff-4db1-b165-86b2564546d4 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T06:51:07Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | oxford-uuid:fc9846ea-85ff-4db1-b165-86b2564546d42022-03-27T13:21:59ZA high resolution spatial population database of Somalia for disease risk mapping.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:fc9846ea-85ff-4db1-b165-86b2564546d4EnglishSymplectic Elements at Oxford2010Linard, CAlegana, VNoor, ASnow, RTatem, A BACKGROUND: Millions 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. RESULTS: Here 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. CONCLUSIONS: The 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. |
| spellingShingle | Linard, C Alegana, V Noor, A Snow, R Tatem, A A high resolution spatial population database of Somalia for disease risk mapping. |
| title | A high resolution spatial population database of Somalia for disease risk mapping. |
| title_full | A high resolution spatial population database of Somalia for disease risk mapping. |
| title_fullStr | A high resolution spatial population database of Somalia for disease risk mapping. |
| title_full_unstemmed | A high resolution spatial population database of Somalia for disease risk mapping. |
| title_short | A high resolution spatial population database of Somalia for disease risk mapping. |
| title_sort | high resolution spatial population database of somalia for disease risk mapping |
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