Modelled gridded population estimates for Kongo-Central Province in the Democratic Republic of Congo, version 4.3
Modelled gridded population estimates for Kongo-Central Province in the Democratic Republic of Congo, version 4.3
This data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100 m grid cells) for Kongo-Central Province in the Democratic Republic of Congo (DRC), along with estimates of the number of people belonging to various age-sex groups. The project team used the Pre-Distribution Registration Survey (PDRS) data from the National Malaria Control Programme (PNLP) collected as part of anti-malarial campaigns in the Democratic Republic of the Congo for 2023, settlement footprints and geospatial covariates to model and estimate population numbers at grid cell level using a Bayesian statistical hierarchical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data. It also allowed the quantification of uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the year 2023. This time period corresponds to the PDRS survey date for Kongo-Central. Although the methods were robust enough to explicitly account for key random biases within the datasets, it is noted that systematic biases, which may arise from sources other than random errors within the observed data collection process, are most likely to remain.
These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 – Phase 2 Scaling project, with funding from the Bill & Melinda Gates Foundation (INV-044979). Project partners included GRID3, the Center for Integrated Earth System Information (CIESIN) within the Columbia Climate School at Columbia University, and WorldPop at the University of Southampton. The final statistical modelling was designed, developed, and implemented by Ortis Yankey. Data processing was done by Ortis Yankey with additional support from Heather Chamberlain. Project oversight was done by Chris Nnanatu, Attila Lazar, and Andy Tatem. The PDRS data from the malaria insecticide treated net (ITN) distribution campaigns were collected, processed, anonymised, and shared by the PNLP and its implementing partners. The settlement extent data was prepared and shared by CIESIN (2024). The data has been clipped to GRID3-CIESIN health area extent (version 6.0) (CIESIN, 2025)
population, Population age and sex structure, Kongo-Central, DRC
University of Southampton
Yankey, Ortis
9965d053-8afb-462f-b7fe-b270e21f2ec1
Chaudhuri, Somnath
ae0507e0-f920-4438-bc9f-ecdd5ac8967a
Chamberlain, Heather
cb939de7-ac47-440e-aeb8-a2e36c110785
Lazar, Attila
d7f835e7-1e3d-4742-b366-af19cf5fc881
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Yankey, Ortis
9965d053-8afb-462f-b7fe-b270e21f2ec1
Chaudhuri, Somnath
ae0507e0-f920-4438-bc9f-ecdd5ac8967a
Chamberlain, Heather
cb939de7-ac47-440e-aeb8-a2e36c110785
Lazar, Attila
d7f835e7-1e3d-4742-b366-af19cf5fc881
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Yankey, Ortis, Chaudhuri, Somnath, Chamberlain, Heather, Lazar, Attila and Tatem, Andrew
(2025)
Modelled gridded population estimates for Kongo-Central Province in the Democratic Republic of Congo, version 4.3.
University of Southampton
doi:10.5258/SOTON/WP00801
[Dataset]
Abstract
This data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100 m grid cells) for Kongo-Central Province in the Democratic Republic of Congo (DRC), along with estimates of the number of people belonging to various age-sex groups. The project team used the Pre-Distribution Registration Survey (PDRS) data from the National Malaria Control Programme (PNLP) collected as part of anti-malarial campaigns in the Democratic Republic of the Congo for 2023, settlement footprints and geospatial covariates to model and estimate population numbers at grid cell level using a Bayesian statistical hierarchical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data. It also allowed the quantification of uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the year 2023. This time period corresponds to the PDRS survey date for Kongo-Central. Although the methods were robust enough to explicitly account for key random biases within the datasets, it is noted that systematic biases, which may arise from sources other than random errors within the observed data collection process, are most likely to remain.
These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 – Phase 2 Scaling project, with funding from the Bill & Melinda Gates Foundation (INV-044979). Project partners included GRID3, the Center for Integrated Earth System Information (CIESIN) within the Columbia Climate School at Columbia University, and WorldPop at the University of Southampton. The final statistical modelling was designed, developed, and implemented by Ortis Yankey. Data processing was done by Ortis Yankey with additional support from Heather Chamberlain. Project oversight was done by Chris Nnanatu, Attila Lazar, and Andy Tatem. The PDRS data from the malaria insecticide treated net (ITN) distribution campaigns were collected, processed, anonymised, and shared by the PNLP and its implementing partners. The settlement extent data was prepared and shared by CIESIN (2024). The data has been clipped to GRID3-CIESIN health area extent (version 6.0) (CIESIN, 2025)
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More information
Published date: 29 August 2025
Keywords:
population, Population age and sex structure, Kongo-Central, DRC
Identifiers
Local EPrints ID: 504389
URI: http://eprints.soton.ac.uk/id/eprint/504389
PURE UUID: d422f60c-ebb1-42e6-8ea2-d9dd63244468
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Date deposited: 08 Sep 2025 17:03
Last modified: 09 Sep 2025 02:17
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Contributors
Creator:
Ortis Yankey
Creator:
Somnath Chaudhuri
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