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Modelled gridded population estimates for Tshopo Province in the Democratic Republic of Congo version 4.3

Modelled gridded population estimates for Tshopo Province in the Democratic Republic of Congo version 4.3
Modelled gridded population estimates for Tshopo 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-metre grid cells) for Tshopo 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 DRC for 2023, settlement extents 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 Tshopo. 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 Gates Foundation (INV-044979). Project partners included GRID3 Inc, 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 Mohamed Megheib with support from Ortis Yankey. Data processing was done by Mohamed Megheib and Tom Abbott with additional support from Heather Chamberlain. Project oversight was done by Attila Lazar, Chris Nnanatu 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, Tshopo, DRC
University of Southampton
Megheib, Mohamed
dc4da9bd-9e0d-4a1a-a3f0-b05fec3a50a4
Yankey, Ortis
9965d053-8afb-462f-b7fe-b270e21f2ec1
Nnanatu, Chris
24be7c1b-a677-4086-91b4-a9d9b1efa5a3
Abbott, Thomas
6dd117e8-cac5-4862-a3fd-ddbf1cbe94bb
Chamberlain, Heather
cb939de7-ac47-440e-aeb8-a2e36c110785
Lazar, Attila
d7f835e7-1e3d-4742-b366-af19cf5fc881
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Megheib, Mohamed
dc4da9bd-9e0d-4a1a-a3f0-b05fec3a50a4
Yankey, Ortis
9965d053-8afb-462f-b7fe-b270e21f2ec1
Nnanatu, Chris
24be7c1b-a677-4086-91b4-a9d9b1efa5a3
Abbott, Thomas
6dd117e8-cac5-4862-a3fd-ddbf1cbe94bb
Chamberlain, Heather
cb939de7-ac47-440e-aeb8-a2e36c110785
Lazar, Attila
d7f835e7-1e3d-4742-b366-af19cf5fc881
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Megheib, Mohamed, Yankey, Ortis, Nnanatu, Chris, Abbott, Thomas, Chamberlain, Heather, Lazar, Attila and Tatem, Andrew (2025) Modelled gridded population estimates for Tshopo Province in the Democratic Republic of Congo version 4.3. University of Southampton doi:10.5258/SOTON/WP00835 [Dataset]

Record type: Dataset

Abstract

This data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100-metre grid cells) for Tshopo 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 DRC for 2023, settlement extents 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 Tshopo. 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 Gates Foundation (INV-044979). Project partners included GRID3 Inc, 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 Mohamed Megheib with support from Ortis Yankey. Data processing was done by Mohamed Megheib and Tom Abbott with additional support from Heather Chamberlain. Project oversight was done by Attila Lazar, Chris Nnanatu 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, Tshopo, DRC

Identifiers

Local EPrints ID: 504250
URI: http://eprints.soton.ac.uk/id/eprint/504250
PURE UUID: 950c9cbd-59fa-4521-b55d-907acfe2a3e3
ORCID for Ortis Yankey: ORCID iD orcid.org/0000-0002-0808-884X
ORCID for Chris Nnanatu: ORCID iD orcid.org/0000-0002-5841-3700
ORCID for Heather Chamberlain: ORCID iD orcid.org/0000-0003-0828-6974
ORCID for Attila Lazar: ORCID iD orcid.org/0000-0003-2033-2013
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 02 Sep 2025 16:45
Last modified: 03 Sep 2025 02:05

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Contributors

Creator: Mohamed Megheib
Creator: Ortis Yankey ORCID iD
Creator: Chris Nnanatu ORCID iD
Creator: Thomas Abbott
Creator: Attila Lazar ORCID iD
Creator: Andrew Tatem ORCID iD

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