WorldPop

This project was initiated in 2021 to generate modelled population estimates for Papua New Guinea (PNG) to support their census preparations. It was powered by the Australian Government through the PNGAus partnership, the United Nations Population Fund (UNFPA ) and the PNG National Statistical Office. The project team combined recent 2019-2021 malaria bednet campaign data, urban structural listing 2021 data, and geospatial covariates to model and estimate population numbers at census unit level, and aggregate at other relevant administrative units (e.g., national, province, and districts) using a Bayesian statistical hierarchical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data hierarchy. It also allowed the quantification of uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the years 2020-21. This time period corresponds to the malaria survey and urban structural listing survey observations (2019-2021; median year: 2020) and the period of the satellite imagery used to generate settlement footprints (2021). 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 in collaboration with the National Statistical Office of PNG and UNFPA under the project called “Population-modelled estimation for Papua New Guinea in collaboration with the National Statistical Office, 2021-22” (PNG40-0000004504). The final statistical modelling was designed, developed, and implemented by Chris Nnanatu. Data processing was done by Amy Bonnie with additional support from Tom Abbott, Tom McKeen, Heather Chamberlain, Ortis Yankey, Duygu Cihan and Assane Gadiaga. Project oversight was done by Attila Lazar and Andy Tatem. Household survey listing data were provided by the National Statistical Office, and the settlement footprint was generated by Planet. Please, note that the same modelled population data (with minor rounding difference of 41 in the national total) can also be downloaded from the NSO’s website: https://www.nso.gov.pg/statistics/population/ How to cite this work WorldPop and National Statistical Office of Papua New Guinea. 2022. Census-independent population estimates for Papua New Guinea (2020-21), version 1.0. WorldPop, University of Southampton. DOI: 10.5258/SOTON/WP00763.

These data were produced by WorldPop at the University of Southampton. These data include gridded estimates of population at approximately 100m and 1km for 2022, along with estimates of the number of people belonging to individual age-sex groups. These results were produced using subnational population estimates for Sudan in 2022 provided in the Common Operational Dataset on Population Statistics (COD-PS) and built-up surfaces/volumes covariates extracted from GHSL datasets; GHS-BUILT-Surface epoch 2020 layer , combined with Digitize Africa building footprints, were used to delineate settled areas. The constrained and unconstrained top-down disaggregation method was used to produce the datasets, i.e. population was only estimated within areas classified as containing built settlement. The modelling work and geospatial data processing was led by Bondarenko M. and Leasure D.R.. Oversight was provided by Tatem A.J. For further details, please, read the Release Statement. Recommended citations Bondarenko M., Leasure D.R., and Tatem A.J. 2023 Gridded population estimates for Sudan using UN COD-PS estimates 2022, version 2.0. WorldPop, University of Southampton. doi:10.5258/SOTON/WP00761 License These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release@worldpop.org for more information. The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release@worldpop.org. WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.