Advances in small area population estimation in the absence of national census data

Advances in small area population estimation in the absence of national census data Edited by Kenneth W. Wachter, University of California Berkeley, Berkeley, CA; received December 16, 2025; accepted July 1, 2026 Abstract Population data at small area scales are essential for effective decision-making, influencing public health, disaster response, and resource allocation, among others. While national censuses remain the cornerstone of population data, they are often constrained by high costs, infrequent collection cycles, and coverage gaps, which can hinder timely data availability. To address these challenges, geospatial statistical approaches using limited microcensus surveys have been demonstrated as a reliable source, but the field has advanced substantially in recent years, with significant developments in both data sources and modeling methodologies. New approaches now leverage routine health intervention campaign data, satellite-derived settlement maps, and bespoke modeling approaches to produce reliable small area population estimates where enumeration is difficult or outdated. Various countries are applying these techniques to support census operations, health program planning, and humanitarian response. This manuscript reviews recent advances in “bottom-up” population mapping approaches, highlighting innovations in input data, modeling methods, and validation techniques. We examine ongoing challenges, including partial observation of buildings under forest canopy, population displacement, and institutional uptake. Finally, we discuss emerging opportunities to enhance these approaches through better integration with traditional data ecosystems, capacity strengthening, and coproduction with national institutions. Data, Materials, and Software Availability There are no data underlying this work. Acknowledgments Author contributions A.J.T., G.B., H.R.C., C.C.N., E.D., D.R.L., O.Y., A.G., S.J., L.d.l.R.R., J.E., and A.N.L. wrote the paper. Competing interests The authors declare no competing interest. References 1 M. Borowitz, J. Zhou, K. Azelton, I.-Y. Nassar, Examining the value of satellite data in halting transmission of polio in Nigeria: A socioeconomic analysis. Data Policy 5, e16 (2023). 2 S. P. Cumbane, G. Gidófalvi, Spatial distribution of displaced population estimated using mobile phone data to support disaster response activities. ISPRS Int. J. Geo Inf. 10, 421 (2021). 3 P. G. Greenough, E. L. Nelson, Beyond mapping: A case for geospatial analytics in humanitarian health. Conflict Health 13, 50 (2019). 4 T. A. Robin et al., Using spatial analysis and GIS to improve planning and resource allocation in a rural district of Bangladesh. BMJ Global Health 4, e000832 (2019). 5 USCB, “Post-enumeration survey and demographic analysis help evaluate 2020 census results” in Census Bureau Releases Estimates of Undercount and Overcount in the 2020 Census (United States Census Bureau, 2022). 6 S. Randall, Where have all the nomads gone? Fifty years of statistical and demographic invisibilities of African mobile pastoralists. Pastoralism 5, 22 (2015). 7 UNECE, Guidelines on the Use of Registers and Administrative Data for Population and Housing Censuses (Geneva, 2018). 8 UN, The Sustainable Development Goals Report 2024 (United Nations, New York, 2024). 9 UNFPA, The value of modeled population estimates for census planning and preparation. Technical Guidance note (United Nations Population Fund (UNFPA), New York, 2020). https://www.unfpa.org/sites/default/files/resource-pdf/V2_Technical-Guidance-Note_Value_of_Modeled_Pop_Estimates_in_Census.pdf. 10 N. A. Wardrop et al., Spatially disaggregated population estimates in the absence of national population and housing census data. Proc. Natl. Acad. Sci. U.S.A. 115, 3529–3537 (2018). 11 S. Tadesse, “The evolving census landscape: Lessons from the 2020 round and anticipated trends for the 2030 round” in United Nations Statistical Commission, 56th Session, Side Event: Advancing Population and Housing Censuses in the 2030 Round; New York, USA; 5 March 2025 (United Nations Statistics Division (UNSD), 2025). https://unstats.un.org/UNSDWebsite/statcom/session_56/side-events/HPC-side-event-5Mar2025/UNSD-PHCs-2020-lessons-and-2030-trends.pdf. 12 E. Jensen, T. Kennel, “Detailed Coverage Estimates for the 2020 Census Released Today” in America Counts: Stories (United States Census Bureau, 2022), 10 March 2022, https://www.census.gov/library/stories/2022/03/who-was-undercounted-overcounted-in-2020-census.html. 13 United Nations Economic and Social Council, Future of population and housing censuses and lessons learned from past and current experiences (E/ESCWA/C.1/2024/4) (United Nations, New York, 2024). 14 E. M. Weber et al., Census-independent population mapping in northern Nigeria. Remote Sens. Environ. 204, 786–798 (2018). 15 R. Hillson et al., Methods for determining the uncertainty of population estimates derived from satellite imagery and limited survey data: A case study of Bo city, Sierra Leone. PLoS One 9, e112241 (2014). 16 D. R. Leasure, W. C. Jochem, E. M. Weber, V. Seaman, A. J. Tatem, National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty. Proc. Natl. Acad. Sci. U.S.A. 117, 24173–24179 (2020). 17 G. Boo et al., High-resolution population estimation using household survey data and building footprints. Nat. Commun. 13, 1330 (2022). 18 G. Boo et al., Tackling public health data gaps through Bayesian high-resolution population estimation. PLoS Global Public Health 5, e0005072 (2025). 19 E. Darin, M. Kuépié, H. Bassinga, G. Boo, A. J. Tatem, La population vue du ciel: Quand l’imagerie satellite vient au secours du recensement. Population 77, 467–494 (2022). 20 L. M. Sanchez-Cespedes et al., Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia. Popul. Stud. 78, 3–20 (2024). 21 WorldPop, Institut National de la Statistique du Mali, Census-cartography-based gridded population estimates for Mali (2020), version 1.0 (WorldPop, University of Southampton, 2020). https://doi.org/10.5258/SOTON/WP00745, https://wopr.worldpop.org/?MLI/Population/v1.0. 22 IPAC, Numbers Matter: The 2020 Census and Conflict in Papua (Institute for Policy Analysis of Conflict Jakarta, 2019). 23 T. A. Sullivan, “Who, what, when, and where of the census” in Census 2020: Understanding the Issues, T. A. Sullivan, Ed. (Springer International Publishing, Cham, 2020), pp. 17–31, https://doi.org/10.1007/978-3-030-40578-6_2. 24 A. M. Wazir, A. Goujon, “Assessing the 2017 census of Pakistan using demographic analysis: A sub-national perspective” in Vienna Institute of Demography Working Papers No. 06/2019 (Vienna Institute of Demography, Vienna, 2019). 25 C. A. Dooley et al., Description of methods for the Zambia modelled population estimates from multiple routinely collected and geolocated survey data, version 1.0 (WorldPop, University of Southampton, 2021), https://doi.org/10.5258/SOTON/WP00707. 26 R. Engstrom, D. Newhouse, V. Soundararajan, Estimating small-area population density in Sri Lanka using surveys and Geo-spatial data. PLoS One 15, e0237063 (2020). 27 C. C. Nnanatu et al., Efficient Bayesian hierarchical small area population estimation using INLA-SPDE: Integrating multiple data sources and spatial-autocorrelation. Preprints (2025). https://doi.org/10.20944/preprints202501.0588.v1. 28 D. R. Leasure, C. A. Dooley, A. Tatem, A Simulation Study Exploring Weighted Likelihood Models to Recover Unbiased Population Estimates from Weighted Survey Data (University of Southampton, 2021). 29 C. C. Nnanatu et al., Estimating small area population from health intervention campaign surveys and partially observed settlement data. Nat. Commun. 16, 4951 (2025). 30 C. C. Nnanatu et al., Modelled gridded population estimates for Nigeria 2025 version 3.0. WorldPop, University of Southampton (2025). https://doi.org/10.5258/SOTON/WP00782. 31 M. Pesaresi, P. Politis, GHS-BUILT-S R2023A—GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) (dataset). European Commission, Joint Research Centre. https://doi.org/10.2905/JRC.939FACR. 32 D. Woods et al., WorldPop high resolution, harmonised annual global geospatial covariates. Version 1.0. University of Southampton, Southampton, UK (2025), https://doi.org/10.5258/SOTON/WP00772. 33 J. J. Nieves et al., Examining the correlates and drivers of human population distributions across low- and middle-income countries. J. R. Soc. Interface 14, 20170401 (2017). 34 W. Sirko et al., Continental-scale building detection from high resolution satellite imagery. arXiv [Preprint] (2021). https://doi.org/10.48550/arXiv.2107.12283 (Accessed 12 March 2026). 35 Microsoft, Worldwide building footprints derived from satellite imagery (GitHub Repository), (2025). https://github.com/microsoft/GlobalMLBuildingFootprints. 36 Ecopia, Global Feature Extraction: Building footprints, (2020). https://www.ecopiatech.com/products/global-feature-extraction. 37 X. X. Zhu, S. Chen, F. Zhang, Y. Shi, Y. Wang, GlobalBuildingAtlas: An open global and complete dataset of building polygons, heights and LoD1 3D models. arXiv [Preprint] (2025). https://doi.org/10.48550/arXiv.2506.04106 (Accessed 2 March 2026). 38 R. Hillson et al., Stratified sampling of neighborhood sections for population estimation: A case study of Bo City, Sierra Leone. PLoS One 10, e0132850 (2015). 39 W. C. Jochem et al., Classifying settlement types from multi-scale spatial patterns of building footprints. Environ. Plan. B: Urban Anal. City Sci. 48, 1161–1179 (2020). 40 C. T. Lloyd et al., Using GIS and machine learning to classify residential status of urban buildings in low and middle income settings. Remote Sens. 12, 3847 (2020). 41 L. Tomás, L. Fonseca, C. Almeida, F. Leonardi, M. Pereira, Urban population estimation based on residential buildings volume using IKONOS-2 images and lidar data. Int. J. Remote Sens. 37, 1–28 (2016). 42 F. Schug, D. Frantz, S. van der Linden, P. Hostert, Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLoS One 16, e0249044 (2021). 43 W. Sirko et al., High-resolution building and road detection from sentinel-2. arXiv [Preprint] (2024). https://doi.org/10.48550/arXiv.2310.11622 (Accessed 10 March 2026). 44 OSM, OpenStreetMap (2025). https://www.openstreetmap.org/#map=5/54.91/-3.43. 45 B. Herfort, S. Lautenbach, J. Porto de Albuquerque, J. Anderson, A. Zipf, The evolution of humanitarian mapping within the OpenStreetMap community. Sci. Rep. 11, 3037 (2021). 46 GRID3, GRID3 Data Hub (2026). https://data.grid3.org/. 47 ACLED, ACLED Data (2026). https://acleddata.com/. 48 C. Scher, J. Van Den Hoek, Nationwide conflict damage mapping with interferometric synthetic aperture radar: A study of the 2022 Russia-Ukraine conflict. Sci. Remote Sens. 11, 100217 (2022). 49 S. Al Shafian, D. Hu, Integrating machine learning and remote sensing in disaster management: A decadal review of post-disaster building damage assessment. Buildings 14, 2344 (2024). 50 L. Dong et al., Defining a city—Delineating urban areas using cell-phone data. Nat. Cities 1, 117–125 (2024). 51 N. N. Patel et al., Improving large area population mapping using geotweet densities. Trans. GIS 21, 317–331 (2017). 52 C. F. Brown et al., Dynamic world, near real-time global 10 m land use land cover mapping. Sci. Data 9, 251 (2022). 53 D. Stathakis, P. Baltas, Seasonal population estimates based on night-time lights. Comput. Environ. Urban Syst. 68, 133–141 (2018). 54 D. R. Leasure, C. A. Dooley, M. Bondarenko, A. J. Tatem, peanutButter: An R package to produce rapid-response gridded population estimates from building footprints, version 1.0.0 (WorldPop, University of Southampton, 2021), https://doi.org/10.5258/SOTON/WP00717, https://github.com/wpgp/peanutButter. 55 E. Darin, D. R. Leasure, R. Kashyap, How accurate are high resolution settlement maps at predicting population counts in data scarce settings. OSF [Preprint] (2025). https://doi.org/10.31219/osf.io/pz38h_v1 (Accessed 10 March 2026). 56 J. V. Tuccillo et al., LandScan HD: A high-resolution gridded ambient population methodology for the world. Popul. Environ. 47, 42 (2025). 57 P. Doupe, E. Bruzelius, J. Faghmous, S. G. Ruchman, “Equitable development through deep learning: The case of sub-national population density estimation” in Proceedings of the 7th Annual Symposium on Computing for Development (Association for Computing Machinery, Nairobi, Kenya, 2016), p. 6. 58 W. Hu et al., “Mapping missing population in rural India: A deep learning approach with satellite imagery” in Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (Association for Computing Machinery, Honolulu, HI, 2019), pp. 353–359. 59 C. Robinson, F. Hohman, B. Dilkina, “A deep learning approach for population estimation from satellite imagery” in Proceedings of the 1st ACM SIGSPATIAL Workshop on Geospatial Humanities (Association for Computing Machinery, Redondo Beach, CA, 2017), pp. 47–54. 60 N. Ahmed et al., Artificial neural network and machine learning based methods for population estimation of Rohingya refugees: Comparing data-driven and satellite image-driven approaches. Vietnam J. Comput. Sci. 06, 439–455 (2019). 61 I. Neal, S. Seth, G. Watmough, M. S. Diallo, Census-independent population estimation using representation learning. Sci. Rep. 12, 5185 (2022). 62 C. Pezzulo et al., Sub-national mapping of population pyramids and dependency ratios in Africa and Asia. Sci. Data 4, 170089 (2017). 63 V. A. Alegana et al., Fine resolution mapping of population age-structures for health and development applications. J. R. Soc. Interface 12, 20150073 (2015). 64 C. C. Nnanatu, S. Chaudhuri, A. N. Lazar, A. J. Tatem, jollofR: A Bayesian statistical model-based approach for disaggregating small area population estimates by demographic characteristics (R package version 0.3.0, WorldPop, University of Southampton, UK, 2025). https://github.com/wpgp/jollofR/. 65 C. Nnanatu et al., Modelled gridded population estimates for Cameroon 2022. Version 1.0 (University of Southampton, 2024), 17 Jun 2024, https://doi.org/10.5258/SOTON/WP00784, https://data.worldpop.org/repo/wopr/CMR/population/v1.0/. 66 M. Viljanen, L. Meijerink, L. Zwakhals, J. van de Kassteele, A machine learning approach to small area estimation: Predicting the health, housing and well-being of the population of Netherlands. Int. J. Health Geogr. 21, 4 (2022). 67 H. R. Chamberlain, A. N. Lazar, A. J. Tatem, High-resolution estimates of social distancing feasibility, mapped for urban areas in sub-Saharan Africa. Sci. Data 9, 711 (2022). 68 V. A. Alegana, C. Pezzulo, A. J. Tatem, B. Omar, A. Christensen, Mapping out-of-school adolescents and youths in low- and middle-income countries. Hum. Soc. Sci. Commun. 8, 213 (2021). 69 P. M. Macharia et al., Modelling geographic access and school catchment areas across public primary schools to support subnational planning in Kenya. Child. Geogr. 21, 832–848 (2023). 70 A. Smith et al., New estimates of flood exposure in developing countries using high-resolution population data. Nat. Commun. 10, 1814 (2019). 71 F. Hierink, N. Rodrigues, M. Muñiz, R. Panciera, N. Ray, Modelling geographical accessibility to support disaster response and rehabilitation of a healthcare system: An impact analysis of Cyclones Idai and Kenneth in Mozambique. BMJ Open 10, e039138 (2020). 72 S. H. Qader et al., Using gridded population and quadtree sampling units to support survey sample design in low-income settings. Int. J. Health Geogr. 19, 10 (2020). 73 J. Cajka, A. Safaa, R. Jamie, J. Allpress, Geo-sampling in developing nations. Int. J. Soc. Res. Methodol. 21, 729–746 (2018). 74 O. Borkovska et al., Developing high-resolution population and settlement data for impactful Malaria interventions in Zambia. J. Environ. Public Health 2022, 2941013 (2022). 75 UCLA-DRC Health Research and Training Program, Kinshasa School of Public Health, Kinshasa, Kongo Central and former Bandundu microcensus survey data (2017-18). (UCLA, Los Angeles, 2018). 76 Institut de la Statistique du Mali 2021. Cartographie du RGPH5. INSTAT, Bamako, Mali. https://www.instat-mali.org/fr/actualites-et-evenements/cartographie-rgph5. 77 H. R. Chamberlain, C. A. Dooley, F. Kakungu, A. J. Tatem, Assessing the accuracy of census-independent small area modelled population datasets. Research Square [Preprint] (Version 1) (2026). https://doi.org/10.21203/rs.3.rs-9558335/v1 (Accessed 5 May 2026). 78 J. M. Davis, M. Wilfahrt, Enumerator experiences in violent research environments. Comp. Polit. Stud. 57, 675–709 (2024). 79 DESA, Guidelines on the use of electronic data collection technologies in population and housing censuses (United Nations, Department of Economic and Social Affairs Statistics Division, New York, 2019). 80 H. Hogan, Distrust in the governments brings risk to the census. Harvard Data Sci. Rev. 2 (2020), https://doi.org/10.1162/99608f99692.99611f99603e99977. 81 H. B. Aguma et al., Mass distribution campaign of long-lasting insecticidal nets (LLINs) during the COVID-19 pandemic in Uganda: Lessons learned. Malar. J. 22, 310 (2023). 82 A. J. Tatem, J. Espey, Global population data is in crisis—Here’s why that matters. https://theconversation.com/global-population-data-is-in-crisis-heres-why-that-matters-251751. 26 March 2025. 83 P. Deville et al., Dynamic population mapping using mobile phone data. Proc. Natl. Acad. Sci. U.S.A. 111, 15888–15893 (2014). 84 N. W. Ruktanonchai, C. W. Ruktanonchai, J. R. Floyd, A. J. Tatem, Using Google location history data to quantify fine-scale human mobility. Int. J. Health Geogr. 17, 28 (2018). 85 M. Sinclair et al., Assessing the socio-demographic representativeness of mobile phone application data. Appl. Geogr. 158, 102997 (2023). 86 D. R. Leasure et al., Nowcasting daily population displacement in Ukraine through social media advertising data. Popul. Dev. Rev. 49, 231–254 (2023). 87 G. Chi, G. J. Abel, D. Johnston, E. Giraudy, M. Bailey, Measuring global migration flows using online data. Proc. Natl. Acad. Sci. U.S.A. 122, e2409418122 (2025). 88 R. Hosner, Z. Strain-Fajth, V. Lefebvre; Flowminder Foundation, “Using survey data to correct for representation biases in mobility indicators derived from mobile operator data to produce high-frequency estimates of population and internal migration” in Netmob (Flowminder Foundation, Stockholm, Sweden, 2023). https://doi.org/10.5281/zenodo.8414414. 89 P. Andrich et al., Social media data for population mapping: A Bayesian approach to address representativeness and privacy challenges. arXiv [Preprint] (2026). https://arxiv.org/abs/2601.22104 (Accessed 10 May 2026). 90 H. R. Chamberlain et al., Building footprint data for countries in Africa: To what extent are existing data products comparable? Comput. Environ. Urban Syst. 110, 102104 (2024). 91 M. Minghini, S. Thabit Gonzalez, L. Gabrielli, Pan-European open building footprints: Analysis and comparison in selected countries. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLVIII-4/W12-2024, 97–103 (2024). 92 H. R. Chamberlain et al., Assessing the impact of building footprint dataset choice for health programme planning: A case study of indoor residual spraying (IRS) in Zambia. Int. J. Health Geogr. 24, 13 (2025). 93 C. Visée et al., Addressing bias in national population density models: Focusing on rural Senegal. PLoS One 19, e0310809 (2024). 94 D. R. Thomson et al., Evaluating the accuracy of gridded population estimates in slums: A case study in Nigeria and Kenya. Urban Sci. 5, 48 (2021). 95 M. Owusu et al., Towards user-driven earth observation-based slum mapping. Comput. Environ. Urban Syst. 89, 101681 (2021). 96 UNHCR, Global Trends: Forced Displacement in 2023 (United Nations High Commissioner for Refugees, Copenhagen, Denmark, 2024). 97 C. A. Dooley, W. C. Jochem, A. Sorichetta, A. N. Lazar, A. Tatem, Description of methods for South Sudan 2020 gridded population estimates from census projections adjusted for displacement, version 2.0 (WorldPop, University of Southampton, 2021), https://doi.org/10.5258/SOTON/WP00710. 98 J. A. Quinn et al., Humanitarian applications of machine learning with remote-sensing data: Review and case study in refugee settlement mapping. Philos. Trans. A Math. Phys. Eng. Sci. 376, 20170363 (2018). 99 E. Darin et al., Mapping refugee populations at high resolution by unlocking humanitarian administrative data. J. Int. Hum. Action 9, 14 (2024). 100 C. Donegan, Y. Chun, A. E. Hughes, Bayesian estimation of spatial filters with Moran’s eigenvectors and hierarchical shrinkage priors. Spatial Stat. 38, 100450 (2020). 101 K. D. Mets, D. Armenteras, L. M. Dávalos, Spatial autocorrelation reduces model precision and predictive power in deforestation analyses. Ecosphere 8, e01824 (2017). 102 S. Openshaw, “The modifiable areal unit problem” in Concepts and Techniques in Modern Geography (Geobooks, Norwich, UK, 1984). 103 J. Paige, G.-A. Fuglstad, A. Riebler, J. Wakefield, Spatial aggregation with respect to a population distribution: Impact on inference. Spatial Stat. 52, 100714 (2022). 104 D. Manley, “Scale, aggregation, and the modifiable areal unit problem” in Handbook of Regional Science, M. M. Fischer, P. Nijkamp, Eds. (Springer Berlin Heidelberg, Berlin, Heidelberg, 2014), pp. 1157–1171, https://doi.org/10.1007/978-3-642-23430-9_69. 105 M. O. Kimberley, M. S. Watt, D. Harrison, Characterising prediction error as a function of scale in spatial surfaces of tree productivity. N. Z. J. For. Sci. 47, 19 (2017). 106 Project on Government Oversight, Dollars and Demographics: How Census Data Shapes Federal Funding Distribution (POGO, Washington, DC, 2023). 107 T. Krieger, D. Meierrieks, Population size and the size of government. Eur. J. Polit. Econ. 61, 101837 (2020). 108 C. Tuholske et al., Implications for tracking SDG indicator metrics with gridded population data. Sustainability 13, 7329 (2021). 109 E. Darin, Demographic figures at risk in the digital era: Resisting commodification, reclaiming the common good. Big Data Soc. 12, (2025), https://doi.org/10.1177/20539517251403948. 110 A. M. van der Bles et al., Communicating uncertainty about facts, numbers and science. R. Soc. Open Sci. 6, 181870 (2019). 111 V. Kudakwashe Paul, H. Kate, G. Matthew, P. Helen, K. Lynda, Local ownership of health policy and systems research in low-income and middle-income countries: A missing element in the uptake debate. BMJ Global Health 4, e001523 (2019). 112 B. Knittel et al., Critical barriers to sustainable capacity strengthening in global health: A systems perspective on development assistance. Gates Open Res. 6, 116 (2022). 113 B. Harrell-Bond, E. Voutira, M. Leopold, Counting the refugees: Gifts, givers, patrons and clients. J. Refug. Stud. 5, 205–225 (1992). 114 C. N. Mayemba et al., A short survey of human mobility prediction in epidemic modeling from transformers to LLMs. arXiv [Preprint] (2024). https://doi.org/10.48550/arXiv.2404.16921 (ver 1, 25 April 2024). 115 D. Leasure, M. Bondarenko, E. Darin, A. Tatem, wopr: An R package to query the WorldPop Open Population Repository, version 1.3.3 (WorldPop, University of Southampton, 2021), https://doi.org/10.5258/SOTON/WP00716, https://github.com/wpgp/wopr. 116 Institut National de la Statistique et de la Démographie du Burkina Faso, Volume I: Evaluation de la qualite des donnees, Etat, Structure et Dynamique de la population (2022). https://www.insd.bf/fr/resultats. 117 Institut de la Statistique du Mali, Resultats Globaux du RGPH5 (2023). https://www.instat-mali.org/laravel-filemanager/files/shares/rgph/rapport-resultats-globaux-rgph5_rgph.pdf. 118 PNG National Statistical Office, Population estimates (2021). https://www.nso.gov.pg/statistics/population/. 119 C. Ninrew, S. Sudan population is 12.4 million-govt estimates (2023). https://www.eyeradio.org/s-sudan-population-is-12-4-million-govt-estimates/. 120 WHO, UNICEF, Geo-Enabled Microplanning Handbook: A product of the WHO-UNICEF COVAX (WHO-UNICEF COVAX GIS Working Group, 2023). 121 UNICEF, Reach the unreached—Geospatial modelling mapping methods (2025). https://github.com/unicef-drp/reach-the-unreached?tab=readme-ov-file. 122 E. Darin, D. R. Leasure, A. J. Tatem, Statistical population modelling for census support (United Nations Population Fund (UNFPA), Leverhulme Centre for Demographic Science, University of Oxford, and WorldPop, University of Southampton, 2023), https://wpgp.github.io/bottom-up-tutorial/, https://doi.org/10.5281/zenodo.7945266. 123 J. M. Espey, A. J. Tatem, D. R. Thomson, Disappearing people: A global demographic data crisis threatens public policy. Science 388, 1277–1280 (2025). 124 A. Gutierrez, ECLAC and UNFPA approach to model populations in Latin America and the Caribbean, IAOS-ISI, Mexico City (2024). https://www.inegi.org.mx/eventos/2024/iaos-isi/doc/34.pdf. Information & Authors Information Published in Classifications Copyright Copyright © 2026 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY). Data, Materials, and Software Availability There are no data underlying this work. Submission history Published online: August 21, 2026 Published in issue: September 1, 2026 Keywords Acknowledgments Author contributions A.J.T., G.B., H.R.C., C.C.N., E.D., D.R.L., O.Y., A.G., S.J., L.d.l.R.R., J.E., and A.N.L. wrote the paper. Competing interests The authors declare no competing interest. Notes This article is a PNAS Direct Submission. PNAS policy is to publish maps as provided by the authors. Authors Metrics & Citations Metrics Altmetrics Citations Cite this article Advances in small area population estimation in the absence of national census data, Proc. Natl. Acad. Sci. U.S.A. 123 (35) e2413993123, https://doi.org/10.1073/pnas.2413993123 (2026). Copied! Copying failed. Export the article citation data by selecting a format from the list below and clicking Export. View Options View options Download this article as a PDF file. PDFPDF and Supporting Information Download the article PDF and supporting information. Download PDF and supporting informationeReader View this article with eReader. eReaderFigures Tables Media References References References 1 M. Borowitz, J. Zhou, K. Azelton, I.-Y. Nassar, Examining the value of satellite data in halting transmission of polio in Nigeria: A socioeconomic analysis. Data Policy 5, e16 (2023). 2 S. P. Cumbane, G. Gidófalvi, Spatial distribution of displaced population estimated using mobile phone data to support disaster response activities. ISPRS Int. J. Geo Inf. 10, 421 (2021). 3 P. G. Greenough, E. L. Nelson, Beyond mapping: A case for geospatial analytics in humanitarian health. Conflict Health 13, 50 (2019). 4 T. A. Robin et al., Using spatial analysis and GIS to improve planning and resource allocation in a rural district of Bangladesh. BMJ Global Health 4, e000832 (2019). 5 USCB, “Post-enumeration survey and demographic analysis help evaluate 2020 census results” in Census Bureau Releases Estimates of Undercount and Overcount in the 2020 Census (United States Census Bureau, 2022). 6 S. Randall, Where have all the nomads gone? Fifty years of statistical and demographic invisibilities of African mobile pastoralists. Pastoralism 5, 22 (2015). 7 UNECE, Guidelines on the Use of Registers and Administrative Data for Population and Housing Censuses (Geneva, 2018). 8 UN, The Sustainable Development Goals Report 2024 (United Nations, New York, 2024). 9 UNFPA, The value of modeled population estimates for census planning and preparation. Technical Guidance note (United Nations Population Fund (UNFPA), New York, 2020). https://www.unfpa.org/sites/default/files/resource-pdf/V2_Technical-Guidance-Note_Value_of_Modeled_Pop_Estimates_in_Census.pdf. 10 N. A. Wardrop et al., Spatially disaggregated population estimates in the absence of national population and housing census data. Proc. Natl. Acad. Sci. U.S.A. 115, 3529–3537 (2018). 11 S. Tadesse, “The evolving census landscape: Lessons from the 2020 round and anticipated trends for the 2030 round” in United Nations Statistical Commission, 56th Session, Side Event: Advancing Population and Housing Censuses in the 2030 Round; New York, USA; 5 March 2025 (United Nations Statistics Division (UNSD), 2025). https://unstats.un.org/UNSDWebsite/statcom/session_56/side-events/HPC-side-event-5Mar2025/UNSD-PHCs-2020-lessons-and-2030-trends.pdf. 12 E. Jensen, T. Kennel, “Detailed Coverage Estimates for the 2020 Census Released Today” in America Counts: Stories (United States Census Bureau, 2022), 10 March 2022, https://www.census.gov/library/stories/2022/03/who-was-undercounted-overcounted-in-2020-census.html. 13 United Nations Economic and Social Council, Future of population and housing censuses and lessons learned from past and current experiences (E/ESCWA/C.1/2024/4) (United Nations, New York, 2024). 14 E. M. Weber et al., Census-independent population mapping in northern Nigeria. Remote Sens. Environ. 204, 786–798 (2018). 15 R. Hillson et al., Methods for determining the uncertainty of population estimates derived from satellite imagery and limited survey data: A case study of Bo city, Sierra Leone. PLoS One 9, e112241 (2014). 16 D. R. Leasure, W. C. Jochem, E. M. Weber, V. Seaman, A. J. Tatem, National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty. Proc. Natl. Acad. Sci. U.S.A. 117, 24173–24179 (2020). 17 G. Boo et al., High-resolution population estimation using household survey data and building footprints. Nat. Commun. 13, 1330 (2022). 18 G. Boo et al., Tackling public health data gaps through Bayesian high-resolution population estimation. PLoS Global Public Health 5, e0005072 (2025). 19 E. Darin, M. Kuépié, H. Bassinga, G. Boo, A. J. Tatem, La population vue du ciel: Quand l’imagerie satellite vient au secours du recensement. Population 77, 467–494 (2022). 20 L. M. Sanchez-Cespedes et al., Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia. Popul. Stud. 78, 3–20 (2024). 21 WorldPop, Institut National de la Statistique du Mali, Census-cartography-based gridded population estimates for Mali (2020), version 1.0 (WorldPop, University of Southampton, 2020). https://doi.org/10.5258/SOTON/WP00745, https://wopr.worldpop.org/?MLI/Population/v1.0. 22 IPAC, Numbers Matter: The 2020 Census and Conflict in Papua (Institute for Policy Analysis of Conflict Jakarta, 2019). 23 T. A. Sullivan, “Who, what, when, and where of the census” in Census 2020: Understanding the Issues, T. A. Sullivan, Ed. (Springer International Publishing, Cham, 2020), pp. 17–31, https://doi.org/10.1007/978-3-030-40578-6_2. 24 A. M. Wazir, A. Goujon, “Assessing the 2017 census of Pakistan using demographic analysis: A sub-national perspective” in Vienna Institute of Demography Working Papers No. 06/2019 (Vienna Institute of Demography, Vienna, 2019). 25 C. A. Dooley et al., Description of methods for the Zambia modelled population estimates from multiple routinely collected and geolocated survey data, version 1.0 (WorldPop, University of Southampton, 2021), https://doi.org/10.5258/SOTON/WP00707. 26 R. Engstrom, D. Newhouse, V. Soundararajan, Estimating small-area population density in Sri Lanka using surveys and Geo-spatial data. PLoS One 15, e0237063 (2020). 27 C. C. Nnanatu et al., Efficient Bayesian hierarchical small area population estimation using INLA-SPDE: Integrating multiple data sources and spatial-autocorrelation. Preprints (2025). https://doi.org/10.20944/preprints202501.0588.v1. 28 D. R. Leasure, C. A. Dooley, A. Tatem, A Simulation Study Exploring Weighted Likelihood Models to Recover Unbiased Population Estimates from Weighted Survey Data (University of Southampton, 2021). 29 C. C. Nnanatu et al., Estimating small area population from health intervention campaign surveys and partially observed settlement data. Nat. Commun. 16, 4951 (2025). 30 C. C. Nnanatu et al., Modelled gridded population estimates for Nigeria 2025 version 3.0. WorldPop, University of Southampton (2025). https://doi.org/10.5258/SOTON/WP00782. 31 M. Pesaresi, P. Politis, GHS-BUILT-S R2023A—GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) (dataset). European Commission, Joint Research Centre. https://doi.org/10.2905/JRC.939FACR. 32 D. Woods et al., WorldPop high resolution, harmonised annual global geospatial covariates. Version 1.0. University of Southampton, Southampton, UK (2025), https://doi.org/10.5258/SOTON/WP00772. 33 J. J. Nieves et al., Examining the correlates and drivers of human population distributions across low- and middle-income countries. J. R. Soc. Interface 14, 20170401 (2017). 34 W. Sirko et al., Continental-scale building detection from high resolution satellite imagery. arXiv [Preprint] (2021). https://doi.org/10.48550/arXiv.2107.12283 (Accessed 12 March 2026). 35 Microsoft, Worldwide building footprints derived from satellite imagery (GitHub Repository), (2025). https://github.com/microsoft/GlobalMLBuildingFootprints. 36 Ecopia, Global Feature Extraction: Building footprints, (2020). https://www.ecopiatech.com/products/global-feature-extraction. 37 X. X. Zhu, S. Chen, F. Zhang, Y. Shi, Y. Wang, GlobalBuildingAtlas: An open global and complete dataset of building polygons, heights and LoD1 3D models. arXiv [Preprint] (2025). https://doi.org/10.48550/arXiv.2506.04106 (Accessed 2 March 2026). 38 R. Hillson et al., Stratified sampling of neighborhood sections for population estimation: A case study of Bo City, Sierra Leone. PLoS One 10, e0132850 (2015). 39 W. C. Jochem et al., Classifying settlement types from multi-scale spatial patterns of building footprints. Environ. Plan. B: Urban Anal. City Sci. 48, 1161–1179 (2020). 40 C. T. Lloyd et al., Using GIS and machine learning to classify residential status of urban buildings in low and middle income settings. Remote Sens. 12, 3847 (2020). 41 L. Tomás, L. Fonseca, C. Almeida, F. Leonardi, M. Pereira, Urban population estimation based on residential buildings volume using IKONOS-2 images and lidar data. Int. J. Remote Sens. 37, 1–28 (2016). 42 F. Schug, D. Frantz, S. van der Linden, P. Hostert, Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLoS One 16, e0249044 (2021). 43 W. Sirko et al., High-resolution building and road detection from sentinel-2. arXiv [Preprint] (2024). https://doi.org/10.48550/arXiv.2310.11622 (Accessed 10 March 2026). 44 OSM, OpenStreetMap (2025). https://www.openstreetmap.org/#map=5/54.91/-3.43. 45 B. Herfort, S. Lautenbach, J. Porto de Albuquerque, J. Anderson, A. Zipf, The evolution of humanitarian mapping within the OpenStreetMap community. Sci. Rep. 11, 3037 (2021). 46 GRID3, GRID3 Data Hub (2026). https://data.grid3.org/. 47 ACLED, ACLED Data (2026). https://acleddata.com/. 48 C. Scher, J. Van Den Hoek, Nationwide conflict damage mapping with interferometric synthetic aperture radar: A study of the 2022 Russia-Ukraine conflict. Sci. Remote Sens. 11, 100217 (2022). 49 S. Al Shafian, D. Hu, Integrating machine learning and remote sensing in disaster management: A decadal review of post-disaster building damage assessment. Buildings 14, 2344 (2024). 50 L. Dong et al., Defining a city—Delineating urban areas using cell-phone data. Nat. Cities 1, 117–125 (2024). 51 N. N. Patel et al., Improving large area population mapping using geotweet densities. Trans. GIS 21, 317–331 (2017). 52 C. F. Brown et al., Dynamic world, near real-time global 10 m land use land cover mapping. Sci. Data 9, 251 (2022). 53 D. Stathakis, P. Baltas, Seasonal population estimates based on night-time lights. Comput. Environ. Urban Syst. 68, 133–141 (2018). 54 D. R. Leasure, C. A. Dooley, M. Bondarenko, A. J. Tatem, peanutButter: An R package to produce rapid-response gridded population estimates from building footprints, version 1.0.0 (WorldPop, University of Southampton, 2021), https://doi.org/10.5258/SOTON/WP00717, https://github.com/wpgp/peanutButter. 55 E. Darin, D. R. Leasure, R. Kashyap, How accurate are high resolution settlement maps at predicting population counts in data scarce settings. OSF [Preprint] (2025). https://doi.org/10.31219/osf.io/pz38h_v1 (Accessed 10 March 2026). 56 J. V. Tuccillo et al., LandScan HD: A high-resolution gridded ambient population methodology for the world. Popul. Environ. 47, 42 (2025). 57 P. Doupe, E. Bruzelius, J. Faghmous, S. G. Ruchman, “Equitable development through deep learning: The case of sub-national population density estimation” in Proceedings of the 7th Annual Symposium on Computing for Development (Association for Computing Machinery, Nairobi, Kenya, 2016), p. 6. 58 W. Hu et al., “Mapping missing population in rural India: A deep learning approach with satellite imagery” in Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (Association for Computing Machinery, Honolulu, HI, 2019), pp. 353–359. 59 C. Robinson, F. Hohman, B. Dilkina, “A deep learning approach for population estimation from satellite imagery” in Proceedings of the 1st ACM SIGSPATIAL Workshop on Geospatial Humanities (Association for Computing Machinery, Redondo Beach, CA, 2017), pp. 47–54. 60 N. Ahmed et al., Artificial neural network and machine learning based methods for population estimation of Rohingya refugees: Comparing data-driven and satellite image-driven approaches. Vietnam J. Comput. Sci. 06, 439–455 (2019). 61 I. Neal, S. Seth, G. Watmough, M. S. Diallo, Census-independent population estimation using representation learning. Sci. Rep. 12, 5185 (2022). 62 C. Pezzulo et al., Sub-national mapping of population pyramids and dependency ratios in Africa and Asia. Sci. Data 4, 170089 (2017). 63 V. A. Alegana et al., Fine resolution mapping of population age-structures for health and development applications. J. R. Soc. Interface 12, 20150073 (2015). 64 C. C. Nnanatu, S. Chaudhuri, A. N. Lazar, A. J. Tatem, jollofR: A Bayesian statistical model-based approach for disaggregating small area population estimates by demographic characteristics (R package version 0.3.0, WorldPop, University of Southampton, UK, 2025). https://github.com/wpgp/jollofR/. 65 C. Nnanatu et al., Modelled gridded population estimates for Cameroon 2022. Version 1.0 (University of Southampton, 2024), 17 Jun 2024, https://doi.org/10.5258/SOTON/WP00784, https://data.worldpop.org/repo/wopr/CMR/population/v1.0/. 66 M. Viljanen, L. Meijerink, L. Zwakhals, J. van de Kassteele, A machine learning approach to small area estimation: Predicting the health, housing and well-being of the population of Netherlands. Int. J. Health Geogr. 21, 4 (2022). 67 H. R. Chamberlain, A. N. Lazar, A. J. Tatem, High-resolution estimates of social distancing feasibility, mapped for urban areas in sub-Saharan Africa. Sci. Data 9, 711 (2022). 68 V. A. Alegana, C. Pezzulo, A. J. Tatem, B. Omar, A. Christensen, Mapping out-of-school adolescents and youths in low- and middle-income countries. Hum. Soc. Sci. Commun. 8, 213 (2021). 69 P. M. Macharia et al., Modelling geographic access and school catchment areas across public primary schools to support subnational planning in Kenya. Child. Geogr. 21, 832–848 (2023). 70 A. Smith et al., New estimates of flood exposure in developing countries using high-resolution population data. Nat. Commun. 10, 1814 (2019). 71 F. Hierink, N. Rodrigues, M. Muñiz, R. Panciera, N. Ray, Modelling geographical accessibility to support disaster response and rehabilitation of a healthcare system: An impact analysis of Cyclones Idai and Kenneth in Mozambique. BMJ Open 10, e039138 (2020). 72 S. H. Qader et al., Using gridded population and quadtree sampling units to support survey sample design in low-income settings. Int. J. Health Geogr. 19, 10 (2020). 73 J. Cajka, A. Safaa, R. Jamie, J. Allpress, Geo-sampling in developing nations. Int. J. Soc. Res. Methodol. 21, 729–746 (2018). 74 O. Borkovska et al., Developing high-resolution population and settlement data for impactful Malaria interventions in Zambia. J. Environ. Public Health 2022, 2941013 (2022). 75 UCLA-DRC Health Research and Training Program, Kinshasa School of Public Health, Kinshasa, Kongo Central and former Bandundu microcensus survey data (2017-18). (UCLA, Los Angeles, 2018). 76 Institut de la Statistique du Mali 2021. Cartographie du RGPH5. INSTAT, Bamako, Mali. https://www.instat-mali.org/fr/actualites-et-evenements/cartographie-rgph5. 77 H. R. Chamberlain, C. A. Dooley, F. Kakungu, A. J. Tatem, Assessing the accuracy of census-independent small area modelled population datasets. Research Square [Preprint] (Version 1) (2026). https://doi.org/10.21203/rs.3.rs-9558335/v1 (Accessed 5 May 2026). 78 J. M. Davis, M. Wilfahrt, Enumerator experiences in violent research environments. Comp. Polit. Stud. 57, 675–709 (2024). 79 DESA, Guidelines on the use of electronic data collection technologies in population and housing censuses (United Nations, Department of Economic and Social Affairs Statistics Division, New York, 2019). 80 H. Hogan, Distrust in the governments brings risk to the census. Harvard Data Sci. Rev. 2 (2020), https://doi.org/10.1162/99608f99692.99611f99603e99977. 81 H. B. Aguma et al., Mass distribution campaign of long-lasting insecticidal nets (LLINs) during the COVID-19 pandemic in Uganda: Lessons learned. Malar. J. 22, 310 (2023). 82 A. J. Tatem, J. Espey, Global population data is in crisis—Here’s why that matters. https://theconversation.com/global-population-data-is-in-crisis-heres-why-that-matters-251751. 26 March 2025. 83 P. Deville et al., Dynamic population mapping using mobile phone data. Proc. Natl. Acad. Sci. U.S.A. 111, 15888–15893 (2014). 84 N. W. Ruktanonchai, C. W. Ruktanonchai, J. R. Floyd, A. J. Tatem, Using Google location history data to quantify fine-scale human mobility. Int. J. Health Geogr. 17, 28 (2018). 85 M. Sinclair et al., Assessing the socio-demographic representativeness of mobile phone application data. Appl. Geogr. 158, 102997 (2023). 86 D. R. Leasure et al., Nowcasting daily population displacement in Ukraine through social media advertising data. Popul. Dev. Rev. 49, 231–254 (2023). 87 G. Chi, G. J. Abel, D. Johnston, E. Giraudy, M. Bailey, Measuring global migration flows using online data. Proc. Natl. Acad. Sci. U.S.A. 122, e2409418122 (2025). 88 R. Hosner, Z. Strain-Fajth, V. Lefebvre; Flowminder Foundation, “Using survey data to correct for representation biases in mobility indicators derived from mobile operator data to produce high-frequency estimates of population and internal migration” in Netmob (Flowminder Foundation, Stockholm, Sweden, 2023). https://doi.org/10.5281/zenodo.8414414. 89 P. Andrich et al., Social media data for population mapping: A Bayesian approach to address representativeness and privacy challenges. arXiv [Preprint] (2026). https://arxiv.org/abs/2601.22104 (Accessed 10 May 2026). 90 H. R. Chamberlain et al., Building footprint data for countries in Africa: To what extent are existing data products comparable? Comput. Environ. Urban Syst. 110, 102104 (2024). 91 M. Minghini, S. Thabit Gonzalez, L. Gabrielli, Pan-European open building footprints: Analysis and comparison in selected countries. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLVIII-4/W12-2024, 97–103 (2024). 92 H. R. Chamberlain et al., Assessing the impact of building footprint dataset choice for health programme planning: A case study of indoor residual spraying (IRS) in Zambia. Int. J. Health Geogr. 24, 13 (2025). 93 C. Visée et al., Addressing bias in national population density models: Focusing on rural Senegal. PLoS One 19, e0310809 (2024). 94 D. R. Thomson et al., Evaluating the accuracy of gridded population estimates in slums: A case study in Nigeria and Kenya. Urban Sci. 5, 48 (2021). 95 M. Owusu et al., Towards user-driven earth observation-based slum mapping. Comput. Environ. Urban Syst. 89, 101681 (2021). 96 UNHCR, Global Trends: Forced Displacement in 2023 (United Nations High Commissioner for Refugees, Copenhagen, Denmark, 2024). 97 C. A. Dooley, W. C. Jochem, A. Sorichetta, A. N. Lazar, A. Tatem, Description of methods for South Sudan 2020 gridded population estimates from census projections adjusted for displacement, version 2.0 (WorldPop, University of Southampton, 2021), https://doi.org/10.5258/SOTON/WP00710. 98 J. A. Quinn et al., Humanitarian applications of machine learning with remote-sensing data: Review and case study in refugee settlement mapping. Philos. Trans. A Math. Phys. Eng. Sci. 376, 20170363 (2018). 99 E. Darin et al., Mapping refugee populations at high resolution by unlocking humanitarian administrative data. J. Int. Hum. Action 9, 14 (2024). 100 C. Donegan, Y. Chun, A. E. Hughes, Bayesian estimation of spatial filters with Moran’s eigenvectors and hierarchical shrinkage priors. Spatial Stat. 38, 100450 (2020). 101 K. D. Mets, D. Armenteras, L. M. Dávalos, Spatial autocorrelation reduces model precision and predictive power in deforestation analyses. Ecosphere 8, e01824 (2017). 102 S. Openshaw, “The modifiable areal unit problem” in Concepts and Techniques in Modern Geography (Geobooks, Norwich, UK, 1984). 103 J. Paige, G.-A. Fuglstad, A. Riebler, J. Wakefield, Spatial aggregation with respect to a population distribution: Impact on inference. Spatial Stat. 52, 100714 (2022). 104 D. Manley, “Scale, aggregation, and the modifiable areal unit problem” in Handbook of Regional Science, M. M. Fischer, P. Nijkamp, Eds. (Springer Berlin Heidelberg, Berlin, Heidelberg, 2014), pp. 1157–1171, https://doi.org/10.1007/978-3-642-23430-9_69. 105 M. O. Kimberley, M. S. Watt, D. Harrison, Characterising prediction error as a function of scale in spatial surfaces of tree productivity. N. Z. J. For. Sci. 47, 19 (2017). 106 Project on Government Oversight, Dollars and Demographics: How Census Data Shapes Federal Funding Distribution (POGO, Washington, DC, 2023). 107 T. Krieger, D. Meierrieks, Population size and the size of government. Eur. J. Polit. Econ. 61, 101837 (2020). 108 C. Tuholske et al., Implications for tracking SDG indicator metrics with gridded population data. Sustainability 13, 7329 (2021). 109 E. Darin, Demographic figures at risk in the digital era: Resisting commodification, reclaiming the common good. Big Data Soc. 12, (2025), https://doi.org/10.1177/20539517251403948. 110 A. M. van der Bles et al., Communicating uncertainty about facts, numbers and science. R. Soc. Open Sci. 6, 181870 (2019). 111 V. Kudakwashe Paul, H. Kate, G. Matthew, P. Helen, K. Lynda, Local ownership of health policy and systems research in low-income and middle-income countries: A missing element in the uptake debate. BMJ Global Health 4, e001523 (2019). 112 B. Knittel et al., Critical barriers to sustainable capacity strengthening in global health: A systems perspective on development assistance. Gates Open Res. 6, 116 (2022). 113 B. Harrell-Bond, E. Voutira, M. Leopold, Counting the refugees: Gifts, givers, patrons and clients. J. Refug. Stud. 5, 205–225 (1992). 114 C. N. Mayemba et al., A short survey of human mobility prediction in epidemic modeling from transformers to LLMs. arXiv [Preprint] (2024). https://doi.org/10.48550/arXiv.2404.16921 (ver 1, 25 April 2024). 115 D. Leasure, M. Bondarenko, E. Darin, A. Tatem, wopr: An R package to query the WorldPop Open Population Repository, version 1.3.3 (WorldPop, University of Southampton, 2021), https://doi.org/10.5258/SOTON/WP00716, https://github.com/wpgp/wopr. 116 Institut National de la Statistique et de la Démographie du Burkina Faso, Volume I: Evaluation de la qualite des donnees, Etat, Structure et Dynamique de la population (2022). https://www.insd.bf/fr/resultats. 117 Institut de la Statistique du Mali, Resultats Globaux du RGPH5 (2023). https://www.instat-mali.org/laravel-filemanager/files/shares/rgph/rapport-resultats-globaux-rgph5_rgph.pdf. 118 PNG National Statistical Office, Population estimates (2021). https://www.nso.gov.pg/statistics/population/. 119 C. Ninrew, S. Sudan population is 12.4 million-govt estimates (2023). https://www.eyeradio.org/s-sudan-population-is-12-4-million-govt-estimates/. 120 WHO, UNICEF, Geo-Enabled Microplanning Handbook: A product of the WHO-UNICEF COVAX (WHO-UNICEF COVAX GIS Working Group, 2023). 121 UNICEF, Reach the unreached—Geospatial modelling mapping methods (2025). https://github.com/unicef-drp/reach-the-unreached?tab=readme-ov-file. 122 E. Darin, D. R. Leasure, A. J. Tatem, Statistical population modelling for census support (United Nations Population Fund (UNFPA), Leverhulme Centre for Demographic Science, University of Oxford, and WorldPop, University of Southampton, 2023), https://wpgp.github.io/bottom-up-tutorial/, https://doi.org/10.5281/zenodo.7945266. 123 J. M. Espey, A. J. Tatem, D. R. Thomson, Disappearing people: A global demographic data crisis threatens public policy. Science 388, 1277–1280 (2025). 124 A. Gutierrez, ECLAC and UNFPA approach to model populations in Latin America and the Caribbean, IAOS-ISI, Mexico City (2024). https://www.inegi.org.mx/eventos/2024/iaos-isi/doc/34.pdf.