Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia

References

• Boo, Gianluca, Edith Darin, Douglas R. Leasure, Claire A. Dooley, Heather R Chamberlain, Attila N. Lázár, Kevin Tschirhart, et al. 2022. High-resolution population estimation using household survey data and building footprints, Nature Communications 13(1): 1–10. https://doi.org/10.1038/s41467-022-29094-x
   PubMed Web of Science ®Google Scholar
• Breiman, L. 1994. The 1991 census adjustment: Undercount or bad data?, Statistical Science 9(4): 458–475. https://doi.org/10.1214/ss/1177010259
   Web of Science ®Google Scholar
• Brooks, Stephen P. and Andrew Gelman. 1998. General methods for monitoring convergence of iterative simulations, Journal of Computational and Graphical Statistics 7(4): 434–455. https://doi.org/10.1080/10618600.1998.10474787
   Web of Science ®Google Scholar
• Car-Hill, R. 2013. Missing millions and measuring development progress, World Development 46: 30–44. https://doi.org/10.1016/j.worlddev.2012.12.017
   Web of Science ®Google Scholar
• [DANE] National Administrative Department of Statistics of Colombia. 2014. Dinámica y Ajustes Metodológicos para los Talleres de Cartografía Social y Estructuración de Rutas. [Dynamics and Methodological Adjustments for the Social Cartography Workshops and Routes Structuring]. Bogotá, Colombia: Census and Demographics Directorate, National Administrative Department of Statistics.
   Google Scholar
• [DANE] National Administrative Department of Statistics of Colombia. 2020. Necesidades Básicas Insatisfechas [Unsatisfied Basic Needs]. Bogotá, Colombia: National Administrative Department of Statistics (DANE). Available: https://www.dane.gov.co/index.php/estadisticas-por-tema/pobreza-y-condiciones-de-vida/necesidades-basicas-insatisfechas-nbi
   Google Scholar
• [DANE] National Administrative Department of Statistics of Colombia. 2021a. Censo Nacional de Población y Vivienda – CNPV 2018: Documento Metodológico [National Population and Housing Census – CNPV 2018: Methodological Document]. Bogotá, Colombia: National Administrative Department of Statistics (DANE).
   Google Scholar
• [DANE] National Administrative Department of Statistics of Colombia. 2021b. Lecciones Aprendidas, Censo Nacional de Población y Vivienda – CNPV 2018 en el marco de la NTC PE 1000 [Learned Lessons, National Population and Housing Census – CNPV 2018 within the framework of the NTC PE 1000]. Bogotá, Colombia: National Administrative Department of Statistics (DANE).
   Google Scholar
• [DANE] National Administrative Department of Statistics of Colombia. 2022a. Estimación de la omisión censal a nivel nacional y subnacional a partir de métodos demográficos, econométricos y geoestadísticos, Metodologías Demográficas Aplicada [Estimation of Census Omission at National and Subnational Levels Based on Demographic, Econometric and Geostatistical Methods], Metodologías Demográficas Aplicada 2.
   Google Scholar
• [DANE] National Administrative Department of Statistics of Colombia. 2022b. Descarga del Marco Geoestadistico Nacional (MGN). Available: https://geoportal.dane.gov.co/servicios/descarga-y-metadatos/descarga-mgn-marco-geoestadistico-nacional/ (accessed: 21 June 2022).
   Google Scholar
• Denwood, Matthew J. 2016. Runjags: An R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS, Journal of Statistical Software 71(9): 1–25. https://doi.org/10.18637/jss.v071.i09
   Web of Science ®Google Scholar
• Dias, Jr., Claudio S. and Ana P. Verona. 2018. Os indígenas nos Censos Demográficos brasileiros pré-1991 [The indigenous people in the Brazilian Demographic Censuses before 1991], Revista Brasileira de Estudos de População 35(3): 1–9. https://doi.org/10.20947/S0102-3098a0058
   Google Scholar
• Dooley, Claire A., Heather R. Chamberlain, Douglas R. Leasure, G. M. Membele, Attila N. Lazar, and Andrew J. Tatem. 2021. Description of Methods for the Zambia Modelled Population Estimates from Multiple Routinely Collected and Geolocated Survey Data, Version 1.0. World Pop, University of Southampton. https://doi.org/10.5258/SOTON/WP00707
   Google Scholar
• Eddelbuettel, Dirk. 2021. JAGS: Just Another Gibbs Sampler for Bayesian MCMC, Version 4.3.0-3build1. Available: https://launchpad.net/ubuntu/(source/jags/4.3.0-3build1
   Google Scholar
• Elvidge, C. D., K. Baugh, M. Zhizhin, F. C. Hsu, and T. Ghosh. 2017. VIIRS night-time lights, International Journal of Remote Sensing 38(21): 5860–5879. https://doi.org/10.1080/01431161.2017.1342050
   Web of Science ®Google Scholar
• Engstrom, R., D. L. Newhouse, and V. Soundararajan. 2020. Estimating small-area population density in Sri Lanka using surveys and geo-spatial data, PLoS ONE 15(8): e0237063. https://doi.org/10.1371/journal.pone.0237063
   PubMed Web of Science ®Google Scholar
• Ericksen, Eugene P. and Joseph B. Kadane. 1986. Using administrative lists to estimate census omissions, Journal of Official Statistics 2(4): 397–414.
   PubMedGoogle Scholar
• Esch, Thomas, Julian Zeidler, Daniela Palacios-Lopez, Mattia Marconcini, Achim Roth, Milena Mönks, Benjamin Leutner, et al. 2020. Towards a large-scale 3D modeling of the built environment—Joint analysis of TanDEM-X, Sentinel-2 and Open Street Map data, Remote Sensing 12(15): 2391. https://doi.org/10.3390/rs12152391
   Web of Science ®Google Scholar
• Esch, Thomas, Elisabeth Brzoska, Stefan Dech, Benjamin Leutner, Daniela Palacios-Lopez, Annekatrin Metz-Marconcini, Mattia Marconcini, et al. 2022. World settlement footprint 3D - A first three-dimensional survey of the global building stock, Remote Sensing of Environment 270: 112877. https://doi.org/10.1016/j.rse.2021.112877
   Web of Science ®Google Scholar
• ESRI. 2017. ArcGIS Desktop: Release 10.5.1. Redlands, CA: Environmental Systems Research Institute.
   Google Scholar
• Farr, T. G., P. A. Rosen, E. Caro, R. Crippen, R. Duren, S. Hensley, M. Kobrick, et al. 2007. The shuttle radar topography mission, Reviews of Geophysics 45(2): RG2004. https://doi.org/10.1029/2005RG000183
   Web of Science ®Google Scholar
• Fein, D. J. 1990. Racial and ethnic differences in U.S. census omission rates, Demography 27(2): 285–302. https://doi.org/10.2307/2061454
   PubMed Web of Science ®Google Scholar
• Feres, Juan C. and X. Mancero. 2001. El método de las necesidades básicas insatisfechas (NBI) y sus aplicaciones en América Latina. [The Unsatisfied Basic Needs (UBN) Method and Its Applications in Latin America]. Santiago, Chile: United Nations. ISBN: 92-1-321791-9.
   Google Scholar
• Gelman, A. and D. B. Rubin. 1992. Inference from iterative simulation using multiple sequences, Statistical Science 7: 457–511. https://doi.org/10.1214/ss/1177011136
   Google Scholar
• Gelman, A. 2006. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper), Bayesian Analysis 1(3): 515–534. https://doi.org/10.1214/06-BA117A
   Web of Science ®Google Scholar
• Geofabrik GmbH. 2018. OpenStreetMap Data Extracts. https://download.geofabrik.de
   Google Scholar
• Global Administrative Areas (GADM). 2019. University of California, Berkeley. Available: http://www.gadm.org (accessed: 10 October 2019).
   Google Scholar
• Google. 2022. Google Open Buildings. https://sites.research.google/open-buildings/
   Google Scholar
• Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. 2017. Google Earth engine: Planetary-scale geospatial analysis for everyone, Remote Sensing of Environment 202: 18–27. https://doi.org/10.1016/j.rse.2017.06.031
   Web of Science ®Google Scholar
• Green, J. J., H. Hanna, L. Woo, R. Haggard, and A. Buffington. 2020. Using community engagement approaches to bolster 2020 census participation, Choices 35(1): 1–6. https://www.jstor.org/stable/27098553
   Google Scholar
• Hogan, H. and K. Wolter. 1988. Measuring accuracy in a post-enumeration survey, Survey Methodology 14(1): 99–116.
   PubMedGoogle Scholar
• [INDEC] Instituto Nacional de Estadística y Censos (Argentina). 1984. La Pobreza en la Argentina: Indicadores de necesidades básicas insatisfechas a partir de los datos del Censo nacional de población y vivienda, 1980. [Poverty in Argentina: Indicators of Unsatisfied Basic Needs Based on Data from the National Population and Housing Census, 1980]. Buenos Aires, Argentina: Instituto Nacional de Estadística y Censos.
   Google Scholar
• Jardim, Maria de Lourdes T. 2001. Uso de las Variables Sintomáticas para Estimar la Distribución Espacial de Población: Aplicación a los Municipios de Rio Grande do Sul, Brasil. [Use of Symptomatic Variables to Estimate the Spatial Distribution of Population: Application to the Municipalities of Rio Grande do Sul, Brazil]. Notas de Población 71(2). Available: http://hdl.handle.net/11362/12695
   Google Scholar
• Kashyap, R., R.G. Rinderknecht, A. Akbaritabar, D. Alburez-Gutierrez, S. Gil-Clavel, A. Grow, J. Kim, et al. 2022. Digital and Computational Demography. SocArXiv. https://doi.org/10.31235/osf.io/7bvpt
   Google Scholar
• Leasure, Douglas R., W. Christopher Jochem, Eric M. Weber, Vince Seaman, and Andrew J. Tatem. 2020. National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty, Proceedings of the National Academy of Sciences 117(39): 24173–24179. https://doi.org/10.1073/pnas.1913050117
   PubMed Web of Science ®Google Scholar
• Marcil, L., K. Afsana, and H. B. Perry. 2016. First steps in initiating an effective maternal, neonatal, and child health program in urban slums: The BRAC Manoshi Project’s experience with community engagement, social mapping, and census taking in Bangladesh, Journal of Urban Health 93: 6–18. https://doi.org/10.1007/s11524-016-0026-0
   PubMed Web of Science ®Google Scholar
• Microsoft. 2022. GlobalMLBuildingFootprints. https://github.com/microsoft/GlobalMLBuildingFootprints
   Google Scholar
• Mossoux, S., M. Kervyn, H. Soulé, and F. Canters. 2018. Mapping population distribution from high resolution remotely sensed imagery in a data poor setting, Remote Sensing 10(9): 1409. https://doi.org/10.3390/rs10091409
   Web of Science ®Google Scholar
• Myrskyla, P. 1999. New statistics made possible by the use of registers, Statistical Journal 16(2-3): 165–180. https://doi.org/10.3233/SJU-1999-162-306
   Google Scholar
• Open Street Map. 2022. http://www.openstreetmap.org
   Google Scholar
• Palacios-Lopez, D., F. Bachofer, T. Esch, M. Marconcini, K. MacManus, A. Sorichetta, J. Zeidler, et al. 2021. High-resolution gridded population datasets: Exploring the capabilities of the world settlement footprint 2019 imperviousness layer for the African continent, Remote Sensing 13(6): 1142. https://doi.org/10.3390/rs13061142
   Web of Science ®Google Scholar
• Paulston, R. G. and M. Liebman. 1994. An invitation to postmodern social cartography, Comparative Education Review 38(2): 215–232. https://doi.org/10.1086/447242
   Web of Science ®Google Scholar
• Plummer, Martin. 2003. Jags: A program for analysis of Bayesian graphical models using Gibbs sampling, in K. Hornik, F. Leisch and A. Zeileis (eds), Proceedings of the 3rd International Workshop on Distributional Statistical Computing. Vienna, Austria: Technische Universität Wien, pp. 1–10.
   Google Scholar
• Plummer, Martyn, Nicky Best, Kate Cowles, and Karen Vines. 2006. CODA: Convergence diagnosis and output analysis for MCMC, R News 6(1): 7–11. Available: https://journal.r-project.org/archive/
   Google Scholar
• R Core Team. 2020. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available: https://www.R-project.org/.
   Google Scholar
• Sanchez-Cespedes, L. M., D. R. Leasure, N. Tejedor-Garavito, G. H. A. Cruz, G. A. G. Velez, A. E. Mendoza, Y. A. M. Salazar, et al. 2022. Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia, SocArXiv. Available: https://doi.org/10.17605/OSF.IO/DW4VR
   Google Scholar
• [UNFPA] United Nations Population Fund. 2020. The Value of Modelled Population Estimates for Census Planning and Preparation, Technical Guidance Note. New York: United Nations Population Fund. Available: https://www.unfpa.org/resources/value-modelled-population-estimates-census-planning-and-preparation
   Google Scholar
• United Nations. 2010. Post Enumeration Surveys, Operational Guidelines. New York: United Nations Secretariat, Department of Economic and Social Affairs, Statistics Division. Available: https://unstats.un.org/unsd/demographic/standmeth/handbooks/Manual_PESen.pdf
   Google Scholar
• Valente, P. 2010. Comment la population est-elle recensée dans les pays européens en 2010? [Census taking in Europe: How are populations counted in 2010?], Population & Sociétés 467(5): 1–4. https://doi.org/10.3917/popsoc.467.0001
   Google Scholar
• Wardrop, N. A., W. C. Jochem, T. J. Bird, H. R. Chamberlain, D. Clarke, D. Kerr, L. Bengtsson, et al. 2018. Spatially disaggregated population estimates in the absence of national population and housing census data, Proceedings of the National Academy of Sciences 115(14): 3529–3537. https://doi.org/10.1073/pnas.1715305115
   PubMed Web of Science ®Google Scholar
• Weber, E. M., V. Y. Seaman, R. N. Stewart, T. J. Bird, A. J. Tatem, J. J. McKee, B. L. Bhaduri, et al. 2018. Census-independent population mapping in northern Nigeria, Remote Sensing of Environment 204: 786–798. https://doi.org/10.1016/j.rse.2017.09.024
   PubMed Web of Science ®Google Scholar