Evaluation of Global Gridded Population Data for Representing Resident Population Distribution in the Brazilian Amazon: The Case of the Baixo Tocantins Region
Article Sidebar
Main Article Content
Abstract
This study evaluated four Global Gridded Population datasets (GPWv4, GHS-POP, HRSL, and WorldPop) to represent the distribution of the resident population in the Brazilian Amazon, focusing on their concordance with population data and adherence to a residence address density proxy. The study area was the Baixo Tocantins region, Pará, chosen for its diversity of land uses and coverages, frequency of forest remnants, and dispersed rural population across island and mainland environments. The Global Grids for 2020 were evaluated through: i) concordance analysis with resident population data, by municipality and census tract, derived from municipal estimates for 2020 and the 2022 Demographic Census, and ii) analysis of adherence to residences estimates, using data from the Brazilian National Address Register for Statistical Purposes (CNEFE), based on a residence density proxy. Mean absolute percentage errors evaluated the Grids' estimates and official data. The highest concordance errors in the population data were observed for the GPWv4 and WorldPop grids. WorldPop and GHS-POP underestimate the occurrence of residences in island and riverbank areas. The HRSL grid showed the highest concordance with Census population data and better adherence to residences estimates compared to CNEFE. Thus, in the absence of disaggregated official population data, the HRSL grid is recommended as the preferred option for studies involving population distribution in the Brazilian Amazon.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish in this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see "The Effect of Open Access").
How to Cite
References
Amaral, S., Gavlak, A. A., Escada, M. I. S. & Monteiro, A. M. V. (2012). Using remote sensing and census tract data to improve representation of population spatial distribution: case studies in the Brazilian Amazon. Population and Environment, 34, 142–170. https://doi.org/10.1007/s11111-012-0168-2.
Archila Bustos, M. F., Hall, O., Niedomysl, T. & Ernstson, U. (2020). A pixel level evaluation of five multitemporal global gridded population datasets: a case study in Sweden, 1990–2015. Population and Environment, 42, 255-277. https://doi.org/10.1007/s11111-020-00360-8.
Bai, Z., Wang, J., Wang, M., Gao, M., & Sun, J. (2018). Accuracy assessment of multi-source gridded population distribution datasets in China. Sustainability, 10(5), 1363. https://doi.org/10.3390/su10051363
Baston, D. (2023). exactextractr version (v0.10.0) [R package]. GitHub. https://github.com/isciences/exactextract.
Bondarenko, M., Kerr, D., Sorichetta, A. & Tatem, A. J. (2020). Census/projection-disaggregated gridded population datasets, adjusted to match the corresponding UNPD 2020 estimates, for 183 countries in 2020 using Built-Settlement Growth Model (BSGM) outputs [Data set]. WorldPop: University of Southampton, UK. https://hub.worldpop.org/geodata/summary?id=49921
Breusch, T .S. & Pagan, A. R. (1979). A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica, 47(5), 1287–1294. https://doi.org/10.2307/1911963
Calka, B., & Bielecka, E. (2020). GHS-POP accuracy assessment: Poland and Portugal case study. Remote Sensing, 12(7), 1105. https://doi.org/10.3390/rs12071105
Campos, J. (2017). Estimativas populacionais a partir de dados orbitais de média resolução espacial: aplicações em municípios da Região Metropolitana de Belo Horizonte. [Tese de doutorado, Universidade Federal de Minas Gerais] Repositório digital de teses e dissertações da UFMG. https://repositorio.ufmg.br/bitstream/1843/FACE-B27P7Y/1/tese___j_rvis_campos.pdf.
Cardoso, A. C. D., Dal’Asta, A. P. & Monteiro, A. M. V. (2023). O que é o urbano na Amazônia contemporânea? Implicações para a vigilância em saúde no bioma. Cadernos de Saúde Pública, 39(9), e00129723. https://doi.org/10.1590/0102-311XPT129723
Carioli, A., Schiavina, M., Freire, S. & MacManus, K. (2023). GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset]. https://doi.org/10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE
Center for International Earth Science Information Network [CIESIN], Columbia University. (2018). Documentation for the Gridded Population of the World, Version 4 (GPWv4), Revision 11 Data Sets. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H45Q4T5F.
Chen, R., Yan, H., Liu, F., Du, W., & Yang, Y. (2020). Multiple Global Population Datasets: Differences and Spatial Distribution Characteristics. ISPRS International Journal of Geo-Information, 9(11), 637. https://doi.org/10.3390/ijgi9110637.
Dahmm, H. & Rabiee, M. (2020). Leaving no one off the map: A guide for gridded population data for sustainable development. Thematic Research Network on Data and Statistics (TReNDS) of the UN Sustainable Development Solutions Network (SDSN), in support of the POPGRID Data Collaborative. https://www.popgrid.org/sites/default/files/documents/Leaving_no_one_off_the_map.pdf
Doxsey-Whitfield, E., MacManus, K., Adamo, S. B., Pistolesi, L., Squires, J., Borkovska, O. & Baptista, S. R. (2015). Taking Advantage of the Improved Availability of Census Data: A First Look at the Gridded Population of the World, Version 4. Papers in Applied Geography, 1(3), 226-234. https://doi.org/10.1080/23754931.2015.1014272
Elvidge, C. D., Baugh, K. E., Kihn, E. A., Kroehl, H. W., Davis, E. R. & Davis, C. W. (1997). Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. International Journal of Remote Sensing, 18(6), 1373-1379. https://doi.org/10.1080/014311697218485
Facebook Connectivity Lab & Center for International Earth Science Information Network [CIESIN] - Columbia University. (2018). High Resolution Settlement Layer (HRSL) - Source imagery for HRSL © 2016 DigitalGlobe - Brazil Population Density Maps and Demographic Estimates (2015-2020) [Data set]. https://data.humdata.org/dataset/brazil-high-resolution-population-density-maps-demographic-estimates.
Fearnside, P. M (2006). Desmatamento na Amazônia: dinâmica, impactos e controle. Acta Amazonica, Manaus, 36(3), 395-400. https://doi.org/10.1590/S0044-59672006000300018
Gonçalves, G. C., Oliveira, L. M. de, Dal’Asta, A. P. & Amaral, S. (2021). Geoinformação Para a Visibilidade das Áreas Urbanas de Cidades Amazônicas. Revista Geoaraguaia, 11(Especial), 149-165. https://periodicoscientificos.ufmt.br/ojs/index.php/geo/article/view/12742.
Hallisey, E., Tai, E., Berens, A., Wilt, G., Peipins, L., Lewis, B., Graham, S., Flanagan, B. & Lunsford, N. (2017). Transforming Geographic Scale: A Comparison of Combined Population and Areal Weighting to Other Interpolation Methods. International Journal of Health Geographics, 16(29). https://doi.org/10.1186/s12942-017-0102-z
Harvey, J. T. (2002). Estimating census district populations from satellite imagery: Some approaches and limitations. International Journal of Remote Sensing, 23(10), 2071-2095. https://doi.org/10.1080/01431160110075901
Instituto Brasileiro de Geografia e Estatística [IBGE]. (2010). Censo Demográfico 2010 [Dataset]. Rio de Janeiro: IBGE. https://censo2010.ibge.gov.br.
Instituto Brasileiro de Geografia e Estatística [IBGE]. (2016). Grade Estatística IBGE 2010. Rio de Janeiro: IBGE. https://biblioteca.ibge.gov.br/visualizacao/livros/liv102043.pdf
Instituto Brasileiro de Geografia e Estatística [IBGE]. (2020). Estimativas de população residente por município, data de referência em 1º de Julho de 2020. Diário Oficial da União em 27 de agosto de 2020. https://www.ibge.gov.br/estatisticas/sociais/populacao/9103-estimativas-de-populacao.html?edicao=28674.
Instituto Brasileiro de Geografia e Estatística [IBGE]. (2022a). Censo Demográfico 2022 [Dataset]. Rio de Janeiro: IBGE. https://censo2022.ibge.gov.br.
Instituto Brasileiro de Geografia e Estatística [IBGE]. (2022b). Malha de setores censitários do Pará - Edição 2022 [Dataset]. Rio de Janeiro: IBGE. https://www.ibge.gov.br/geociencias/downloads-geociencias.html?caminho=organizacao_do_territorio/malhas_territoriais/malhas_de_setores_censitarios__divisoes_intramunicipais/censo_2022_preliminar/setores/shp/UF.
Instituto Brasileiro de Geografia e Estatística [IBGE]. (2022c). Malha territorial municipal brasileira - Edição 2022 [Dataset]. Rio de Janeiro: IBGE. https://www.ibge.gov.br/geociencias/organizacao-do-territorio/malhas-territoriais/15774-malhas.html.
Instituto Brasileiro de Geografia e Estatística [IBGE]. (2023). Cadastro Nacional de Endereços para Fins Estatísticos (CNEFE) do Censo 2022. Rio de Janeiro: IBGE. https://www.ibge.gov.br/estatisticas/sociais/populacao/38734-cadastro-nacional-de-enderecos-para-fins-estatisticos.html.
Instituto Brasileiro de Geografia e Estatística [IBGE]. (2024a). Censo Demográfico 2022 - Coordenadas geográficas dos endereços no Censo Demográfico 2022 : nota metodológica n. 01. Rio de Janeiro: IBGE. https://biblioteca.ibge.gov.br/index.php/biblioteca-catalogo?view=detalhes&id=2102063
Instituto Brasileiro de Geografia e Estatística [IBGE]. (2024b). Censo Demográfico 2022 - Cadastro Nacional de Endereços para Fins Estatísticos: Nota metodológica n. 04. Rio de Janeiro: IBGE. https://biblioteca.ibge.gov.br/index.php/biblioteca-catalogo?view=detalhes&id=2102091
Instituto Nacional de Pesquisas Espaciais [INPE]. (2023). Programa de Monitoramento da Amazônia e Demais Biomas [PRODES]: Desmatamento anual [Dataset]. https://terrabrasilis.dpi.inpe.br/downloads/
Instituto Nacional de Pesquisas Espaciais [INPE] & Empresa Brasileira de Pesquisa Agropecuária [EMBRAPA]. (2020). Uso e cobertura da terra do Bioma Amazônia - TerraClass [Data set]. http://terrabrasilis.dpi.inpe.br/downloads/
Kuffer, M., Owusu, M., Oliveira, L., Sliuzas, R. & van Rijn, F. (2022). The missing millions in maps: Exploring causes of uncertainties in global gridded population datasets. ISPRS International Journal of Geo-Information, 11(7), 403. https://doi.org/10.3390/ijgi11070403
Lei, Z., Xie, Y., Cheng, P. & Yang, H. (2023). From auxiliary data to research prospects, a review of gridded population mapping. Transactions in GIS, 27(1), 3–39. https://doi.org/10.1111/tgis.13020
Leyk, S., Uhl, J. H., Balk, D. & Jones, B. (2018). Assessing the accuracy of multi-temporal built-up land layers across rural-urban trajectories in the United States. Remote Sensing of Environment, 204, 898-917. https://doi.org/10.1016/j.rse.2017.08.035
Leyk, S., Gaughan, A. E., Adamo, S. B., de Sherbinin, A., Balk, D., Freire, S., Rose, A., Stevens, F. R., Blankespoor, B., Frye, C., Comenetz, J., Sorichetta, A., MacManus, K., Pistolesi, L., Levy, M., Tatem, A. J. & Pesaresi, M. (2019). The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use. Earth System Science Data, 11(3), 1385–1409. https://doi.org/10.5194/essd-11-1385-2019
Lloyd, C. T., Sorichetta, A. & Tatem, A. J. (2017). High resolution global gridded data for use in population studies. Scientific Data, 4(1), 170001. https://doi.org/10.1038/sdata.2017.1
Lu, D. & Weng, Q. (2006). Use of impervious surfaces in urban land-use classification. Remote Sensing of Environment, 102(1-2), 146-156. https://doi.org/10.1016/j.rse.2006.02.010
Nagle, N. N., Buttenfield, B. P., Leyk, S. & Spielman, S. (2013). Dasymetric Modeling and Uncertainty. Annals of the American Association of Geographers, 104(1), 80-95. https://doi.org/10.1080/00045608.2013.843439
Nobre, C. A., Obregón, G. O., Marengo, J. A., Fu, R. & Poveda, G. (2009). Characteristics of Amazonian Climate: Main Features. In: Keller, M., Bustamante, M., Gash, J. & Silva Dias, P. (Ed.) Amazonia and Global Change. American Geophysical Union. https://doi.org/10.1029/2009GM000903
Openshaw, S. (1984). The modifiable areal unit problem. Norwich: Geo Books.
Renner, K., Schneiderbauer, S., Pruß, F., Kofler, C., Martin, D. & Cockings, S. (2018). Spatio-temporal population modelling as improved exposure information for risk assessments tested in the Autonomous Province of Bolzano. International Journal of Disaster Risk Reduction, 27, 470–479. https://doi.org/10.1016/j.ijdrr.2017.11.011
Schiavina, M., Melchiorri, M., Pesaresi, M., Politis, P., Carneiro Freire, S. M., Maffenini, L., Florio, P., Ehrlich, D., Goch, K., Carioli, A., Uhl, J., Tommasi, P. & Kemper, T. (2023). GHSL data package 2023 (JRC133256). Publications Office of the European Union. https://doi.org/10.2760/098587
Silva, D. (2023). VaLin-Pop: uma nova grade populacional para a região metropolitana do Vale do Paraíba e Litoral Norte, SP. [Dissertação de Mestrado, Instituto Nacional de Pesquisas Espaciais] Repositório digital de teses e dissertações do INPE. http://mtc-m21d.sid.inpe.br/col/sid.inpe.br/mtc-m21d/2023/10.19.20.37/doc/thisInformationItemHomePage.html
Smith, S. K. & Morrison, P. A. (2005). Small-Area and Business Demography. In: Poston, D. L. & Micklin, M. (Orgs.) Handbook of Population. Handbooks of Sociology and Social Research. Springer, Boston, MA. https://doi.org/10.1007/0-387-23106-4_26
Souza, A. R., Escada, M. I. S., Marujo, R. de F. B. & Monteiro, A. M. V. (2019). Cartografia do Invisível: Revelando a Agricultura de Pequena Escala com Imagens Rapideye na Região do Baixo Tocantins, PA. Revista Do Departamento De Geografia, 38, 137-153. https://doi.org/10.11606/rdg.v38i1.151603
Souza, A. R., Adorno, B. V., Gonçalves, G. C., Bragion, G. da R., Oliveira, K. D., Escada, M. I. S., Reis, M. S., Sant’Anna, S. J. S. & Amaral, S. (2021). Paisagens e uso da terra em núcleos populacionais e estabelecimentos rurais da região do Baixo Tocantins - Pará. São José dos Campos-SP: INPE. 81 p. Relatório Técnico de Atividade de Campo de 2018 e 2019 dos Cursos de Pós-Graduação em Sensoriamento Remoto e em Ciências do Sistema Terrestre do INPE, em parceria com a Universidade Federal do Pará. http://mtc-m21d.sid.inpe.br/col/sid.inpe.br/mtc-m21d/2021/06.18.16.50/doc/thisInformationItemHomePage.html.
Souza, A. R., Amaral, S., Dal’Asta, A. P., Escada, M. I. S. & Monteiro, A. M. V. (2023) Modelos dasimétricos para desagregar dados do Censo Agropecuário. In: Anais do XX Simpósio Brasileiro de Sensoriamento Remoto. São José dos Campos : INPE. http://marte2.sid.inpe.br/col/sid.inpe.br/marte2/2023/05.16.12.12/doc/156100.pdf
Souza, A. R. (2024). Os sistemas do açaí na paisagem amazônica: elementos para análise da economia do açaí da região do Baixo Tocantins-PA. [Tese de Doutorado, Instituto Nacional de Pesquisas Espaciais]. Repositório digital de teses e dissertações do INPE. https://mtc-m21d.sid.inpe.br/col/sid.inpe.br/mtc-m21d/2024/09.03.22.32/doc/publicacao.pdf.
Tiecke, T. G., Liu, X., Zhang, A., Gros, A., Li, N., Yetman, G., Kilic, T., Murray, S., Blankespoor, B., Prydz, E. B. & Dang, H. H. (2017). Mapping the world population one building at a time. arXiv. https://doi.org/10.48550/arXiv.1712.05839.
United Nations [UN] (2015). UN World Population Prospects: Revision 11 [Data Booklet]. https://www.un.org/en/development/desa/publications/world-population-prospects-2015-revision.html.
Weng, Q. & Hu, X. (2008). Medium Spatial Resolution Satellite Imagery for Estimating and Mapping Urban Impervious Surfaces Using LSMA and ANN. IEEE Transactions on Geoscience and Remote Sensing, 46(8), 2397-2406. https://doi.org/10.1109/TGRS.2008.917601
Wickham, J. D., Stehman, S. V., Gass, L., Dewitz, J., Fry, J. A., & Wade, T. G. (2013). Accuracy assessment of NLCD 2006 land cover and impervious surface. Remote Sensing of Environment, 130, 294–304. https://doi.org/10.1016/j.rse.2012.12.001
WorldPop. (n.d.). WorldPop methods. University of Southampton. Retrieved [June 2024], from https://www.worldpop.org/methods/
Xu, Y., Ho, H. C., Knudby, A. & He, M. (2021). Comparative assessment of gridded population data sets for complex topography: A study of Southwest China. Population and Environment, 42, 360–378. https://doi.org/10.1007/s11111-020-00366-2
Yin, X., Li, P., Feng, Z., Yang, Y., You, Z. & Xiao, C. (2021). Which Gridded Population Data Product Is Better? Evidences from Mainland Southeast Asia (MSEA). ISPRS International Journal of Geo-Information, 10(10), 681. https://doi.org/10.3390/ijgi10100681
Zha, Y., Gao, J. & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583-594. https://doi.org/10.1080/01431160304987
Zhao, T. & Wang, W. (2023). Coordination Dynamics between Population Change and Built-Up Land Expansion in Mainland China during 2000–2020. Sustainability, 15(22), 16059. https://doi.org/10.3390/su152216059
Zhou, Y., Ma, M., Shi, K. & Peng, Z. (2020). Estimating and Interpreting Fine-Scale Gridded Population Using Random Forest Regression and Multisource Data. ISPRS International Journal of Geo-Information, 9(6), 369. https://doi.org/10.3390/ijgi9060369