The Population Seen from Space: When Satellite Images Come to the Rescue of the Census.
The size of the population, the denominator of many statistical indicators, is crucial for public policy. National statistical offices organize the collection of this information, most often through a census. But what happens when parts of a country are not accessible to census enumerators? Today, s...
| Published in: | Population (1634-2941) Vol. 77; no. 3; pp. 437 - 489 |
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| Main Authors: | , , , , |
| Format: | Article |
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Institut National d'Etudes Demographiques
2022
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=160720275&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 160720275 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 16342941 46MR jtl: Population (1634-2941) issn: 16342941 maglogo: N pubinfo: dt: 2022 vid: 77 iid: 3 pid: 10666 pub: Institut National d'Etudes Demographiques artinfo: ui: 160720275 10.3917/popu.2203.0467 ppf: 437 ppct: 52 formats: fmt: @attributes: type: P size: 27.7MB tig: atl: The Population Seen from Space: When Satellite Images Come to the Rescue of the Census. aug: au: Darin, Edith Kuépié, Mathias Bassinga, Hervé Boo, Gianluca Tatem, Andrew J. affil: WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom; Leverhulme Centre for Demographic Science, Department of Sociology, University of Oxford. United Nations Population Fund, Dakar, Senegal. Institut national de la statistique et de la démographie, Ouagadougou, Burkina Faso. su: Government policy Remote-sensing images Geographic information systems Bayesian analysis Statistical learning sug: subj: Government policy Remote-sensing images Geographic information systems Bayesian analysis Statistical learning keyword: Bayesian statistics building footprint Burkina Faso census geospatial data gridded population hierarchical model remote sensing données géospatiales empreinte du bâti modèle hiérarchique population carroyée recensement statistiques bayésiennes télédétection Bayesian statistics building footprint Burkina Faso census geospatial data gridded population hierarchical model remote sensing données géospatiales empreinte du bâti modèle hiérarchique population carroyée recensement statistiques bayésiennes télédétection ab: The size of the population, the denominator of many statistical indicators, is crucial for public policy. National statistical offices organize the collection of this information, most often through a census. But what happens when parts of a country are not accessible to census enumerators? Today, spatial data extracted from satellite imagery offer high-resolution geographical information with complete coverage. When combined with a partial population count, they offer an unprecedented opportunity to estimate the size of the population in inaccessible areas. The spatial precision of these data also makes possible the production of a high-resolution gridded population estimate, an innovative data format at the intersection of geography and demography. Based on the case of Burkina Faso, this article analyses how, by dividing a country into 100 m by 100 m cells, a Bayesian hierarchical model can be used to estimate the population of areas with security challenges which could not be enumerated during the 2019 census. This gridding allows the resulting counts to be disaggregated using a statistical learning model, yielding unparalleled spatial precision in population estimates. pubtype: Academic Journal doctype: Article src: R language: Multiple languages refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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