Please use this identifier to cite or link to this item: https://repository.iimb.ac.in/handle/2074/17784
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dc.contributor.authorSoundararajan, Vidhya
dc.contributor.authorEngstrom, Ryan
dc.contributor.authorNewhouse, David
dc.date.accessioned2021-03-26T15:16:31Z-
dc.date.available2021-03-26T15:16:31Z-
dc.date.issued2020
dc.identifier.issn1932-6203
dc.identifier.urihttps://repository.iimb.ac.in/handle/2074/17784-
dc.description.abstractCountry-level census data are typically collected once every 10 years. However, conflicts, migration, urbanization, and natural disasters can rapidly shift local population patterns. This study demonstrates the feasibility of a "bottom-up"-method to estimate local population density in the between-census years by combining household surveys with contemporaneous geo-spatial data, including village-area and satellite imagery-based indicators. We apply this technique to the case of Sri Lanka using Poisson regression models based on variables selected using the Least Absolute Shrinkage and Selection Operator (LASSO). The model is estimated in villages sampled in the 2012/13 Household Income and Expenditure Survey, and is employed to obtain out-of-sample density estimates in the non-surveyed villages. These estimates approximate the census density accurately and are more precise than other bottom-up studies using similar geo-spatial data. While most open-source population products redistribute census population "top-down" from higher to lower spatial units using areal interpolation and dasymetric mapping techniques, these products become less accurate as the census itself ages. Our method circumvents the problem of the aging census by relying instead on more up-to-date household surveys. The collective evidence suggests that our method is cost effective in tracking local population density with greater frequency in the between-census years.
dc.publisherPLOS
dc.subjectCensuses
dc.subjectGeographic mapping
dc.subjectHumans
dc.subjectPopulation density
dc.subjectSatellite imagery
dc.subjectSri Lanka
dc.titleEstimating small-area population density in Sri Lanka using surveys and Geo-spatial data
dc.typeJournal Article
dc.identifier.doi10.1371/journal.pone.0237063
dc.vol.noVol.15
dc.issue.noIss.8
dc.journal.namePLoS One
Appears in Collections:2020-2029 C
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