Abstract
Country-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.
Highlights
Up-to-date estimates of population density in small areas are valuable inputs for policymakers [1, 2]
Poisson regression results using the census data based on the variables selected from Least Absolute Shrinkage and Selection Operator (LASSO) regularization indicate that geo-spatial indicators have strong predictive power in predicting village level population density (Table 4)
Many existing estimates of estimating local population use a top-down dasymetric mapping approach to distribute census data based on a set of covariates derived from satellite imagery and other ancillary data
Summary
Up-to-date estimates of population density in small areas are valuable inputs for policymakers [1, 2]. They could, for example, facilitate efficient delivery of public goods and services and infrastructure projects [3]; track net migration patterns, especially in response to civilian conflicts, political upheavals, and climate tragedies; and help us better understand the impact of geographically-targeted economic policy interventions, such as Special Economic Zones. Traditional population data sources do not meet these requirements, as censuses provide local population measurements infrequently, typically decennially. Household surveys can yield more frequent population estimates, they do not cover the entire country, and are not representative at small administrative levels, in the developing world.
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