Two aspects of these results are particularly noteworthy. First, the survey used to calibrate the model covers only 17% of the villages in Sri Lanka. The fact that the combination of satellite imagery with this type of small survey predicts population density as accurately as the estimates that use an entire census demonstrates the cost-effectiveness of this approach.<sup>5</sup> In fact, simulations indicate that combining survey data with satellite data generates sub-district estimates of population density that are as precise as a survey that samples 80 percent of the villages nationwide. Second, although our estimates are not as accurate as the most accurate top-down estimates, the model gives comparable or better performance than most others. However, because surveys are collected much more frequently than censuses, bottom-up methods that combine satellite imagery with surveys have the crucial advantage of remaining up to date even as the census ages.<sup>6</sup> This study fits into a rapidly growing literature on using satellite imagery to predict population counts and density in local areas. Wardrop et al. (2018) provide an overview of these studies. Top-down approaches to distribute population remain popular in the literature. For example, Stevens et al. (2015) use a dasymetric approach to redistribute population counts from the census using a Random Forest model-based weighting scheme in Cambodia, Vietnam, and Kenya. Using a combination of regression and tree-based methods, Anderson et al. (2014) predict population density for districts in Peru where no direct samples were available, using two coarse satellite-based covariates: Normalized Difference Vegetation Index (NDVI) and the daytime Land Surface Temperature (LST).<sup>7</sup> Our contribution to this literature is threefold. First, relatively few studies use “bottomup” techniques (Wardrop et al., 2018). Early studies using bottom-up style methods either use simple area measures and/or coarse satellite imagery, or mostly focus on developed countries where there are fewer data limitations (Sutton et al., 2001; Biljecki et al., 2016; Li and Weng, 2005).<sup>8</sup> Second, we compare prediction accuracies between imageries of various resolutions to ascertain if, and by how much, higher resolution imageries are better at predicting density compared to lower resolution imageries. Prior studies have not examined the trade-offs across

> 5 It is important here to clarify that even though we do not directly rely on the census, the census remains essential to provide the sampling frame for subsequent and periodic surveys. Our method, hence, does not reduce the importance of the census.

> 6 For instance, as we will show in section 4, WorldPop 2010 that utilizes the 2001 Sri Lankan census (a gap of nine years) for redistribution, performs much worse than WorldPop 2015 that uses the 2012 census (a gap of three years).

> 7 We also speak to a parallel literature that uses other sources of big data such as mobile phones to map population distribution (Deville et al., 2014) or estimate population maps based on age structure (Alegana et al., 2015), and to an emerging literature that uses satellite data and machine learning techniques to predict human welfare and demographic variables, and urban market boundaries (McBride and Nichols, 2016; Athey, 2017; Engstrom et al., 2017; Jean et al., 2016; Vogel et al., 2018).

> 8 Hillson et al. (2014) use bottom-up techniques but answer a different but related question of what survey size would support a particular confidence level in the estimating population using rooftop area in Bo, Sierra Leone.

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