# **1 Introduction**

Up-to-date estimates of population density in small areas is a valuable input for policymakers (Stevens et al., 2015; Wardrop et al., 2018). They could, for example, facilitate efficient delivery of public goods and services and infrastructure projects (Guiteras et al., 2018); track net migration patterns, especially in response to civilian conflict, political upheavals, and climate tragedies; and help 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. Although household surveys can yield more frequent population estimates, they are not representative at small administrative levels. The challenge of tracking population is particularly exacerbated in low- and lower-middle- income countries where population growth rates are high and net migration patterns are rapid (Figure 1).

Satellite imagery, in combination with survey data, has the potential to fill this gap by generating more frequent estimates of population that are representative in small areas. Satellites offer several advantages as data collection instruments. They systematically and universally capture large geographic areas, produce imagery at reasonably regular intervals, and are unaffected by topography, political climate, or conflict. The technology and market for both collecting and processing satellite-based imagery are developing rapidly, and with ever more satellites being launched, high-spatial resolution imagery is increasingly available at reasonable cost.

Although existing publicly available population products incorporate satellite data, this does not imply that they are up to date. Existing products typically rely on dasymetric mapping approaches to distribute population from the census to lower administrative units, either using equal weights, or weights based on satellite indicators and advanced statistical techniques. These “top-down” methods may not provide accurate or up-to-date population estimates for two reasons. First, the accuracy of distributing population relies heavily on the input population data, that is, on the census (Wardrop et al., 2018). However, the census itself may quickly become outdated due to frequent migration patterns, and rapid and uneven population growth.<sup>1</sup> Second, these methods often rely on Night Time Lights (NTL) or other low-spatial resolution or coarse built-up area measures from satellite and other sources to distribute census population, and hence are limited at fine spatial scales (Vogel et al., 2018). This study seeks to overcome these limitations by proposing a “bottom-up” method which

> 1 Wardrop et al. (2018) provide examples of censuses that are outdated in countries such as Lebanon and Somalia due to conflicts, and postponed or cancelled in some others such as Afghanistan, Madagascar and the Republic of Congo. It is especially critical that we are able to obtain local population estimates for conflict ridden and unstable countries not only to track settlements and refugee movements, but also to provide aid and assistance.

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