One of the earlier applications of the use of satellite and geo‐spatial data to predict outcomes was the use of night‐ time lights to predict GDP. Night‐time lights are well‐suited to predicting cross‐country levels of GDP (Henderson et al., 2012; Pinkovskiy and Sala‐i‐Martin, 2016). However, at the within‐country level they are much better suited to predicting population density than welfare, and the correlation of night‐time lights with local wages and local poverty rates has typically been found to be weak (Engstrom et al., 2017a; Mellander et al., 2015). Night‐time lights may therefore not be very well suited to uses akin to small‐area estimation, particularly in a place such as South Sudan where only about 3 percent of households have access to electricity (World Bank, 2018). More recent research has focused on training deep‐learning algorithms to extract a diverse range of features from high resolution satellite imagery, for example counting the number of cars on a street, distinguishing road types, recognizing materials roofs are made of, tree coverage, the contrast and number of jagged edges, etc. (Engstrom et al., 2017). This allows making poverty predictions at a much higher level of disaggregation (Jean et al., 2016; Krizhevsky et al., 2012; Sedda et al., 2015). Engstrom et al. (2017) provide a useful overview of the current state of the literature and show the predictive power of a range of indicators constructed from satellite data in estimating poverty at the village‐level.

In the case of the HFS in South Sudan, predictions from a set of linear models were used to project poverty estimates to inaccessible areas based on already extracted satellite features and geo‐spatial data, given the objective of creating reliable and transparent poverty measures. The poverty imputation follows a process that is relatively similar to small area estimation, though only the point estimates were estimated and not higher moments of the outcome distribution (see for example: Elbers Chris et al., 2003; Guadarrama et al., 2016; Haslett, 2016). Poverty as measured in the 2016 wave of the HFS is regressed on a range of geo‐spatial characteristics such as distance to urban centers, distance to the electricity grid, annual rainfall, annual temperatures, urban‐rural status, IPC phase classification, and others. The estimated model is then used to calculate expected poverty rates across regions where the household survey data are not available, but where the geo‐spatial data are available. Poverty rates are predicted at the 100m*100m level across South Sudan. The poverty estimates then need to be weighted by local population counts to eliminate potential bias caused by vast uninhabited areas. Given the lack of reliable administrative data on settlements or population counts, local populations were in turn estimated using a set of covariates derived from geo‐referenced data such as urbanicity, roads, clinics, and buildings.

# Estimating Settlements Data

The aggregation of poverty estimates to the county and state levels needs to be calibrated against suitable population estimates. Naively aggregating poverty rates across broad geographic regions would result in extremely high poverty rates given the vast uninhabited expanses isolated from the rest of the country, in which a model would likely predict high poverty rates. Indeed, South Sudan is sparsely populated relative even to most other large African countries, in 2008 South Sudan had a population density of approximately 13 persons per kilometer squared compared to the Sub‐Saharan Africa average of 35.<sup>37</sup> Because an accurate high‐resolution map of population density is not available for South Sudan, the spatial distribution of settlements was used as a proxy for population density in order to calculate weights with which to weight poverty estimates.

The methodology had to employ a novel process to generate estimates of settlements given the absence of more recent and up to date population data since the 2008 Census. This process was based on a wide variety of data sources and variables associated with population density, leveraging varied sources of data such as open source data from Open Street Maps on residential areas, roads, health facilities, schools, data from the Global Urban Footprint project, as well as data form the survey itself. The map of settled areas in South Sudan was built by processing and regrouping the data sets (Table 9 in Appendix E). The map of settled areas was created as a binary map (1=settled, 0=not settled) at 100m resolution. While drawing the map, the data sets were manually checked against Google Satellite imagery for the presence of settlements. One advantage of this system of estimation for settlements is that each component can be updated independently as new data become available or the situation within the country

> 37 World Development Indicators.

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