predictors, for example the distance to urban centers and the distance to the electricity grid. Such variables are informative especially with respect to their impact on poverty and can be better predictors than binary variables indicating access based on a cutoff might be. However, they also can have difficulty predicting “pockets of poverty” sitting in otherwise wealthier areas, for example slums in urban areas, or the converse. This could exacerbate the spatially smooth predictions already introduced by the assumption of constant coefficients from the linear regression. Unfortunately, the impact this may have had on the estimation is difficult to test using cross‐validation with survey data that was designed to be widely distributed geographically. Therefore, it is impossible to test what the share of variation in welfare across EAs is dampened by the use of these spatially smooth predictors. This is an area which warrants future research given the predictive power of such variables and could be better tested using data collected more finely over large areas such as a Census.

Secondly, there is a very poor understanding of the population distribution in South Sudan and no reliable sampling frame against which to extrapolate our predictions. The implications of this are that while the model can predict into geographic pixels based on the existing data, it is difficult to aggregate by county without knowing how to weight each pixel according to the population present within it. Thus, poverty maps aggregated by area are likely to over‐ estimate poverty rates as most areas within each county are likely to have lower population density and high poverty. The solution to this problem is to define a new sampling frame for the country, then re‐calculate county‐ level predictions based on this sampling frame. This was attempted in this study by estimating an urban gradient based on multiple data sources and their relationship with urbanicity. However, some of these data are likely to be out of date for many of the same reasons that a traditional Census exercise is complicated. The rapid and enormous movement of people caused by the conflict is likely to have compounded this problem. Building newer and more up to date population sample frames should be a priority for researchers interested in South Sudan. This could be achieved either by conducting a traditional census, or by leveraging the recently available satellite imagery using and machine‐learning based methods to extract features. Such extract could be used to help define settled areas and their associated population density to create a predictive population surface (Engstrom et al., 2017a; Jean et al., 2016; Pasquale et al., 2017). Based on this, new sample frames can be built to use for future data collection work, which is badly needed in the context of South Sudan.

The model structure was voluntarily kept simple (linear combination) to ease its interpretation given that it was constructed as a proof of concept to show the potential of spatial data for imputing poverty to supplement poverty survey estimates. Furthermore, although where these techniques may have the most value, which is where there might have been a crisis or emergency or where safety is a concern, these techniques are also the most difficult to apply. Indeed, the link between poverty and such variables is much more likely to be structural than transient across much of South Sudan. Indeed, a set of issues that arise in this estimation method is the difficulty of modeling the dynamics of poverty and shocks. Many of the areas where the enumerators could not go were inaccessible because of recent conflict and it is difficult to account for this in a cross‐sectional model as such, given the potentially endogenous nature of conflict and poverty whereby some conflict events are concentrated around wealthier areas. One of the areas for future research might be to leverage the time series that area available for various types of geo‐ spatial data to try to account for some of these dynamics relating poverty rates to shocks and imbalances.

# 6. Conclusion

The HFS conducted several rounds of data collection at a time of upheaval in the short history of South Sudan. In particular, Wave 3 of the HFS consisted of a major data collection effort during what effectively became one of the deepest humanitarian crises in recent history. The HFS was conceived within the context of the crisis and was therefore designed to leverage new technologies for monitoring and implementation as well as methodological innovations in survey design. This allowed the HFS team to monitor closely the survey and facilitate the implementation while facing a multitude of challenges induced by the escalating crisis. Unfortunately, the growing intensity of the conflict eventually led to a shortened survey with deprived sample size. In the end, after almost 9 21