samples from high resolution satellite imagery to estimate slum populations in Dar‐es‐Salaam, Tanzania. Afzal et al (2015) incorporated satellite data into poverty prediction modeling for Pakistan and Sri Lanka, and concluded that its inclusion can lead to substantial improvements in modeled estimates of poverty.

Specifically related to sampling, the literature is more limited and mainly found in the public health literature. Dreiling et al (2009) tested the use of satellite images for household selection in rural counties of South Dakota, and found, while less time consuming, the method did a poor job of identifying the inhabited dwellings. Grais et al (2007) used a random point selection methodology in their study of vaccination rates in urban Niger, and compared the results to a random walk. They do not find statistically significant differences between the methods, though the sample size was limited, but conclude that interviewers found the random point selection methods more straightforward to implement than the random walk. Lowther et al (2009) used satellite imagery to map more than 16,000 households in urban Zambia to select young children for a measles prevalence survey. They find the method easy to implement, but do not do a formal comparison with alternatives. Kondo et al (2014) use a point selection mechanism in the city of Sanitiago Atitlán similar to our proposed Qibla method, but assume the method to be equal probability. A similar method was used by Kumar (2007), in which satellite maps overlaid with remote sensing data were used to create stratification for an air pollution study in India, and then selected random points. Kolbe and Hutson (2006) use a similar method to select households in Port‐au‐Prince, Haiti. Their study incorporates the probability of selection for randomly selecting from nearby structures when the selected points do not fall on the roof of a dwelling, but these probabilities would be distorted if the household density were unevenly distributed or for households close to the boundary. Other public health studies, such as the World Health Organization Expanded Program on Immunization studies, use the “spin the pen” method to choose a starting household and then interview a tight cluster of households, though this method has been shown to be nonprobability (Bennett et al 1994, Grais et al 2007).

Outside the field of public health, Himelein et al (2014) used circles generated around random points to survey pastoralist populations in eastern Ethiopia, with the stratification developed from satellite imagery. A variation of this method was considered for the High Frequency Survey Pilot, but the methodology is likely unsuited to a dense metropolitan area, because it involves surveying all households within the selected circles. The uncertainty over the final cluster size was also an issue in Mogadishu as clusters with too few households increased costs, while clusters with too many households increased the time in the field and raised security concerns.

This paper brings together alternatives developed from this literature and applies them to a conflict environment. We take a rigorous approach using simulations and careful estimation of weights to compare the methods across a variety of potential field conditions. The results offer general guidelines for practitioners developing implementation plans for conflict settings.

# **2. Data**

To explore the challenges of the random walk and the four proposed alternatives, we simulated the use of each method in three example PSUs from Mogadishu, Somalia. We purposefully chose three census enumeration areas as the PSUs for this exercise to illustrate the variation in physical layout present in 4