population size and area within EAs is much more easily handled algorithmically. By taking into account recent data on boundaries, roads and other features the algorithmic process also creates EAs that are more practical for fieldwork and can be updated as new data become available. Finally, because each EA is already associated with an estimation of population size, these EAs can be easily turned into a nationally representative sampling frame.

One limitation of the method we have presented is that it relies on good quality data. Modeling population density is not the object of this work (Lloyd et al. 2017; Wardrop et al. 2018), however, reliable population estimates are crucial to use our method to obtain operational EAs. In addition, in Somalia, the method works well where OSM data sets and other ancillary data were available and of good quality - mostly in the highly populated areas or where volunteer OSM mapping effort has been highest. For instance, the boundaries of the first category (Figure 4a, b, c, g, h and i) are aligned with visible demarcations on the ground. This is since the used geospatial layers to automatize the EA delineation were derived from high-resolution satellite imagery. This pattern can be seen in the second category, but there is often a visible discrepancy to the actual natural boundaries (Figure 4 d and f) where natural boundary data appear not to exist. Finally, EA boundaries in the third category (Figure 4e) are devoid of boundary data and rely entirely on the quadtree algorithm to generate EAs. The lack of data or vast featureless areas is particularly problematic in rural areas because large stretches of land in Somalia are not or only sparsely populated. In addition, OSM road coverage does not exist in these areas. However, we note that the algorithm could easily be updated and re-run when new data become available. With the increasing prevalence of open-source data in development contexts (e.g. AidData.org, GRID3), these data gaps are likely to be filled before the next census.

Manual digitization of EAs may be still a preferable option in some cases, where no data on roads or natural boundaries are available, or where local context weighs significantly in terms of choosing EAs. For example, areas where security or gerrymandering may be important considerations will require more hands-on control of EAs to include or exclude specific houses or neighborhoods. In addition, manual digitization does not rely on ancillary data sets as long as high-resolution satellite imagery is available, the EAs can be manually digitized. However, manual digitization is prone to many errors including leaving gaps, unclosed EAs and greater irregularity in actual population within EAs. Importantly, intensive multi-resource involvement in manual EA delineation must be considered.

Although the actual household size per urban EA was one of the inputs of the population modeling, deviation still exists between actual population (based on HH size) and estimated population based on the modeling in manual urban EAs. This result may be explained by the fact that the modeling considered additional recent data sets besides the urban HH information to distribute the population within the grid cells (see appendixes 2 and 3). In addition, there were discrepancies between PESS 2014 HH number within urban EAs and regional population estimates. This means that, if HH size per EAs were considered to generate the total population per region, similar total population on the region could not be achieved as it was reported in the PESS 2014 particularly in Gedo. In addition, the very high population size based on the gridded population data set in the manual urban EAs (Figure 6) could be a result of their large spatial coverage. For instance, there is a case that an urban EA contains 1,500 HH (The HH were listed in the field based on PESS 2014).

Our use of a pre-existing tool that was not designed for the application provided us with valuable learning about the relevant features of EAs, as well as the challenges associated with the process and areas where the approach can be optimized. For example, AZTool was not developed to aggregate the multitude of small regions that were created by splitting the map using so many different data sets. The AZTool has a soft compactness constraint to produce EAs that are not oddly shaped (e.g. elongated or convoluted), however, the constraint is not often satisfied. This might be because the input data set 16