<u>(www.worldpop.org, 2018), Global Rural-Urban Mapping Project, Version 1 (GRUMP) (CIESIN et al.</u> 2011), Gridded Population of the World Version 4 (GPWv4) (CIESIN, 2017), Gridded Population of the World, United Nation (GPW UNEP, 2006) and Global Human Settlement Population Grid (GHSPOP) (JRC and CIESIN 2015). However, none of these data sets on its own was sufficient for our purposes, as they were created without the use of the PESS 2014 data, or the final total population was not adjusted to match the PESS regional total. In addition, we had access to more recent data sets (highresolution DigitalGlobe population estimates), which we wanted to use to ensure that our EA delineation of Somalia is based on the most up to date population estimates. Therefore, we produced a novel 100m x 100m population density map to calibrate our EA delineation. We give below an only succinct overview of the method employed as it is not the object of the present paper, and it is not relevant to the description and results of our novel automated process for EA delineation, which can accept as input any gridded data set of sufficiently high resolution. Appendices 2 and 3 list the data sources that we used for urban and rural areas respectively, and the transformations we applied in order to obtain a 100m x 100m raster for each. Data sources include information on building density, household density and population density. We used the World Bank survey (UNFPA 2014) to estimate a median number of people per building and per household to approximate population density from data on building and household densities. In places lacking data but identified as settled, we modeled population density based on the distribution of population estimates in similar settlements. We then set population density to zero in locations known to be not settled, and to a low value in locations that could be settled but for which we have no data (around known settlements). Finally, we rescaled the population density map thus obtained using the PESS 2014 regional totals (UNFPA 2014).

# **2.5 Split and merge algorithm for the creation of Enumeration Areas (EAs)**

# **2.5.1 Splitting process**

The aim of the splitting process is to partition the country into regions that are as small as possible so that the subsequent merging process has enough flexibility to combine them into optimal EAs. We used three steps to do this.

_Step 1 - Splitting based on geo-referenced features to create regions with tangible boundaries_ The country was split using road data, rural settlement boundaries, waterway and river data and administrative boundaries from OpenStreetMap (OSM), using the feature to polygons tool in ArcGIS. These data sets where either lines or polygons, whose geometry will be used to create area features, were the input features to the tool. From the merging of these features, each small “closed” area became a feature in the output feature class (here called ‘Primary Units’ (PU). This first step results in a set of fully contiguous units that are much smaller than the target EA size, with no gaps or islands and with all regions delineated by georeferenced features. If the road data are complete, then the process ensures that no building will be cut.

For the areas that do not meet the criteria, Thiessen polygons based on settlement locations were created. However, for sparsely populated areas or vast featureless areas, with only a few geo-referenced settlements or residential areas, areas created from this approach were still larger than 9km<sup>2</sup> .

# _Step 2 - Estimate population and area_

With the PU feature set defined, we were able to then compute the population size for each PU using the high resolution gridded population data sets in the Zonal Statistics Tool in ArcGIS.

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