square meters or larger than 500 square meters was eliminated. The lower bound was obtained using data on average housing size in rural areas from the Bangladesh Integrated Household Survey (2015). The available imagery was contrasted with the buildings obtained from OSM to ensure reasonable coverage (Figure 5.B).<sup>29</sup> We took the number of households in a mauza (according to the 2011 national census) and calculated how many segments would need to be created to obtain 100 households per segment. At the same time, the number of buildings in the segment was calculated. Given these two numbers, it was assumed that households were uniformly distributed in the buildings available in the data set. The team then proceeded to calculate the number of buildings needed per segment to obtain the same number of segments as if segments had been defined based on population.<sup>30</sup> This procedure was undertaken to connect the number of households in the 2011 census data to the buildings observed in the OSM data set. If the underlying assumption was reasonable, dividing the segments using buildings would produce segments with roughly the same number of households<sup>31</sup> within a particular mauza.

Using the centroids of each building, the team generated non-overlapping polygons within the mauza (Figure 5.C). To generate the polygons, the team minimized the total squared distance from the center of the segment to each building in the segment, given the minimum and the maximum number of buildings that are allowed in each subset.<sup>32</sup> The segments were then checked, to solve any inconsistencies due to irregular shapes of particular mauzas. Once this process was complete, the team moved forward with a ground-truthing exercise to verify assumptions, followed by listing of the selected segments (Figure 5.D).

This process described had to accommodate two detours: First, for some rural northern mauzas the availability of demarcated buildings in OSM data was low, so in those cases a different approach was used. Since enumeration teams would in any case be sent to the field to list, segments were created based on the assumption that the population was uniformly distributed across the area. Enumeration areas were then created that field teams would be asked to list using field protocols.

Second, the mauza shapefiles less accurate in urban areas: In general, the shapefiles available were not a perfect match to the actual areas; with this in mind, the team proceeded with listing in the selected mauzas using the available boundaries. In two cases (Light House Para and Nazirar Tek) in Cox’s Bazar Sadar, the location of the mauzas on the shapefile did not coincide with the actual location of the areas.<sup>33</sup> The field team

> 29 In 13 cases the coverage was low, so a different approach was adopted to segment within those mauzas.

> 30 For example, in Shahparir Dwip, the 2011 census reports 4,351 households, and 2,081 buildings were found. To obtain 100 household segments, it was necessary to create 44 segments. Assuming that the distribution of population is uniform, each segment will have around 47 buildings (2081/44).

> 31 In a sense, the listing process was able to validate the assumption, which worked surprisingly well in rural areas.

> 32Although a set number of segments had to be created, this process needed to run with some flexibility. The number of buildings had an upper and a lower bound. For example, if 200 buildings are observed and two segments are needed, the method will use 99 as a lower bound and 101 as an upper bound as a first try, then proceed until a solution to the problem is obtained and the two segments are drawn. On average, the segments in that area will have the same number of buildings. For more details on the process, see <u>https://pro.arcgis.com/en/pro-app/tool-reference/geostatistical-analyst/generate-subset-polygons.htm.</u>

> 33 For example, Light House Para in the shapefile was located on the beach with no households, whereas in actuality, the area is in a moderately populated area much farther inside the city.

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