grids, we use data at a spatial resolution of approximately 1 km.<sup>31</sup> While the _LandScan_ and _GHSPop_ data are available for the entire globe, the _WorldPop_ data are limited to 53 countries, including 27 LAC countries. One other key difference is that _LandScan_ provides estimates of ambient population (i.e. average population over 24 hours), while both _GHS-Pop_ and _WorldPop_ provide estimates of residential population.<sup>32</sup>

# _3.2  Travel time and settlement point data_

The travel time data used in the original AI analysis were based on “... _estimates of the time required to travel 1 km over different road and off-road surfaces...”_ derived from a cost surface that was constructed from a variety of GIS data layers (Uchida and Nelson, 2010). These layers included data on road and rail networks, navigable rivers and water bodies, travel delays for crossing international borders, roughness of terrain and foot based travel for off-road and paths.<sup>33</sup> The AI estimates generated for this paper are based on an updated version of this same cost surface layer that is taken from Berg, Blankespoor, Li and Selod (2017). This updated layer is derived from more recent (i.e. _circa_ 2010 versus _circa_ 2000) data on roads, railroads, and land cover.

In contrast to the population and travel time data, we use the same settlement point layer as Uchida and Nelson (2010) – i.e. GRUMP Settlement Points v 1.<sup>34</sup> Because these data relate to the year 2000, it means that the updated AI estimates of national urban population shares generated as part of this paper are likely to be biased downwards. This will be the case, in particular, to the extent that there are settlements that were below �<sup>�</sup> (i.e. the population threshold used to define a “sizeable” settlement) in 2000, but whose populations have since grown to exceed this threshold. The new agglomerations that will have been created as a result of this will be missed by our updated AI estimates.<sup>35</sup>

# **4. Results**

In implementing the AI and cluster methods, we use the same parameter values as Uchida and Nelson (2010) and Dijkstra and Poelman (2014) respectively. Hence, for the AI, we assume �<sup>�</sup> � 50,000, �<sup>�</sup> �150 people per km<sup>2</sup> , and �<sup>�</sup> �60 minutes, while, for the cluster method, we assume �<sup>�</sup> �� �300 people per km<sup>2</sup> and �<sup>�</sup> �� �5000. For these parameter values, we generate estimates of the share of each country’s population that lives in urban areas using each of the three gridded population data sets described above – i.e. _LandScan 2012_ , _GHS-Pop_ and _WorldPop_ . We also estimate national urban population shares using the cluster method based solely on the share of population living in high density clusters, for which �<sup>�</sup> = 1,500 people per km<sup>2</sup> and �<sup>�</sup> = 50,000 people. Hence, overall, we generate a total of nine different sets of “consistent” estimates of national

> 31 Both _WorldPop_ and _GHS-Pop_ data are available at a higher spatial resolution (100 m in the case of _WorldPop_ ).  For this paper, however, the data were aggregated up to a resolution of 1 km to allow for comparability with the _LandScan_ data.

> 32 _GHS-Pop_ data are available for download from <u>http://ghsl.jrc.ec.europa.eu/ghs_pop.php, while</u> _WorldPop_ can be downloaded from http://www.worldpop.org.uk/.

> 33 See Appendix Table A.1 in Uchida and Nelson (2010) for more details.

> 34 http://sedac.ciesin.columbia.edu/data/set/grump-v1-settlement-points.

> 35 Performing a complete update of the AI remains an obvious area for future research.

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