# _3.1 Gridded population data_

In their original AI analysis, Uchida and Nelson (2010) constructed estimates of urban population shares for a global cross-section of countries for the year 2000 using two global gridded population data sources – CIESIN’s _GRUMP_ (Global Rural-Urban Mapping Project) v 1 and Oak Ridge National Laboratories’ _LandScan_ population grids.<sup>25</sup> Uchida and Nelson’s estimates were then subsequently updated to the year 2010 as part of the World Bank and IMF’s Global Monitoring Report 2013 on _Rural-Urban Dynamics and the Millennium Development Goals_ (World Bank and IMF, 2013). In this update, the same two global gridded population data sources were used except that the gridded population data instead now referred to the year 2010. In the case of GRUMP, the 2010 population data are based on the projection of data from earlier years.<sup>26, 27</sup> Meanwhile, for the application of the cluster method, Dijkstra and Poelman (2014) limited their attention to the EU-27 countries, and used a mixture of population grids for the year 2006 based on different methods.<sup>28</sup> Subsequent to this, Deuskar and Stewart (2016) have also applied the cluster method to a further 53 countries using _WorldPop_ gridded population data.<sup>29</sup> In this paper, we use updated estimates of the national share of population living in urban areas based on the application of both the AI and cluster methods. We generate these estimates using three different population grids – _LandScan 2012_ , _GHS-Pop_ and _WorldPop_ . All three of these grids have extensive cross-country coverage and are derived using “top-down” methods which involve the dasymetric distribution of population data for sub-national administrative units across grid cells. Where the three methods differ is in the exact weights they use to distribute the population within administrative areas. Although the exact methods are proprietary, _LandScan_ derives the weights using a modeling process that relies on “ _primary geospatial input or ancillary datasets, including land cover, roads, slope, urban areas, village locations, and high resolution imagery analysis; all of which are key indicators of population distribution_ .” _LandScan_ also applies _ex post_ adjustments to the weights where obvious errors are identified through a manual verification and modification process.<sup>30</sup> _WorldPop_ uses a similar range of input data to derive weights through a “random forest” modeling approach (Stevens, Gaughan, Linard and Tatem, 2015). Finally, _GHS-Pop_ assigns the population of a given administrative unit to the built-up area within that unit. The built-up area data are taken from JRC’s Global Human Settlement Layer (GHSL), which is itself derived from satellite imagery (Freire, MacManus, Pesaresi, Doxsey-Whitfield and Mills, 2016). For all three population

> 25 Uchida and Nelson’s (2010) original AI analysis was undertaken as an input into the World Bank’s 2009 World Development Report (World Bank, 2008).

> 26 In updating the AI, the World Bank and IMF used the same settlement point and travel time layers as in Uchida and Nelson’s (2010) original work, both of which referred to the year 2000.  In this sense, the update performed was only partial.

> 27 Ellis and Roberts (2015) also make use of the same 2010 AI estimates.

> 28 For Croatia, Denmark, Sweden, Finland, Austria, the Netherlands, Slovenia, Switzerland and Norway they used grids that were constructed based on “bottom-up” methods from geo-coded address locations and population register data. By contrast, for the remaining EU countries they used a population disaggregation grid created by the European Commission’s Joint Research Center (JRC) based on administrative area population and CORINE land cover data. Finally, they used LandScan data for French overseas regions, as well as Madeira and Açores in Portugal (see Dijkstra and Poelman, 2014, pp 7-8 for more details).

> 29 Although _WorldPop_ data sets are available for more countries, Deuskar and Stewart limit themselves to countries for which the data have been derived using “random forest” modeling methods.  These methods are thought to be superior to earlier methods that were used to generate the population grids for other countries.

> 30 See <u>http://web.ornl.gov/sci/landscan/landscan_documentation.shtml for more information.</u>

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