force survey ( _Survei Tenaga Kerja Nasional,_ SAKERNAS) for the years 2013 – 2015.<sup>12</sup> In doing so, we measure the commuting flow from a given origin district _i_ to a destination district _j_ as the share of workers who live in _i_ but commute daily to work in _j_ , where – following SAKERNAS – workers are defined to include all employed wage workers including casual workers, selfemployed workers, and unpaid family workers, where anyone who worked for at least one hour consecutively in the previous week, including temporary non-workers who normally meet the condition, is considered employed.

Both the AI and the two cluster algorithms require a gridded population data set. We use the _Landscan-2012_ gridded population data set produced by Oak Ridge National Laboratory. This population grid has a resolution of 30 arc-seconds. An earlier version of the same population grid was used by Uchida and Nelson (2009) in their original application of the AI. More generally, the _Landscan_ population grid is the most established global gridded population data set and has been widely used in social scientific research.<sup>13</sup> This includes the paper by Henderson _et al_ . (2018), who use the same _Landscan-2012_ data in identifying urban areas and for constructing measures of population, and economic, density for six African countries. Importantly, Henderson _et al_ . (2018) “ground-truth” the _Landscan_ data, reaching the conclusion that the data do good job in estimating population at a fine spatial scale. The population grid is derived through distributing population data for sub-national administrative units across grid cells using a modeling process that relies on other geo-spatial data sources and high-resolution satellite imagery analysis.<sup>14</sup> In addition to gridded population data, the AI also requires data on estimated travel times. The travel time data originally used by Uchida and Nelson (2009) for the AI were based on “... _estimates of the time required to travel 1 km over different road and off-road surfaces...”_ and were derived from a cost surface that was constructed from a variety of Geographic Information System (GIS) data layers. 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>15</sup> The AI estimates used in this paper are taken from Roberts _et al._ (2017) and based on an updated version of this same cost surface layer from Berg _et al._ (2017). This updated layer is derived from more recent (i.e. _circa_ 2010 versus _circa_ 2000) data on roads, railroads, and land cover.<sup>16</sup> Finally, as already described in Section 2, the NTL data that we use in this paper are VIIRS NTL data taken from NOAA’s 2015 annual composite product.

# _3.2. Mapping to districts_

> 12 Importantly, the sampling strategy of the SAKERNAS August rounds is stratified at the district level for these years. We average the commuting flows over three years, rather than using a single year, to smooth out any temporary measurement error in the survey data.

> 13 Alternative population grids that we could have used are WorldPop (http://www.worldpop.org.uk/) and GHS-Pop

> (http://ghsl.jrc.ec.europa.eu/ghs_pop.php). Roberts _et al._ (2017) compare the level of agreement between maps of urban areas generated using the AI and cluster algorithms with different gridded population products. In general, the level of agreement is fairly high.

> 14 See http://web.ornl.gov/sci/landscan/landscan_documentation.shtml for more information.

> 15 See Appendix Table A.1 in Uchida and Nelson (2009) for more details.

> 16 One important limitation of the resultant travel time estimates is that they do not take account of travel time delays owing to traffic congestion.

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