The discrepancies between these measures and our predicted urbanization rates are substantial. But such discrepancies should not come as a surprise, as these other measures are more parsimonious in their definition of what makes a place urban. In light of these differences, we see these studies as offering interesting perspectives, but not fundamentally challenging our results.

The discrepancy is also substantial relative to some recent urban layer products based on satellite imagery that report high urbanization rates. The Geopolis project proposes a measure based on built-up cover, cell contiguity and a population threshold, and predicts an urbanization rate of 42.0 percent for 2011 (Denis and Marius-Gnanou 2011). The GHSL project applies thresholds on share of built-up area, cell contiguity, population size and population density to define urban cores. Based on this approach, GHSL estimates that 53.6 percent of India’s population was urban in 2015 (Dijkstra et al. 2018; Pesaresi et al. 2019).<sup>10</sup> These figures exceed our predicted urbanization rate, sometimes by a wide margin.

Meanwhile, other recent studies based on satellite imagery suggest the urbanization rate could be close to or even lower than our prediction. Baragwanath et al. (2019) apply an algorithmic approach with different distance buffers to define urban markets for India. Their estimates on the share of India’s population residing in urban markets in 2011 have a median value of 29.1 percent and a mean value of 27.6 percent. As another example, the Metropolitan Areas Extension Database or BEAM project uses carefully cleaned nighttime lights to identify urban areas (Ch et al. 2018). Based on its results, 23.9 percent of India’s population lived in urban areas in 2010.

On closer inspection, however, the difference between our estimates and those in other recent studies does not arise from the underlying land classification, but rather from the population data used. This can be seen by comparing our estimates to two global urban layer products relying on satellite imagery and available as open source data, namely BEAM and GHSL. As mentioned above, a recent study based on BEAM leads to a lower urbanization rate than our methodology, while a study based on GHSL delivers a much higher urbanization rate.

For the Indian places considered in our universe, the total area we classify as urban based on our methodology is quite close to that of the GHSL urban core and higher than the BEAM urban area (table 9). However, these urban areas translate into very different estimates of the urbanization rate depending on which gridded population data are used. We consider three products in this respect: GHSL for 2015, LandScan for 2011 and WorldPop for 2010.<sup>11</sup> Our predicted urbanization rate for India would have declined only marginally had we used population data from LandScan, instead of data from the 2011 Census of India. But it would have been much smaller if we had used population data from WorldPop and much higher had we used population data from GHSL. The similarity of results, relative to the 2011 Census of India, makes the use of Landscan data more appealing than the alternatives.

> 10 GHSL country statistics can be generated from the Community pre-Release of GHS Data Package <u>https://ghsl.jrc.ec.europa.eu/CFS.php.</u>

> 11 Information on gridded population is provided by Schiavina et al. (2019) for GHSL, by LandScanTM (2011) <mark>for LandScan and by W</mark> orldPop (2017) for WorldPop.

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