derived from Landsat 7 and Landsat 8 satellites have been widely used for academic research, because of their high spatial resolution, high spectral resolution and long temporal coverage (Burchfield et al. 2006).<sup>5</sup> We compared GHSL with two other products reporting built-up cover. One of them is based on imagery from MODIS and the other on imagery from the twin satellites TerraSAR-X and TanSAR (Friedl et al. 2010; Esch et al. 2017).<sup>6</sup> Because of its higher resolution GHSL captures built-up cover in forest areas, such as Kerala, better than MODIS. The correlation between built-up data based on GHSL and those on TerraSARX and TanSAR imagery is very high. For the built-up share at the town and village level it reaches 0.96. In light of this, and also given the wide use of the Landsat-based landcover product, we chose GHSL 2013/14 as our data source. Our _built-up share_ indicator was computed for every place as the ratio between its built-up area and its total area.

For nighttime lights, we relied on the Global Radiance Calibrated Nighttime Lights product. Data in this case are derived from images taken by the Operational Linescan System (OLS) sensors aboard satellites from the US Air Force Defense Meteorological Satellite Program (DMSP). The product has a higher quality than other products because it captures stable lights, addresses the saturation problem, and is intercalibrated to reduce inconsistencies across satellites (Elvidge et al. 2009; Hsu et al. 2015; NOAA 2014). The intensity of nighttime lights is reported in Digital Numbers (DN) as of 2011.

A well-known challenge when using data on nighttime lights is that even areas without human activity may appear to be lit up (Henderson et al. 2003; Li and Zhou 2017). This may be due to blooming, to the reflection of light from surrounding water, to the sensitivity of the sensors, or to geo-referencing errors made while capturing and composing the data. To avoid an overestimation of the lit-up share, a threshold is generally set up for nighttime light intensity, and only observations above the threshold are considered urban. However, the appropriate threshold varies considerably across countries, and it is correlated with their level of economic development.

To identify the appropriate threshold, we reviewed a dozen studies that together present 16 threshold estimates for different countries. We then matched their results with the corresponding gross domestic product per capita. We found a positive and statistically significant correlation between threshold estimates and product per capita. Using the estimated relationship, we concluded that the appropriate threshold for India in 2011 was 15 DN (Galdo et al. 2018). Our _lit-up share_ indicator was thus computed as the fraction of a place’s surface with light intensity above this threshold.

Beyond these four indicators, our framework is flexible to the inclusion of other metrics providing information on how urban a place may be in practice. Including these other metrics in the prediction model helps assess whether the four indicators we consider are informative enough on their own. Following the literature, in some of the analyses we added the normalized difference vegetation index

- 5      The Landsat 7 satellite was launched in 1999. It has seven spectral bands at a spatial resolution of 30 m and a temporal frequency of 16 days. Landsat 8 was launched in 2013. It includes nine spectral bands with the same spatial resolution and temporal frequency as Landsat 8.

- 6     MODIS based product relies on images taken by sensors aboard satellites from the Earth Observing System, known as the Land Cover Type Yearly Grid. The product has a resolution of 500 m and is available from 2001 onward. The imagery from the twin satellites TerraSAR-X and TanSAR are radar images, which are part of the recent WorldDEM program. Built-up cover data for 2011 are available at a resolution of 12 m.

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