- _Subjective assessments_ of images provided by Google Earth are used to determine whether the cities, towns and villages in the sample are urban or rural in practice. Google Earth is an integrated collection of processed satellite, aerial, street view, and 3‐D images. Using this combination of perspectives, a layperson can judge the characteristics of a specific location. Google Earth images have been used to crowd‐source judgments on city safety, inequality, and gentrification (Hwang and Sampson 2014; Salesses, Schechtner, and Hildalgo 2013). They have also been used in the remote sensing literature to establish training sites and validation sites at the pixel level (Bontemps and others 2011; Schneider, Friedl, and Potere 2009; Yu and Gong 2012).

- A regression analysis combining _multiple sources of information_ is used to identify the correlates of units in the sample being urban in practice. We georeference population census data, which come from the ground, to all administrative boundaries in India. We also incorporate data from open‐source satellite imagery, which come from outer space, on both built‐up areas and nighttime lights. Bootstrapping and cross‐validation are applied to choose the model specifications with higher prediction accuracy. These model specifications and the estimated coefficients are then used to predict whether each of the other units in the country is urban or rural in practice.

This exercise allows a more accurate assessment of the rate and speed of urbanization in India than was available before. Contrary to frequent claims, we do not find that India is substantially more urban at present than stated by census data. And the speed of urbanization over the last decade is only marginally higher than official statistics suggest. At the same time, however, we find a considerable misclassification of locations in the midrange between villages and state capitals.

The analysis also has implications beyond India. We find that the accuracy of the assessment increases substantially when combining data from the ground and from outer space. In the process, it appears that some of the most frequently used indicators of urban status can yield misleading results. Among the indicators from the ground, population density is a poor predictor of urban status. Among those from outer space, nighttime light intensity leads to a drastic overestimation of the speed of urbanization.

# **2. A representative sample of units**

The official boundaries of cities, towns and villages are built on historical data and capture the best knowledge governments have of the spatial distribution of economic activities and people. Admittedly, these boundaries may be too small to account for large metropolitan areas, which typically include nearby suburban and exurban areas. But they define the constituency to which each local government provides services and is accountable. And they are the basic sample unit used by national statistical agencies to conduct censuses and surveys. Therefore, important information about cities, towns and villages can be captured at this level of spatial disaggregation.

## _The universe of administrative units_

In this study, we take advantage of newly digitized boundaries of administrative units in India, now available all the way down to the town or village level. Although not official, these boundaries are based on India’s _Administrative Atlas_  _2011_ (ORGI 2011b). They were generated as part of a broader research project, the Spatial Database for South Asia (Li and others 2015). Construction of the boundaries required 4