depending on their primary input data: (1) satellite images of land cover and of built‐up characteristics; (2) satellite images of areas lit up at night; and (3) gridded population data created by using satellite images and other auxiliary information to redistribute aggregated data from population censuses.

To many, these approaches hold the promise of cross‐country neutrality and comparability because they ensure global coverage, spatial granularity, and temporal consistency. But a consensus has yet to emerge on the most reliable method to identify urban areas. To date, then, the results have been heterogeneous. At the global level, the urban areas identified cover from less than 300,000 square kilometers to over 700,000 square kilometers, depending on the method (Potere and others 2009).

Some features of the input data and their interpretation may help explain the large variations in results when using these methods:

- Most analyses are conducted at the cell level, but a city is more than the sum of its cells. This remark goes back to the subjectivity of the concept of a city. The proper of an urban setting is the interlinking of cells in a broader spatial context. Assessing whether a cell is urban without referring to the surrounding cells is thus potentially misleading, and even more so when the urbanization process is messy (World Bank 2015).

- Analyses relying primarily on land cover classification algorithms can accurately identify built‐up areas and infrastructurethe signature characteristics of urban areas. But cities include other land cover types such as parks and water bodies. The continuum between rural and urban areas poses a challenge to these algorithms as well (Mertes and others 2015; Pesaresi, <mark>Ehrlich, a</mark> nd others 2016; Schneider 2012).

- Analyses relying primarily on nightlight data capture the concentration of human activities in an unprecedented manner. However, problems such as blooming lead to biased estimates of the urban extent (Elvidge and others 2007; Elvidge and others 2009; Li and Zhou 2017). As access to electricity becomes more common and reliable, the pace of urbanization could be misinterpreted as well.

- When using gridded population data, one fundamental issue is the lack of reliable information on disaggregated administrative boundaries. Open‐source boundaries are available mostly at the district or subdistrict level (GADM 2012). Population census data are at best georeferenced to these levels of administrative boundaries. And both population data and administrative boundaries tend to be out of date by several years.

To address these limitations, we develop a tractable method for identifying urban areas and illustrate its potential by applying it to India. The proposed method differs from the previous literature in three important ways:

- Instead of cells, we use _cities, towns and villages_ , as officially defined, as our units of analysis. Because the available boundaries of administrative units tend to be unavailable, out of date, or erroneous, we rely on newly available georeferenced and digitized boundaries of administrative units at all levels, based on the India’s _Administrative Atlas_  _2011_ (ORGI 2011b) **.** Although the analysis covers the entire universe of cities, towns, and villages, an in‐depth assessment is conducted for a stratified sample of them. The sample design results in roughly three‐quarters of the units being administratively urban and the rest administratively rural.

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