and southernrelatively well. Only for the northeastern region the coverage of the sample is limited. This is partially due to the exclusion from the universe of Manipur, Mizoram, Tripura, Meghalaya, and Assam, five small states with relatively poor household survey data. It is also due to the shortcomings of village boundaries in the states of Arunachal Pradesh and Meghalaya. Not surprisingly, the places in the sample are quite different in size, with their surface varying from 0.1 to 464.7 km<sup>2</sup> .

# **3. Human judgment**

Places are usually classified as urban or rural based on a narrow set of indicators that most often includes population and administrative status. Sometimes additional metrics such as population density or the share of employment out of agriculture are considered as well. But even this may not be enough to ensure that the classification is consistent. Cities have diverse characteristics both across and within countries. This multiplicity of aspects to consider calls for an element of human judgment.

_Google images as virtual site visits_ A thorough subjective assessment would require visiting every city, town, and village in the sample and literally seeing how they look like. However, the time and cost implications of visiting 1,277 places scattered across a large country are dissuasive. To resolve a similar challenge, an emerging literature uses Google images as a proxy for information gathered through field surveys. Salesses et al. (2013) rely on over 4,000 Google street view images to crowdsource perceptions on safety and livability, for two major cities of the US. In a scaling up effort, Dubey et al. (2016) expand this approach to over 110,000 images from 56 cities. Naik et al. (2016) use the images associated with quantified human judgment as the training set and develop a model to further quantify neighborhood appearance for 19 cities in the US through machine learning. And Naik et al. (2015) apply the machine learning model to study the changes in neighborhood appearance and their relationship with neighborhood socioeconomic characteristics.

Following the literature, we relied on open-source images accessible through Google Earth and Google Maps as a proxy for real site visits. The images provided by Google are an integrated collection of processed satellite imagery, aerial photos and street view pictures. A zoom-in option allows exploring details with a resolution ranging from 15 cm to 15 m. With these images a layperson, with no special training in remote sensing, can judge the characteristics of large numbers of places.<sup>2</sup> The views convey information on the type of land cover, the characteristics of man-made structures, the layout of these structures, and transportation vehicles on roads. While three-dimensional images are not available for India, the combination of rotation and tilt options allows examining each place from different angles, giving a sense of the height and quality of buildings. A street view option providing 360-degree panoramic ground-level photos is also available for selected places in India. And tags highlight amenities such as educational institutions, health facilities, religious buildings and recreational parks, among others.

To support the virtual assessment of the places in the sample, we overlaid the digitized boundary of each of these places on top of Google images, and used the images falling within the corresponding boundary as the subject for human judgment (figure 3). For the vast majority of places, we relied on images taken

> 2 Information on these images is available at https://www.google.com/streetview/explore/, https://support.google.com/earth/answer and https://support.google.com/mapsdata/.

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