Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We propose a methodology for identifying urban areas that combines subjective assessments with machine learning, and we apply it to India, a country where several studies see the official urbanization rate as an under-estimate. For a representative sample of cities, towns and villages, as administratively defined, we rely on human judgment of Google images to determine whether they are urban or rural in practice. We collect judgments across four groups of assessors, differing in their familiarity with India and with urban issues, following two different protocols. We then combine the judgment-based classification with data from the population census and from satellite imagery to predict the urban status of the sample. The Logit model, and LASSO and random forests methods, are applied. These approaches are then used to decide whether each of the out-of-sample administrative units in India is urban or rural in practice. We do not find that India is substantially more urban than officially claimed. However, there are important differences at more disaggregated levels, with “other towns” and “census towns” being more rural, and some southern states more urban, than is officially claimed. The consistency of human judgment across assessors and protocols, the easy availability of crowd-sourcing, and the stability of predictions across approaches, suggest that the proposed methodology is a promising avenue for studying urban issues.