Identifying Urban Areas by Combining Human Judgment and Machine Learning

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dc.creatorGaldo, Virgilio
dc.creatorLi, Yue
dc.creatorRama, Martin
dc.date2020-02-26T15:48:54Z
dc.date2020-02-26T15:48:54Z
dc.date2020-02
dc.date.accessioned2026-07-01T00:39:56Z
dc.descriptionThis paper proposes a methodology for identifying urban areas that combines subjective assessments with machine learning, and applies 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, human judgment of Google images is used to determine whether they are urban or rural in practice. Judgments are collected across four groups of assessors, differing in their familiarity with India and with urban issues, following two different protocols. The judgment-based classification is then combined 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. The analysis does 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.
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dc.formattext/plain
dc.identifierhttp://documents.worldbank.org/curated/en/920791582554716856/Identifying-Urban-Areas-by-Combining-Human-Judgment-and-Machine-Learning-An-Application-to-India
dc.identifierhttps://hdl.handle.net/10986/33392
dc.identifier10.1596/1813-9450-9160
dc.identifier.urihttp://hdl.handle.net/123456789/408660
dc.languageEnglish
dc.publisherWorld Bank, Washington, DC
dc.relationPolicy Research Working Paper;No. 9160
dc.rightsCC BY 3.0 IGO
dc.rightshttp://creativecommons.org/licenses/by/3.0/igo
dc.rightsWorld Bank
dc.subjectURBAN AREA
dc.subjectURBANIZATION
dc.subjectHUMAN JUDGMENT
dc.subjectGOOGLE IMAGES
dc.subjectCROWD SOURCING
dc.subjectPOPULATION CENSUS
dc.subjectSATELLITE IMAGERY
dc.subjectMACHINE LEARNING
dc.subjectLOGIT MODEL
dc.subjectLASSO
dc.subjectRANDOM FORESTS METHOD
dc.titleIdentifying Urban Areas by Combining Human Judgment and Machine Learning
dc.titleAn Application to India
dc.typeWorking Paper
dc.typeDocument de travail
dc.typeDocumento de trabajo

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