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Identifying Urban Areas by Combining Data from the Ground and from Outer Space : An Application to India

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  • Galdo,Virgilio
  • Li,Yue
  • Rama,Martin G.
Abstract
This paper develops a tractable method to identify urban areas and applies it to India, where urbanization is messy. Google Earth images are assessed subjectively to determine whether a stratified large sample of Indian cities, towns and villages, as officially defined, are urban or rural in practice. Based on these assessments, a regression analysis combines two sources of information?data from georeferenced population censuses and data from satellite imagery?to identify the correlates of units in the sample being urban. The resulting model is used to predict whether the other units in the country are urban or rural in practice. Contrary to frequent claims, India is not substantially more urban than implied by census data. And the speed of urbanization is only marginally higher than official statistics suggest. But a considerable number of locations are misclassified in the midrange between villages and state capitals. The results confirm the value of combining subjective assessments with data from these different sources.

Suggested Citation

  • Galdo,Virgilio & Li,Yue & Rama,Martin G., 2018. "Identifying Urban Areas by Combining Data from the Ground and from Outer Space : An Application to India," Policy Research Working Paper Series 8628, The World Bank.
    • Handle: RePEc:wbk:wbrwps:8628
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    File URL: http://documents.worldbank.org/curated/en/892371540833795715/pdf/WPS8628.pdf
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    Cited by:

    1. Daniele Ehrlich & Sergio Freire & Michele Melchiorri & Thomas Kemper, 2021. "Open and Consistent Geospatial Data on Population Density, Built-Up and Settlements to Analyse Human Presence, Societal Impact and Sustainability: A Review of GHSL Applications," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    2. World Bank, "undated". "South Asia Economic Focus, Fall 2017," World Bank Publications - Reports 28397, The World Bank Group.
    3. Galdo, Virgilio & Li, Yue & Rama, Martin, 2021. "Identifying urban areas by combining human judgment and machine learning: An application to India," Journal of Urban Economics, Elsevier, vol. 125(C).

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