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

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,...

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Main Authors:	Galdo, Virgilio, Li, Yue, Rama, Martin
Format:	Working Paper
Language:	English
Published:	World Bank, Washington, DC 2018
Subjects:	URBANIZATION URBAN EXTENT URBAN SPRAWL SATELLITE IMAGERY GEOSPATIAL ECONOMICS GEOREFERENCED DATA
Online Access:	http://documents.worldbank.org/curated/en/892371540833795715/Identifying-Urban-Areas-by-Combining-Data-from-the-Ground-and-from-Outer-Space-An-Application-to-India http://hdl.handle.net/10986/30648

id	okr-10986-30648
recordtype	oai_dc
spelling	okr-10986-306482021-06-08T14:42:48Z Identifying Urban Areas by Combining Data from the Ground and from Outer Space : An Application to India Galdo, Virgilio Li, Yue Rama, Martin URBANIZATION URBAN EXTENT URBAN SPRAWL SATELLITE IMAGERY GEOSPATIAL ECONOMICS GEOREFERENCED DATA 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. 2018-11-01T18:44:00Z 2018-11-01T18:44:00Z 2018-10 Working Paper http://documents.worldbank.org/curated/en/892371540833795715/Identifying-Urban-Areas-by-Combining-Data-from-the-Ground-and-from-Outer-Space-An-Application-to-India http://hdl.handle.net/10986/30648 English Policy Research Working Paper;No. 8628 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper South Asia India
repository_type	Digital Repository
institution_category	Foreign Institution
institution	Digital Repositories
building	World Bank Open Knowledge Repository
collection	World Bank
language	English
topic	URBANIZATION URBAN EXTENT URBAN SPRAWL SATELLITE IMAGERY GEOSPATIAL ECONOMICS GEOREFERENCED DATA
spellingShingle	URBANIZATION URBAN EXTENT URBAN SPRAWL SATELLITE IMAGERY GEOSPATIAL ECONOMICS GEOREFERENCED DATA Galdo, Virgilio Li, Yue Rama, Martin Identifying Urban Areas by Combining Data from the Ground and from Outer Space : An Application to India
geographic_facet	South Asia India
relation	Policy Research Working Paper;No. 8628
description	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.
format	Working Paper
author	Galdo, Virgilio Li, Yue Rama, Martin
author_facet	Galdo, Virgilio Li, Yue Rama, Martin
author_sort	Galdo, Virgilio
title	Identifying Urban Areas by Combining Data from the Ground and from Outer Space : An Application to India
title_short	Identifying Urban Areas by Combining Data from the Ground and from Outer Space : An Application to India
title_full	Identifying Urban Areas by Combining Data from the Ground and from Outer Space : An Application to India
title_fullStr	Identifying Urban Areas by Combining Data from the Ground and from Outer Space : An Application to India
title_full_unstemmed	Identifying Urban Areas by Combining Data from the Ground and from Outer Space : An Application to India
title_sort	identifying urban areas by combining data from the ground and from outer space : an application to india
publisher	World Bank, Washington, DC
publishDate	2018
url	http://documents.worldbank.org/curated/en/892371540833795715/Identifying-Urban-Areas-by-Combining-Data-from-the-Ground-and-from-Outer-Space-An-Application-to-India http://hdl.handle.net/10986/30648
_version_	1764472526612725760

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

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