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,...
| Main Authors: | , , |
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| Language: | English |
| Published: |
World Bank, Washington, DC
2018
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| Subjects: | |
| 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 |
| Summary: | 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. |
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