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Missing millions: undercounting urbanization in India
by
Onda, Kyle
, Sinha, Parmanand
, Stevens, Forrest R.
, Kaza, Nikhil
, Gaughan, Andrea E.
in
Algorithms
/ Censuses
/ Clustering
/ Demography
/ Environment
/ Jurisdiction
/ Measurement
/ Original Paper
/ Population
/ Population (statistical)
/ Population Economics
/ Public Health
/ Public officials
/ Social Sciences
/ Sociology
/ Statistics
/ Urban areas
/ Urban population
/ Urban populations
/ Urbanization
2019
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Missing millions: undercounting urbanization in India
by
Onda, Kyle
, Sinha, Parmanand
, Stevens, Forrest R.
, Kaza, Nikhil
, Gaughan, Andrea E.
in
Algorithms
/ Censuses
/ Clustering
/ Demography
/ Environment
/ Jurisdiction
/ Measurement
/ Original Paper
/ Population
/ Population (statistical)
/ Population Economics
/ Public Health
/ Public officials
/ Social Sciences
/ Sociology
/ Statistics
/ Urban areas
/ Urban population
/ Urban populations
/ Urbanization
2019
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Do you wish to request the book?
Missing millions: undercounting urbanization in India
by
Onda, Kyle
, Sinha, Parmanand
, Stevens, Forrest R.
, Kaza, Nikhil
, Gaughan, Andrea E.
in
Algorithms
/ Censuses
/ Clustering
/ Demography
/ Environment
/ Jurisdiction
/ Measurement
/ Original Paper
/ Population
/ Population (statistical)
/ Population Economics
/ Public Health
/ Public officials
/ Social Sciences
/ Sociology
/ Statistics
/ Urban areas
/ Urban population
/ Urban populations
/ Urbanization
2019
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Journal Article
Missing millions: undercounting urbanization in India
2019
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Overview
The measurement and characterization of urbanization crucially depends upon defining what counts as urban. The government of India estimates that only 31% of the population is urban. We show that this is an artifact of the definition of urbanity and an underestimate of the level of urbanization in India. We use a random forest-based model to create a high-resolution (~ 100 m) population grid from district-level data available from the Indian Census for 2001 and 2011, a novel application of such methods to create temporally consistent population grids. We then apply a community-detection clustering algorithm to construct urban agglomerations for the entire country. Compared with the 2011 official statistics, we estimate 12% more of urban population, but find fewer mid-size cities. We also identify urban agglomerations that span jurisdictional boundaries across large portions of Kerala and the Gangetic Plain.
Publisher
Springer,Springer Netherlands,Springer Nature B.V
Subject
/ Censuses
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