Urbanization in India: Population and Urban Classification Grids for 2011

India is the world’s most populous country, yet also one of the least urban. It has long been known that India’s official estimates of urban percentages conflict with estimates derived from alternative conceptions of urbanization. To date, however, the detailed spatial and settlement boundary data needed to analyze and reconcile these differences have not been available. This paper presents gridded estimates of population at a resolution of 1 km along with two spatial renderings of urban areas—one based on the official tabulations of population and settlement types (i.e., statutory towns, outgrowths, and census towns) and the other on remotely-sensed measures of built-up land derived from the Global Human Settlement Layer. We also cross-classified the census data and the remotely-sensed data to construct a hybrid representation of the continuum of urban settlement. In their spatial detail, these materials go well beyond what has previously been available in the public domain, and thereby provide an empirical basis for comparison among competing conceptual models of urbanization.

[1]  C. Chandramouli,et al.  The Census of India , 1932, Nature.

[2]  Stefan Leyk,et al.  Understanding urbanization: A study of census and satellite-derived urban classes in the United States, 1990-2010 , 2018, PloS one.

[3]  Kamala Marius-Gnanou,et al.  Toward a better appraisal of urbanization in India, a fresh look at the landscape of morphological agglomerates , 2010 .

[4]  Huadong Guo,et al.  A Global Human Settlement Layer From Optical HR/VHR RS Data: Concept and First Results , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[5]  R. Bhagat,et al.  EMERGING PATTERN OF URBANIZATION AND THE CONTRIBUTION OF MIGRATION IN URBAN GROWTH IN INDIA , 2009 .

[6]  Ferri Stefano,et al.  Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014 , 2016 .

[7]  Aneta J. Florczyk,et al.  Exposing the urban continuum: implications and cross-comparison from an interdisciplinary perspective , 2018, Int. J. Digit. Earth.

[8]  C. Field Climate change 2014 : impacts, adaptation and vulnerability : Working Group II contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change , 2014 .

[9]  Gordon McGranahan,et al.  Urban growth in emerging economies : lessons from the BRICS , 2014 .

[10]  A. Kundu,et al.  Changing Patterns of Migration in India: A Perspective on Urban Exclusion , 2016 .

[11]  P. Mukhopadhyay,et al.  Subaltern Urbanisation in India , 2017 .

[12]  Stefan Leyk,et al.  Assessing the Accuracy of Multi-Temporal Built-Up Land Layers across Rural-Urban Trajectories in the United States. , 2018, Remote sensing of environment.

[13]  Pierre Soille,et al.  Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas , 2016, Remote. Sens..

[14]  K. Riahi,et al.  The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century , 2017 .

[15]  Brian C. O'Neill,et al.  Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways , 2016 .

[16]  Deborah Balk,et al.  The Distribution of People and the Dimension of Place: Methodologies to Improve the Global Estimation of Urban Extents , 2004 .

[17]  Martino Pesaresi,et al.  A New Method for Earth Observation Data Analytics Based on Symbolic Machine Learning , 2016, Remote. Sens..

[18]  J. Lamond,et al.  Cities and Flooding: A Guide to Integrated Urban Flood Risk Management for the 21st Century , 2012 .

[19]  J. Mennis Generating Surface Models of Population Using Dasymetric Mapping , 2003, The Professional Geographer.

[20]  L. Dijkstra,et al.  Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping , 2017 .