Urbanization in India: Population and Urban Classification Grids for 2011

Balk, Deborah; Montgomery, Mark R.; Engin, Hasim; Lin, Natalie; Major, Elizabeth; Jones, Benjamin A.

doi:10.3390/data4010035

Cited by 39 publications

(29 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This could also contribute to error in the other direction, as our disaggregation may allocate population growth that actually occurred in concentrated cities throughout the district in which a given city is located. Other data products have become recently available at finer geographic scale that could have improved the results (e.g., Balk et al 2019;Meiyappan et al 2018). However, they also suffer from poor spatial precision of spatial units and reconciling them to create temporally consistent units is an arduous task.…”

Section: Discussionmentioning

confidence: 99%

Missing millions: undercounting urbanization in India

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Missing millions: undercounting urbanization in India

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Unfortunately, for both the use of logistic curves to interpolate between estimates of built-settlement population data and cubic splines to interpolate between estimates of builtsettlement population density, independent data does not exist to evaluate the error or uncertainty of these interpolated values largely because of absolute data availability. Further, even if such urban population and urban population density data existed, because of the differing definitions of urban (2,111,112), most probably we would have had a definitional disagreement with our adopted BS definition (unless these data came with corresponding spatial extents).…”

Section: Discussionmentioning

confidence: 99%

Modelling Built-Settlements between Remotely-Sensed Observations

Nieves¹,

Sorichetta²,

Linard³

et al. 2019

Preprint

View full text Add to dashboard Cite

Mapping settlement extents at the annual time step has a wide variety of applications in demography, public health, sustainable development, and many other fields. Recently, while more multitemporal urban feature or human settlement datasets have become available, issues still exist in remotely-sensed imagery due to coverage, adverse atmospheric conditions, and expenses involved in producing such feature sets. These challenges make it difficult to increase temporal coverage while maintaining high fidelity in the spatial resolution. Here we demonstrate an interpolative and flexible modeling framework for producing annual builtsettlement extents. We use a combined technique of random forest and spatio-temporal dasymetric modeling with open source subnational data to produce annual 100m x 100m resolution binary settlement maps in four test countries of varying environmental and developmental contexts for test periods of five-year gaps. We find that in the majority of years, across all study areas, the model correctly identified between 85-99% of pixels that transition to built-settlement. Additionally, with few exceptions, the model substantially out performed a model that gave every pixel equal chance of transitioning to the category "built" in each year. This modelling framework shows strong promise for filling gaps in crosssectional urban feature datasets derived from remotely-sensed imagery, provide a base upon which to create future built/settlement extent projections, and further explore the relationships between built area and population dynamics.As a result, many studies have turned to a definition based upon the remotely-sensed physical features of urban areas, i.e. the built-environment. However, even reducing the definitional scope of urban to its physical dimension, the form of built-environment can widely vary across space and time due to materials used, differences in urban morphology, and the surrounding environmental context (18,31,32). Initially, remotely sensed urban definitions were optically-based thematic classifications of land cover, typically captured the "built-environment," including buildings, roads, runways, and, sometimes erroneously, bare soil (18,(33)(34)(35). Later improvements using supporting information about the surrounding environment and vegetation during post-processing helped discern the true built-environment from the surrounding land covers (18). Other notable advances include the use of high resolution orthographic imagery to detect subtle short-term built-environment change in China (36) and the use of Landsat imagery to create multi-temporal thematic representations of the built environment across the globe (37).Coinciding with advances in imagery, statistical methods, and computational resource availability, high-resolution datasets with global extent have been created either through combining multi-source optical imagery with contrast detection methods (38,39) or utilizing Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted:

show abstract

“…The population densities of various townships of India were calculated based on Indian population proportion (table 2) [7; 57]. The population density of Ballabgarh was 551 people per square kilometer [46].…”

Section: Methodsmentioning

confidence: 99%

“…Triangular (7,8,9) [28; 80] Proportion of people moving from ICU to critical illness (Ventilator assistance) 0.88 [28] Treatment duration in ventilator state (in days) Triangular (5,7,12) [80] Time between symptom arrival and admission (with no intervention) (in days) 5 [77] Time between symptom arrival and admission (with intervention) (in days) 3 [77] Proportion of people who die Number of deaths/ Number of infections (as per Indian statistics)…”

Section: Model Parametersmentioning

confidence: 99%

An Agent Based Model for assessing spread and health systems burden for COVID-19 using a synthetic population in Rangareddy district, Telangana state, India

Narassima

Jammy

Pant

et al. 2020

Preprint

View full text Add to dashboard Cite

Covid-19 disease, caused by SARS-CoV-2 virus, has infected over four million people globally. It has been declared as a global public health emergency by the World Health Organization. Researchers and governments are striving to do their best to fight against this pandemic. Several Mathematical models mostly based on compartmental modeling are being used for projections for Covid-19 in India. These projections are used for policy level decisions and public health prevention activities. Compartmental models are mostly used for Covid-19 projections. Unlike compartmental models, which consider population average, the Agent based models (ABM) models consider individual behavior in the models for projections. ABMs, yet rarely used for Covid-19, provide better insights into projections compared to compartmental models. We present an ABM approach with a small synthetic population of India, to examine the patterns and trends of the Covid-19 in terms of infected, admitted, critical cases requiring intensive care and/ or ventilator support, mortality and recovery. The parameters for the ABM model are defined and model run for a period of 365 days for three different non-pharmaceutical intervention (NPI) scenarios. AnyLogic platform was used for the ABM simulations. Results revealed that the peak values and slope of the curve declined as NPI became more stringent. The results could provide a platform for researchers and modelers to explore this approach for conducting ABM for Covid-19 projections with inclusion of interventions and health system preparedness.

show abstract

Urbanization in India: Population and Urban Classification Grids for 2011

Cited by 39 publications

References 22 publications

Missing millions: undercounting urbanization in India

Missing millions: undercounting urbanization in India

Modelling Built-Settlements between Remotely-Sensed Observations

An Agent Based Model for assessing spread and health systems burden for COVID-19 using a synthetic population in Rangareddy district, Telangana state, India

Contact Info

Product

Resources

About