An analysis of urban growth trends in the post-economic reforms period in India

Chadchan, Jayprakash; Shankar, R.

doi:10.1016/j.ijsbe.2012.05.001

Cited by 127 publications

(44 citation statements)

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“…The spatial extent of the agro-climatic zones was digitized according to the National Portal on Mechanization and Technology [45]. The urbanization of India is also reflected in the rapid expansion of built-up areas [22,50,51] and low-density sprawl [48], together with a decline of other types of land cover, including open land, agriculture land, and bodies of water [52][53][54]. By capturing the distinct spectral profile of built-up areas, by means of earth observation, it is possible to map and to quantify the extent of urbanization and the pace of urban growth.…”

Section: Introductionmentioning

confidence: 99%

Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine

et al. 2016

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Urbanization often occurs in an unplanned and uneven manner, resulting in profound changes in patterns of land cover and land use. Understanding these changes is fundamental for devising environmentally responsible approaches to economic development in the rapidly urbanizing countries of the emerging world. One indicator of urbanization is built-up land cover that can be detected and quantified at scale using satellite imagery and cloud-based computational platforms. This process requires reliable and comprehensive ground-truth data for supervised classification and for validation of classification products. We present a new dataset for India, consisting of 21,030 polygons from across the country that were manually classified as "built-up" or "not built-up," which we use for supervised image classification and detection of urban areas. As a large and geographically diverse country that has been undergoing an urban transition, India represents an ideal context to develop and test approaches for the detection of features related to urbanization. We perform the analysis in Google Earth Engine (GEE) using three types of classifiers, based on imagery from Landsat 7 and Landsat 8 as inputs. The methodology produces high-quality maps of built-up areas across space and time. Although the dataset can facilitate supervised image classification in any platform, we highlight its potential use in GEE for temporal large-scale analysis of the urbanization process. Our methodology can easily be applied to other countries and regions.

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Section: Introductionmentioning

confidence: 99%

Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine

et al. 2016

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“…Large tracts of land that either lay fallow or were used for non-mechanized agriculture over several centuries are now bearing the brunt of heavy machinery for projects such as expansion of transportation networks 24 , formation of special economic zones (SEZs) 25 , quarrying, etc. The Ancient Monument and Archaeological Sites and Remains (AMASR) Act prohibits all construction within 100 m of protected monuments, but there have been efforts to grant exceptions for projects deemed to be sufficiently in the 'public interest' 26 .…”

Section: Impact Of Developmental Activities On Archaeological Landscapesmentioning

confidence: 99%

The Need for a National Archaeological Database

Gupta¹,

Das²,

Balan³

et al. 2017

Current Science

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India's economic development is evident in its industrial growth, extensive transportation network, and rapidly expanding cities, towns and villages. While this growth has numerous positive aspects, it also has the potential to cause irrevocable damage (directly or indirectly) to rich archaeological heritage of the country. The present study makes three contributions. First, it examines several archaeological sites where economic developmental activities have caused significant damage. Second, it demonstrates how the risk of further damage can be minimized using geospatial solutions to protect and manage such sites. Third, it conceptualizes a framework for incorporating spatial and non-spatial knowledge of archaeological sites into a National Archaeological Database. We propose that this national archive should be made publicly accessible under the Digital India programme, where it can assist decision makers (development authorities, state departments, etc.) and help citizens plan for future economic growth while preserving the fragile remnants of our past.

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“…In 1983 planning commission prepared four reports on housing and urban development through the task force (Mohan, 1992). Indian metropolises at this time suffer from basic services and shortages of infrastructure due to uncontrolled growth of population (Chadchana and Shankar, 2012). So NCU discarded the backward area development policy and identified 329 cities called GEMs (Generator of Economic Momentum) which were further divided into NPCs (National Priority Centres) and SPC (State Priority Centres) (Batra, 2009).…”

Section: Growing or Declining Metropolitan Populationsmentioning

confidence: 99%

Retrospect of post-colonial metropolitan planning in India: Critical appraisal

Karmakar¹

2015

J. Geogr. Reg. Plann.

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Metropolitan planning after the six decades has evolved so much. Planning issues are not deviated from that much. Early urban planning was guided by their master plans. Four major metropolises of India namely Kolkata, Mumbai, Delhi and Chennai have formulated their master plans between mid sixties to late seventies. The paper tried to find out the evolution of the urban planning ideas from 1960s onward with respect to four Indian metropolises, to examine their planning strategy and understand if it is enough to solve the problem of the metropolises and lastly how neoliberal paradigm has shaped their strategy. It is apparent that each city masters plan failed to solve the problem. Population growth, land use strategy and housing problem remain the major issue. In the Early 90's India steps in neoliberal approach and urban development organizations shifting their planning strategy from strategic to entrepreneurial deregulates the housing and real estate market.

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An analysis of urban growth trends in the post-economic reforms period in India

Cited by 127 publications

References 1 publication

Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine

Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine

The Need for a National Archaeological Database

Retrospect of post-colonial metropolitan planning in India: Critical appraisal

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