JOURNAL ARTICLE

Analysing Spatio-temporal Dynamics of Urban Sprawl, Evolving Pattern of Urban Landscape and Driving Forces of Urban Growth: A Case of Varanasi Planning Region, India.

  • Published In: Journal of Asian & African Studies (Sage Publications, Ltd.), 2026, v. 61, n. 1. P. 213 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Dutta, Ratnadeep; Banerjee, Iman 3 of 3

Abstract

This article focuses on analyzing the spatio-temporal dynamics of urban sprawl, evolving urban landscape patterns, and the driving forces behind urban growth in the Varanasi Development Area (VDA), a rapidly expanding metropolitan region in northern India. Using geo-spatial methods including the Landscape Expansion Index (LEI), landscape metrics, and logistic regression modeling, the study examines urban growth modes—infilling, edge-expansion, and leapfrog development—over two decades (2001–2011 and 2011–2021). Findings reveal that edge-expansion and leapfrog development dominate Varanasi's urban growth, contributing to fragmented and dispersed built-up landscapes primarily outside statutory municipal boundaries, driven significantly by proximity to transportation infrastructure and existing urban centers. The research underscores challenges posed by unplanned peri-urban expansion and highlights the need for strategic land use regulation to promote sustainable urbanization in the region.

Additional Information

  • Source:Journal of Asian & African Studies (Sage Publications, Ltd.). 2026/02, Vol. 61, Issue 1, p213
  • Document Type:Article
  • Subject Area:Politics and Government
  • Publication Date:2026
  • ISSN:0021-9096
  • DOI:10.1177/00219096241287355
  • Accession Number:191102178
  • Copyright Statement:Copyright of Journal of Asian & African Studies (Sage Publications, Ltd.) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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