An Analytical Investigation of Urban Expansion Patterns in the Kolkata Metropolitan Development Authority (KMDA) Region Using Geoinformatics
DOI:
https://doi.org/10.46488/Keywords:
Peripheral urban development, geospatial study, spatial expansion patternsAbstract
The Kolkata Metropolitan Area (KMA) has experienced significant and rapid urban expansion on its outskirts during the last three decades marked by low-density, scattered development, often referred to as urban sprawl. This study thoroughly investigates the spatio-temporal patterns of urban expansion within the Kolkata Metropolitan Development Area (KMDA) from 1990 to 2020, utilizing advanced geoinformatics tools and comprehensive spatial metrics. The Landsat images for the years 1990, 2000, 2010, and 2020 were similarly procured in order to determine the degree and trends of urban sprawl. In the analysis of the directional expansion, with the application of the standard deviation ellipses and wedges, the predominant orientation of growth emerged – north-south – while by 2020, most of the growth had shifted to the southwestern region. Also, the rates of urban growth were measured through the Annual Urban Expansion Rate (AUER), the Urban Expansion Intensity Index (UEII), and the Landscape Expansion Index (LEI). The focus area experienced an urban land increase of 446.71 square kilometers, with the tallest Figure 3 (a) urban growth rate, reaching 5.42%, between 1990 and 2000, and its downward trend in the following decades. The main growth trend, according to LEI study, was edge expansion, which is a symptom of sprawl, whereas infilling and peripheral growth patterns supported urban diffusion and clustering. The Area-Weighted Mean Patch Fractal Dimension (AWMPFD) was used to classify urban spatial patterns into major core, secondary core, suburban fringe, and dispersed settlements. This analysis revealed a correlation between central aggregation and peripheral fragmentation. The association between AWMPFD values and urban spatial distribution was validated by multiple correspondence analysis (MCA), providing resource managers and urban planners with important information.