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Urban expansion has been significant and rapid over the last 30 years, with the outward growth of the Kolkata Metropolitan Area (KMA). Much of this growth has followed a lowdensity, disparate development pattern, commonly known as urban sprawl. This study aims to examine the spatial expansion pattern in the Kolkata Metropolitan Development Area (KMDA) between 1990 and 2020 through the application of advanced geoinformatics tools and spatial metrics. We analyzed Landsat Satellite images from 1990, 2000, 2010, and 2020 to evaluate urban areas, including their extent and trends. Patterns of directional expansion, assessed using standard deviation ellipses and wedge analysis, showed a clear north-to-south axis of growth in the study area. The expansion of urbanization by 2020 was therefore more concentrated in the south-western region. Urban growth rates were measured using the Annual Urban Expansion Rate (AUER), Urban Expansion Intensity Index (UEII), and Landscape Expansion Index (LEI). The urban land cover of the study area increased by 446.71 km' during the study period. The highest growth rate was from 1990 to 2000 (5.42%), followed by a decline in subsequent decades. LEI analysis revealed edge expansion as the prevalent growth type, which is a typical feature of urban sprawl. A mixture of infilling and peripheral growth patterns points to the processes of urban diffusion and clustering. Results for the Department of Labrador were obtained using the Area-Weighted Mean Patch Fractal Dimension (AWMPFD), which classified the urban spatial patterns into four types: major core, secondary core, suburban fringe, and dispersed settlements. Central aggregation and peripheral fragmentation are related straightforwardly. Multiple correspondence analysis (MCA) further confirmed this spatial distribution pattern, which has valuable implications for both resource managers and urban planners.
ABSTRACT
Urban expansion has been significant and rapid over the last 30 years, with the outward growth of the Kolkata Metropolitan Area (KMA). Much of this growth has followed a lowdensity, disparate development pattern, commonly known as urban sprawl. This study aims to examine the spatial expansion pattern in the Kolkata Metropolitan Development Area (KMDA) between 1990 and 2020 through the application of advanced geoinformatics tools and spatial metrics. We analyzed Landsat Satellite images from 1990, 2000, 2010, and 2020 to evaluate urban areas, including their extent and trends. Patterns of directional expansion, assessed using standard deviation ellipses and wedge analysis, showed a clear north-to-south axis of growth in the study area. The expansion of urbanization by 2020 was therefore more concentrated in the south-western region. Urban growth rates were measured using the Annual Urban Expansion Rate (AUER), Urban Expansion Intensity Index (UEII), and Landscape Expansion Index (LEI). The urban land cover of the study area increased by 446.71 km' during the study period. The highest growth rate was from 1990 to 2000 (5.42%), followed by a decline in subsequent decades. LEI analysis revealed edge expansion as the prevalent growth type, which is a typical feature of urban sprawl. A mixture of infilling and peripheral growth patterns points to the processes of urban diffusion and clustering. Results for the Department of Labrador were obtained using the Area-Weighted Mean Patch Fractal Dimension (AWMPFD), which classified the urban spatial patterns into four types: major core, secondary core, suburban fringe, and dispersed settlements. Central aggregation and peripheral fragmentation are related straightforwardly. Multiple correspondence analysis (MCA) further confirmed this spatial distribution pattern, which has valuable implications for both resource managers and urban planners.
Key Words:
Peripheral urban development Geospatial study Spatial expansion patterns Multiple correspondence analysis Area-weighted mean patch fractal dimension Urban metrics
(ProQuest: ... denotes formulae omitted.)
INTRODUCTION
Urban expansion is a spatiotemporal demographic phenomenon (Clark 1982). It describes how a hamlet or village's basic characteristics are changed to boost population density in an urban area. (Chettry & Manisha 2022). This process is complex and dynamic, involving changes in the functional and physical components of the built environment, which accelerate the conversion of rural areas into urbanized forms (Castle & Crooks 2006, Dahal et al. 2017). It is a multifaceted phenomenon rigorously analyzed by researchers in various fields, including urban planning, geography, sociology, economics, and environmental science. A causal relationship exists between urban expansion and urbanization. Urban growth leads to urbanization. Globally, urbanization has primarily resulted from the conversion of rural areas into urban zones or the influx of populations into pre-existing cities (Yildiz & Doker 2016, Lima & Romanelli 2019). Regions experiencing rapid urbanization, characterized by the transformation from rural to urban landscapes, undergo distinct socio-economic changes along with the spatial expansion of urban land, which significantly impacts resources and the environment (Carruthers & Ulfarsson 2003, Grimm et al. 2008). This process typically begins at a single point and radiates outward in various directions. Growth patterns differ from one urban area to another, influenced by intra-urban variations in local conditions such as land availability (Jiao 2015). Camagni et al. (2002) identified five types of urban growth: infilling, expansion, linear development, sprawl, and large-scale projects. Wilson etal. (2003) categorized urban growth into infill, expansion, isolated, linear, and clustered branches. Each major city worldwide exhibits a unique growth pattern and nature, with geometric attributes and spatial distributions varying across different growth types. Moreover, the directions and speeds of development may differ significantly. The spatiotemporal patterns of urban growth generally oscillate between the diffusion and coalescence phases. Urbanization is typically assumed to progress from the diffusion phase, characterized by an increasing number of impervious/built-up patches, to the coalescence phase, where these patches merge into continuous built-up areas (Dietzel et al. 2005a).
Urban growth has accelerated significantly over the past few decades owing to large-scale migration of populations to cities. By 2005, over 50% of the global population resided in urban areas (UNEP 2005). At the current rate of growth, it is projected that by 2030, more than 60% of the global population will live in urban areas (Moeller & Blaschke 2006, Odindi & Mhangara 2012). Rapid urbanization and city expansion are among the most prominent global trends of this century (World Economic Forum 2015, 2016, 2017). In contrast to 1950, when only 30% of the global population lived in urban areas, cities now accommodate over half of the world's population, with 54% residing in urban areas by 2014 (United Nations 2015). The global trend of urbanization has primarily resulted from the conversion of rural areas into urban zones and the influx of populations into existing cities (Yildiz & Doker 2016, Lima & Romanelli 2019). According to the United Nations, nearly half of the world's population lives in or near urban areas. The rate of urban growth is notably higher in developing countries than in developed countries, driven largely by extensive infrastructure development and population migration to urban centers (Sui & Zeng 2000). The expansion of urban areas is outpacing urban population growth (Tewolde & Cabral 2011). In many regions, urbanization is being accelerated by the dynamics of the global economy, leading to significant transformations in the planet's physical landscape (Soja 2013, Abbas 2016). Metropolitan cities in developing countries worldwide have experienced rapid growth in response to population surges and economic expansion (Al-Sharif & Pradhan 2015, Metzger et al. 2016).
Rapid urbanization is assessed through the extent of suburban expansion and urban sprawl, as outlined by Harris & Ventura (1995) and Sajjad (2014). Detecting and quantifying urban expansion patterns and processes, as well as focusing on the spatial determinants of urban growth, are standard practices in urban sprawl studies (Forman 1995, Wasserman 2000, Ramachandra et al. 2012, Mithun et al. 2016, Mukherjee 2012). Wilson & Chakraborty (2013) suggested that examining the physical attributes of urban growth as a pattern of urban development is a prevalent method for characterizing urban sprawl. Besides, several studies (Torrens & Alberti 2000, Rahman et al. 2011, Pandey et al. 2013, Li et al. 2013, Luo & Wei 2009, Schnaiberg etal. 2002, Yeh & Xia 2001, Dewan & Yamaguchi 2009, Jenerette et al. 2007, Pijanowski et al. 2010, Tian et al. 2012, Carrion Flores & Irwin 2004, Gustafson et al. 2005, Jiang etal. 2013, Rui & Ban 2011, Sudhira et al. 2004, Seto & Kaufmann 2003) attempted to determine urban growth based on determinants such as changes in the urban built-up area, proximity indicators, topographical factors, neighborhood variables, and variables among socio-economic attributes.
Urbanization in India has experienced significant growth since the post-independence period, particularly over the past three decades (Bhagat & Mohanty 2009). The urban population rose from 10.84% in 1901 to 17.29% in 1951, reaching approximately 31.16% in 2011 (MHUPA 2016). With 377.16 million urban inhabitants, India is the second most urbanized country globally, accounting for 11% of the world's urban population. It is projected to increase by 13% by 2030 (Lauther 2011) and 50% by 2050 (Das et al. 2021). Recent Census of India data indicate that, despite the relatively slow pace of urbanization, the absolute population increase in urban areas has surpassed the total rural population in the country (Census of India 1991,2011). However, urbanization in the cities of developing countries continues without adequate planning or infrastructure expansion. Consequently, unlike in developed nations, rapid urbanization in developing countries (Montgomery 2008) often results in unplanned and disorderly urban expansion (Cohen 2006, Grimm et al. 2008). A notable aspect of India's urbanization is the rapid growth of metropolitan suburbs, contributing to spatial transformation (UNFPA 2007, The World Bank 2013). Since the last quarter of the twentieth century, India has predominantly experienced outward expansion in most large megacities, characterized by sprawl, where the peripheries have increasingly absorbed small towns and villages rather than accommodating rural migrants within the city core.
India has experienced rapid urbanization over the past three decades (Bhagat & Mohanty 2009). Over time, the number of urban agglomerations (UA) has increased significantly each decade, as seen in the expansion of cities such as Mumbai, Delhi, Kolkata, Bangalore, Chennai, and Hyderabad. By 2021, India was anticipated to have the largest concentration of urban agglomerations globally (Chakrabarti 2017, Taubenbóck et al. 2009). The Kolkata Urban Agglomeration is the tenth largest in the world and the third largest in India, particularly in Eastern India (UN 2011). Over the past few decades, KUA has consistently expanded, with the city rapidly extending into peripheral areas (Bhatta 2009). Numerous studies have explored this phenomenon (Bhatta 2009, Ramachandra et al. 2014, Mondal et al. 2015, 2016, Sahana et al. 2018, Chakrabarty et al. 2021, Ray et al. 2023) to analyse urban growth as well as land transformation along with the effects on peripheral land use and land cover, especially in urban areas within the Kolkata Municipal Corporation and its adjacent regions. However, there is a dearth of information about population growth as the primary driving parameter of urbanization. Population growth drives urban expansion through diffusion and coalescence (Dietzel et al. 2005b), often at the expense of existing land use and land cover. In addition, the increasing gravity potential of the statutory towns (ST) with increasing population growth causes the emergence and growth of census towns (CT) and socio-economic transition in terms of occupational migration. Therefore, considering such gaps and the contextual background of the growth dynamics of urban agglomeration within the KMDA, this study aims to provide a parametric overview of the spatiotemporal urban growth within the KMDA region.
The Study Area
The Kolkata Urban Agglomeration (UA) encompasses the administrative areas governed by the Kolkata Metropolitan Development Authority (KMDA), which includes three municipal corporations (Howrah, Kolkata, and Chandan Nagar), 38 municipalities, 77 non-municipal urban towns, 16 outgrowths, and 445 rural villages. KMDA operates as a statutory authority under the Urban Development Department of the Government of West Bengal, India. As illustrated in Fig. 1, the KMDA's jurisdiction covers six districts: Kolkata, Howrah, Hooghly, Nadia, North 24 Parganas and South 24 Parganas. Kolkata UA is situated between latitudes 22°00' 19" № and 23 00' 00" №, and longitudes 88°00' 04" E and 88°00'33" E, spanning an area of 1851 km?.
A concentrated population and dense settlements on both sides of the Hooghly River mark the Kolkata Urban Agglomeration. According to the 2011 Census, the total population of the Kolkata Metropolitan Area (KMA) was 14.72 million, with a population density of 7,950 persons per square kilometer. The area's annual population growth rate was 1.8% by 2011, with projections suggesting an increase to 20 million by 2021 and 21.1 million by 2025 (Census of India 2011, KMDA 2011). Kolkata ranks among the 30 largest megacities worldwide, with a population of 10 million (UN 2007). The region has undergone substantial migration, resulting in numerous challenges, including land scarcity, overpopulation, and heightened strain on available resources. These problems have profoundly influenced the anthropogenic landscape, leading to substantial alterations in land use and land cover (LULC), particularly in urban centers such as Kolkata. The slums of urban agglomerations display a heterogeneous land-use pattern, incorporating residential, commercial, and industrial functions (Roy et al. 2014, Sugiyama 2008, Bhatta 2009). The eastern region of the KMA, encompassing locales such as Bidhan Nagar, Rajarhat, Maheshtala, and Sonarpur, features back swamps and marshy terrains that are progressively being invaded by local inhabitants for residential construction, frequently lacking adequate planning (Ghosh & Sen 1987, Dasgupta et al. 2013).
MATERIALS AND METHODS
Data Sources
The study used Landsat digital data obtained from the TM, ETM+, and OLI/TIRS sensors for the years 1990, 2000, 2010, and 2020, sourced from the USGS Earth Explorer website (http://earthexplorer.usgs.gov, accessed on 5 December 2022). The selection of Landsat images was driven by their availability and medium-to-high spatial resolution, making them suitable for this study. In contemporary research involving remote sensing and GIS, Landsat data have been widely used to assess urbanization processes. Detailed information on the data is presented in Table 1. These three sets of Landsat digital data were referenced to the UTM map projection (Zone 45 N) using the WGS84 geodetic datum and were Level-1 Terrain corrected (L1T).
In addition, administrative maps (https://kmda.wb.gov. in/) and population census data (https://censusindia.gov.in) for the years 1991, 2001, and 2011 were used to enumerate the spatiotemporal nature of urban physical growth and population growth dynamics. In addition, some literary sources have been used as an ancillary database. However, all the extracted data and information were analyzed to significantly fulfill the goal of the present study.
Although this study concerns only the urban expansion characteristics of the Kolkata Metropolitan Development Area (KMDA), it is worthwhile to examine these findings in a wider comparative context. The same narrative of belt and sprawl emerging at the peripheries has also been observed in other metropolitan regions of India, such as Delhi, Mumbai, and Hyderabad, providing for dynamics of urbanization shifting to the preferred edge due to land availability and corridor-induced growth. For example, research in Hyderabad and Ahmedabad suggests a similar pattern, but infill development seems to have been more prominent in cities with strict land-use regulations and redevelopment incentives. In KMDA's urban growth, therefore, there is a striking parallel to fast-growing cities in the Global South, such as Jakarta, Indonesia, and Lagos, Nigeria, where, in much the same way, unregulated peri-urban development is fueled by comparable forces: in-migration, investment in infrastructure, and informal land markets. However, unlike cities that have committed themselves to compact city policies (e.g., Barcelona or Portland), growth in infill is relatively weak in the KMDA, indicating the need to include more intentional urban containment orientations. In this study, for the thematic depiction as well as quantitative analysis of the spatiotemporal pattern and nature of urban growth, multi-temporal satellite images were used as the primary source of data generation.
Methodology
Mapping of urban patches and their growth dynamics: Following the modified methodology proposed by He et al. (2010) after Zha et al. (2003), the urban patches of the study area were extracted using an optimum threshold from the digital index called the built-up index (BUI). This index comprises three mathematical computations:
i The NDVI was calculated using the following equation (Rouse et al. 1974):
...
ii. Based on the high spectral response of the built-up areas to the wavelength range of 1.55-1.75 um (SWIR) and low spectral response to the wavelength range of 0.76-0.90 um (NIR), the normalized built-up index (NDBI) was calculated as follows:
...
iii. Finally, the Built-up Index (BUI) was computed by subtracting NDVI from NDBI. A higher BUI value for a pixel indicates a greater likelihood of representing a built-up area.
...
To calculate the BUI from the Landsat 8/OLI sensor, a slight modification in the formulation was applied according to Bhatti et al. (2014). In the Landsat 8/OLI sensor, bands 6 and 7 are highly sensitive to urban built-up areas and are highly correlated. In addition, in the Landsat 8/TIRS sensor, two thermal bands (bands 10 and 11) are also sensitive to urban built-up areas and are correlated. Therefore, for the optimum utilization of those bands, Principal Component Analysis (PCA) was applied using band pairs 6,7 and 10,11. Finally, the first PC from each pair was considered for its high variance, and the sum of those first PCs was used as the replacement of band 5 (for TM and ETM+) in the original NDBI of Zha et al. (2003).
...
Finally, the Built-up Index (BUlo,1) was calculated by subtracting NDVI; y from NDBIo,,, where a higher pixel value suggested a greater likelihood of representing a builtup area.
...
After calculating the raster-based built-up information in a temporal manner, using an optimum threshold, urban built-up patches were extracted, and by applying the XOR operation, the urban patch growth dynamics were estimated.
Depiction of Typical Urban Expansion
Based on earlier studies (Camagni et al. 2002, Wilson et al. 2003, Forman 1995), there are three different patterns of urban growth. The patterns comprise infilling, edge expansion, and outliers. Xu et al. (2007) asserted that urban expansion can be evaluated as a result of the amalgamation of these three categories. Liu et al. (2010) employed the Landscape Expansion Index (LEI) in this study to determine the prevailing urban expansion trend in the KMDA region. The calculation for the LEI may be as follows:
...
Where, A,, represents the area where the buffer zone of a new urban patch intersects with an existing urban patch, Anu refers to the area where the buffer zone overlaps with non-urban patches.
The patch is considered an outlying expansion if LEI = 0, which is a range of 0-100. The patch can be considered edge-expansion if 0 < LEI < 50. Finally, the patch can be categorized as an infilling expansion if LEI >= 50.
The analysis of the Landscape Expansion Index (LEI) revealed a dominance of edge expansion behind urban growth in the KMDA, in addition to supporting infill and outlying growth. This finding is consistent with other findings on urban sprawl in Kolkata. Chakraborty et al. (2017) and Das and Das (2020), where peri-urban expansion is similar and edge-dominant, and unregulated land conversion and the role of transportation corridors in patterning growth were also highlighted.
In other Indian metropolitan areas, such as Delhi and Bangalore, studies by Sudhira et al. (2004) and Jat et al. The early phases are characterized by edge expansion with infilling (2008), indicating increasing infilling as the urban core develops. In Kolkata, the transition from edge to infill growth appears to be taking place at a much slower pace, which might be attributed to the fragmented nature of governance, coupled with the belated provision of infrastructure in peripheral zones. These comparative perspectives are useful as they strengthen the understanding that urban sprawl in Kolkata is structurally similar to other metropolises, yet is shaped by the unique socio-political and infrastructural conditions that also shape the spatial patterns of urban growth (Sharma & Awasthi 2019).
A hybrid approach of spatial metrics and geoinformatics techniques was applied to identify the urban growth metrics in the Kolkata Metropolitan Development Area (KMDA) during 1990-2020. Landsat satellite images of 1990, 2000, 2010, and 2020 were processed to map the urban land cover subtypes using classification methods.
The spatial indices employed to quantify urban growth rate and intensity are as follows:
Urban Growth Rate (absolute): The average growth rate of the built-up area from one year to the next (e.g., for 10 years, calculate the mean), expressing how fast the built-up area is transformed from one year to another.
Urban Expansion Intensity Index (ОЕП): Measures the spatial intensity of urbanization within administrative units, facilitating the identification of urbanization hotspots.
To analyze growth trends: Landscape Expansion Index (LEI): This parameter characterizes urban growth categories, such as edge expansion, infilling, or outlying, helping to understand the dominant mode of sprawl.
To describe the spatial shape and complexity of urban forms: Area-Weighted Mean Patch Fractal Dimension (AWMPFD) (m2): Analyzes the geometric complexity and compactness of urban patches, determining the types of major core (or compact), secondary core, suburban fringe, and dispersed settlements.
Finally, to explore the relationships between spatial forms and metrics, Multiple Correspondence Analysis (MCA) was used, which is a statistical procedure for building and validating associations between categorical spatial patterns and indices for the solid interpretation of urban spatial dynamics.
This index would collectively help to set a framework for understanding the scale, type, and pattern of change over time.
In the 1990s and the early 2000s, the rearward expansion of major transport corridors, including National Highway 6 (Mumbai-Kolkata Highway) and Diamond Harbour Road, aided in urban spread to the southwest. The establishment of industrial hubs, such as the Dankuni Industrial Belt and Kalyani Expressway Industrial Corridor, also attracted residential and commercial development in those directions.
Changes in policy also had a significant impact. Targeted infrastructure improvements-especially water supply, drainage, and transport-were excavated along the peri-urban fringe under the auspices of the KMDA's Metropolitan Development Plan (MDP) and through investments through the Jawaharlal Nehru National Urban Renewal Mission (JNNURM), thus indirectly shaping urban growth. Additionally, these fringes have become attractive to lower- and middle-income populations with the promotion of low-cost housing schemes in southern and south-western zones. Economic variables, such as the affordability of land and real estate speculation in peripheral regions, also encouraged outward expansion, particularly in areas where laws governing land use were abrogated or otherwise weakly enforced. The contextual analysis sheds light on the directional bias in urban growth revealed by spatial analysis.
Study of spatial and directional dynamics of urban patches: То understand the spatial distribution and prevailing orientation of urban patches over time, this study uses the Standard Deviational Ellipse (SDE) (Qiao et al. 2018). The center of SDE indicates the centroid of the urban land area. The shifting locations of the temporal SDE centers show how the spatial orientation and layout of the urban area are evolving. The standard deviations of the longitudinal and transverse axes indicate the density concentration of urban land. According to Zhong etal. (2019), the azimuth angle of the SDE indicates the main direction of the distribution trend. The angle of rotation in the clockwise direction from the north to the SDE's long axis. A greater difference in magnitude between the longand short-axis values results in items, such as urban land, having a more noticeable directionality. A range of standard deviations includes the centroids of approximately 68% of the total elements when the elements exhibit a definite spatial distribution, with the elements concentrated at the center and progressively decreasing toward the periphery. Two standard deviation intervals contain approximately 95% of the data points, whereas three standard deviation intervals include nearly 99% of the total data points.
The coordinates of the feature set are (x1, y1), (x2, y2) .. ., (xn,yn), then the direction angle © of the SDE is calculated as follows (Zhao et al. 2014):
...
The standard deviations of the long and short axes are 6, and 8y respectively, ANd are calculated as:
...
Where, X; and ÿ; are the differences between the mean centre coordinates x, y, and the feature coordinates x;, Vi, respectively.
In this study, to estimate the temporal dynamics of the spatial distribution of the urban land area, the concept of the difference of the main trend direction (DMTD) of an ellipse with regard to its long axis was implemented (Tang et al. 2021). The DMTD can be calculated as the difference between the azimuth angles of two consecutive temporal SDE (i.e., 6, of the beginning year and 6, of the ending year). However, if the difference between these two azimuth angles was greater than 90°, the difference was subtracted from 180°.
In addition, another quantitative measure, such as the spatial difference index (SDI), has been implemented to enumerate the degree of spatial difference between the temporal distributions of urban land areas (Zhao 2014). The SDI value ranges from 0 to 1. A higher SDI indicates greater spatial variation. The SDI is defined as follows: ...sDI=1Where
...
Where SDEb and SDEe are the SDE at the beginning and end of the period, respectively.
In addition to the application of SDE, another approach has been implemented to enumerate the directional dynamics of the spatiotemporal arrangement of the urban land surface. Considering the SDE approach as the global assessment of the directional trend of urban land surface, for the local-level assessment of the same, the area under KMDA was divided into eight segments (North, North-East, East, South-East, South, South-West, West, and North-West) in a wedge-like manner based on the mean center of the KMDA boundary. Finally, the areas of urban land under each wedge were calculated temporally.
Study of urban metrics: Spatial matrices are commonly employed to assess urban expansion. Depending on data availability, different types of measures were used in this study, such as the Urbanization Index (UI), Urban Expansion Index (UEI), Annual Urban Expansion Rate (AUER), Urban Expansion Intensity Index (UEII), and Urban Expansion Differentiation Index (UEDI).
Urban Expansion Index (UEI):
The spread of urban areas can be explained by urban
expansion. The urban expansion index illustrates the rapidity of urban landscape growth and is calculated using Equation 10.
...
Where 'i' is the spatial unit. BY is the urban patch area at the beginning of the year, and EY is the urban patch area at the end of the year.
Urban Expansion Intensity Index (UEII):
The Urban Expansion Intensity Index (UEII), which emphasizes the ratio of a spatial unit's urban growth to the entire study area and duration, measures urban expansion variability (Hu et al. 2007). The UEII result shows the extent to which a spatial unit has urbanized relative to the total research region.
...
Where 'i' is the spatial unit. BY is the urban patch area at the beginning of the year, and EY is the urban patch area at the end of the year.
Annual Urban Expansion Rate (AUER):
Regardless of the size of the geographical unit, the Annual Urban Expansion speed (AUER) calculates the average annual speed of land growth over two time intervals (Acheampong et al. 2017). Without any upper or lower bounds, the AUER result shows the amount of fluctuation in the built-up land area over time. This was calculated as follows:
...
Where 'i' is the spatial unit. BY is the urban patch area at the beginning of the year, and EY is the urban patch area at the end of the year.
Analysis of urban landscape fragmentation: Urban landscape fragmentation is the outcome of contiguous urban built-up land cover eventually breaking up into different regions owing to edge effects (Rahman et al. 2016). Urban landscape fragmentation was analyzed using a pattern metric called the area-weighted mean patch fractal dimension (AWMPFD) index. This is comparable to the Mean Patch Fractal Dimension (MPFD) index. The use of area as a weighting component for each patch was the only difference. Fragmentation will be less the higher the AWMPFD value and vice versa. The value is in the range of 1 and 2. It can be calculated as follows:
...
Multiple correspondence analysis (MCA): MCA is a multivariate graphical method for analyzing categorical data and an exploratory data analysis methodology (Benzecri 1979, Sourial et al. 2010). The combined scaling of the row and column variables to reveal information on the relationships between them row and column variables is a crucial component of this study. Both gualitative and guantitative data were subjected to correspondence analysis. The last stage of the study was rescaling the characteristic vectors into ideal scores. The relative relevance of the elements was assessed by normalizing these ideal values. According to Weller et al. (1990), correspondence analysis can be used to determine the best variable order for a particular collection of attributes. The maximum value between each row and column element can be usedto address the homogeneity between the row and column elements, which defines the relationship between the elements. The current study computed the correspondence between the typical zonal distribution of urban spatial patterns and the classified values of the AWMPFD index.
RESULTS AND DISCUSSION
Temporal Urban Expansion and Directional Dynamics
Urban spatial expansion is a continuous process that occurs in stages. The assessment and analysis of the spatiotemporal expansion of urban patches play a significant role in the study of urban growth. In this study, NDBI-derived urban patches were mapped (Fig. 2) and analyzed for the years 1990, 2000, 2010, and 2020. Within this 30-year span, the urban patches have increased by 446.71 sq. km at the rate of 4.14%. The maximum areal growth was observed between 1990 and 2000. From 1990 to 2000, the area increased by 195.30 sq.km at a rate of 5.42%. However, in the succeeding 20 years, the rate of growth has certainly slowed down to 2.30% in the span of 2000 to 2010 and 1.80% in the span of 2010 to 2020. In 2010, the area increased by 127.83 km? from 2000, and in 2020, the area increased by 123.57 km? from 2010 (Table 2).
Over a span of 30 years, the KMDA region saw substantial changes in its urban area, with an Annual Urban Expansion Rate (AUER) of 2.73% and a growth rate of 4.14%. Between 1989 and 2019, the urban area expanded by 446.71 sq. km, with an Urban Expansion Intensity Index (UEII) of 0.86 (Table 3). Such a UEII value indicates an apparently faster areal growth (Ren et al. 2013).
Although the KMDA region experienced a general increase in urban area growth over the 30-year span, there were varying growth patterns in a decadal manner. The urban built-up areas of the KMDA experienced significantly faster growth (UEN of 1.12) between 1990 and 2000. Within this span, the urban area expanded by 195.30 sq.km with the AUER and growth rate of 4.43% and 5.43%, respectively. In the successive spans (from 2000 to 2010 and 2010 to 2020), the urban area expanded with a decreasing growth rate as well as AUER. From 2000 to 2010 and 2010 to 2020, the urban area expanded by 127.83 sq.km and 123.57 sq.km with growth rates of 2.30% and 1.81% and AUER of 2.09% and 1.68%, respectively. In addition, a slower rate of expansion within both spans was evident, with the UEII values being 0.73 and 0.71, respectively (Table 3).
The geometric characteristics of the Standard Deviation Ellipse (SDE) (Table 4). The four types describe the distribution pattern of urban patches, their spreading ranges and directional trends, and the shifting of the gravity centers on a temporal basis (Fig. 4).
Within the overall span of the study, the gravity centers of the urban built-up area shifted by 5.83 km southward from their base position in 1990 (Table 5). Such a shift could truly hypothesize the spatial expansion of the urban built-up area southward. The maximum shifting was observed from 2000 to 2010 (2.41 km).
The varying sizes of the temporal Standard Deviation Ellipses (SDEs) and the lengths of their long and short axes for the urban built-up area in the KMDA illustrate spatial dispersion in terms of the areal dynamics of geographical features such as urban built-up areas. Table 4 shows an evident increasing trend in the areas of temporal SDEs. The most significant areal expansion within the SDE occurred between 1990 and 2000, with an increase of 85.53 sq. km. km. From 2000 to 2010, the area expanded by 40.11 sq. km, and from 2010 to 2020, by 34.47 sq. km. This growth in the SDE area suggests that urban built-up patches outside the standard deviation ellipse expanded more rapidly than those within it. Furthermore, the orientation and distances of the long and short axes depict the directional trend and magnitude of the expansion of the urbanized area. A larger difference between the long and short axes indicates a more obvious directional expansion of the urbanized areas. In addition, such directional expansion could also be analyzed by the nature of flattening, eccentricity, and compactness. The directional orientations of temporal urban built-up areas and the representative long axis were similar to the NorthEast-South-West direction throughout the study period (Fig. 3) having a mere rotational angular variation, a decrease in flattening and eccentricity, and a corresponding increase in the distance of the short axis (Table 4) from 1990 to 2020 indicates the gradual growth of new urban built-up patches in the east-west direction. The increasing aspect ratio or lowering of the compactness of SDEs could be an evident of such a scenario. From the geometric aspects of the temporal SDEs, it can be seen that the area under the KMDA has experienced the maximum outgrowth in the East-West direction fromits clustered center within the span of 2010 to 2020.
However, despite the increasing area of SDEs of urban built-up patches temporally, decreasing values of the spatial difference index (SDI) indicate the consistency in the spatial distribution of urban built-up patches and the higher degree of spatial concentration of the same inside the SDE rather than outside (Table 5). The SDI is a function of the distribution discrepancies between the spatial objects within the SDEs, and the smaller the intersection areas of the SDEs, the larger the SDI, and vice versa. In the present study, within the span of 2000 to 2020, an almost similar trend in the spatial growth rate of urban built-up patches was observed (Table 3), and the resultant SDE intersection areas enumerated the consistency in the spatial distribution ofurban built-up patches with lesser SDI values ranging between 0.14 and 0.11 (Table 5). The only exception is seen for the span of 1990 to 2000, within which the maximum urban built-up growth rate is 31% (Table 2) is the enumerator of urban growth inconsistency and hence a relatively higher SDI, that is 0.20 (Table 5).
To assess urban expansion, the application of the Standard Distance Ellipse (SDE) provides an overall description of urban growth, considering both statistical dispersion and the direction of growth based on the angular orientation of the major axis. However, detailed insights into the directional growth dynamics of urban patches over time are better captured by transferring the area information of urban patches (Table 6) to directional wedges (Fig. 4). Table 5 shows how the urbanization of the region has changed over the course of four decades (1990, 2000, 2010, and 2020), with the directions of urban expansion being north (N), northeast (NE), east (E), southeast (SE), south (S), southwest (SW), west (W), and northwest (NW). This dataset reflects the dynamics of urban sprawl and the changing landscape over time, showcasing how urban areas have grown in different parts of the region (Abdulhameed et al. 2025a, 2025b).
From 1990 to 2020, the data revealed a pronounced trend of urban expansion across all regions, with significant growth in each direction. The North (N) region consistently maintained the largest urban area throughout the three decades. Beginning with an urban area of 111.16 square kilometers in 1990, the North's urban footprint expanded dramatically to 169.70 square kilometers by 2000, then to 197.06 square kilometers in 2010, and finally reaching 222.20 square kilometers by 2020. This represents a more than twofold increase over the 30-year span, underscoring the North's role as the primary hub of urban growth (Table 6).
This trend of urban expansion is not confined to the North but is mirrored in other regions, albeit to varying degrees. For instance, the Southeast (SE) region saw its urban area increase from 31.49 square kilometers in 1990 to 52.77 square kilometers by 2000, a growth that continued to 69.89 square kilometers in 2010 and reached 88.33 square kilometers by 2020. Similarly, the Southwest (SW) region experienced substantial growth, with its urban area expanding from 48.46 km? in 1990 to 76.27 km? in 2000, further increasing to 96.71 km? in 2010, and finally to 126.02 km? by 2020. These expansions reflect broader patterns of urban sprawl, as the growth of cities and towns has increasingly pushed outward into the surrounding areas (Table 6).
This outward expansion is indicative of several underlying factors. The consistent and significant growth in these regions suggests a combination of population increase, economic development, and possibly demand for more residential, commercial, and industrial spaces as cities evolve. The data indicate the differing intensities of urban expansion among regions, which are influenced by factors such as land availability, infrastructural development, and regional planning strategies. The significant growth in the Southeast and Southwest regions can be ascribed to advantageous conditions for urban development, including relatively undeveloped land, proximity to economic centers, and particular municipal policies promoting expansion in these areas.
Furthermore, the data indicate that urban sprawl has not been uniform: instead, it has exhibited diverse patterns based on location. The North, Southeast, and Southwest regions, which experienced substantial urban area expansion, presumably signify crucial growth corridors where infrastructure and economic activity have catalyzed urbanization. Conversely, regions such as the West (W), which commenced with a modest urban area of 7.02 square kilometers in 1990 and expanded to 35.27 square kilometers by 2020, also underwent considerable relative growth, albeit the absolute extent of urbanization was less than that of the North, Southeast, and Southwest regions. This underscores the disparate roles of various locations in the overarching trend of urban sprawl, with certain areas emerging as significant growth hubs, while others have evolved more incrementally.
Overall, Table 6 shows a significant and widespread expansion of urban areas in all directions from 1990 to 2020, with the North, Southeast, and Southwest regions standing out as major areas of growth. These data underscore the dynamic nature of urbanization, where economic, demographic, and policy factors converge to drive the expansion of cities and towns into new areas, reshaping the landscape over time. The growth observed in these regions reflects the broader trends of urban sprawl, where the increasing demand for space and resources has led to the continuous outward expansion of urban areas, influencing the development patterns and spatial organization of the regions over the past three decades.
In addition, key metrics such as the Growth Rate (GR), urban expansion Intensity Index (UEI), and Annual Urban Expansion Rate (AUER) provide a comprehensive overview of urban expansion (Table 3). The growth rate (GR) data reveal interesting patterns over the three decades. The West (W) and Northwest (NW) regions recorded the highest growth rates in the first decade (1990-2000), at 13.88 and 10.83, respectively. The elevated growth rates indicate a phase of rapid urbanization in these areas during the 1990s. Nevertheless, the ensuing decades exhibited a significant decline in growth rates throughout all regions. The growth rate in the North (N) region decreased from 5.27 in the first decade to 1.28 in the last decade (2010-2020). Urban areas have continued to expand, although at a significantly slower rate over time, as evidenced by the declining growth rates in all directions. Several factors, such as restrictions on available land, shifts in the economy, or modifications to urban planning laws, could lead to this.
The Urban Expansion Intensity Index (UEII) also sheds light on the processes of urban growth. The Northwest (NW) region saw a drop to 0.52 between 2010 and 2020 from a high UEII of 0.94 between 1990 and 2000. The UEII decreased in the Southeast (SE) region from 0.84 in the first decade to 0.68 in the most recent one. The decrease in the UEII indicates a slowdown in the pace of urban growth in these locations, even as urban areas are still expanding. In contrast, over the course of three decades, the UEII values in the Southern (S) region showed a rather constant pattern of urban expansion, suggesting that this pattern may be more durable.
This slowdown in urban development is also indicated by the Annual Urban Expansion Rate (AUER). In the first decade, the West (W) area had the highest AUER of 9.10: however, in the second decade, it significantly decreased to 3.68. Similarly, throughout the same period, the AUER in the North (№) region decreased from 4.32 to 1.21. The steady decline in the AUER at multiple sites suggests that while cities are still growing, the rate of new construction is slowing down. This could indicate that many regions are approaching urban saturation, which means that less space is available for further development.
The data demonstrate a distinct trend of substantial urban expansion in all directions over the past three decades, but with a marked deceleration in the rate of increase in the later years. The elevated growth rates and UEII values in the preceding decades indicate a phase of fast urbanization, presumably propelled by economic expansion, population growth, and perhaps more permissive urban planning rules. Nonetheless, the diminishing growth rates, UEII, and AUER in recent decades indicate that these places are transitioning into a more advanced stage of urban development, wherein the emphasis may be shifting towards the management of existing urban areas rather than outward expansion. This could also reflect changes in policy towards more sustainable urban development, with an emphasis on improving infrastructure and services within existing urban areas rather than promoting unchecked sprawl.
Among the three temporal spans-1990 to 2000, 2000 to 2010, and 2010 to 2020-the first decade saw the most rapid urban growth in the KMDA, with an increase of 196.56 sq. km, accounting for 35.22% of the total urban area by the year 2000. In comparison, the newly developed urban patches in the subsequent periods, 2000-2010 and 2010-2020, constituted 18.71% and 15.34% of the total area, respectively. Based on these expansion figures and the concept of the Landscape Expansion Index (LEI), edge expansion was predominant, followed by outlying and infilling growth (Fig. 5). The increasing trend of edgeexpanding urban growth in the study area typically indicates the spread of sprawl, whereas infilling and outlying growth types reflect the agglomeration and dispersion of urban patches, respectively.
In the first temporal period, an area of 148.11 sq. km. was edge-expanding growth, which reduced to 107.19 sq. km. and then again increased to 113.28 sq. km. in the next temporal periods, respectively. From the first to the second temporal period, there was the maximum decline in edge expansion in the northern sector (26.97 sq. km), followed by northeast (6.57 sq. km), south-west (3.67 sq. km), south (3.56 sq. km) and north-west (2.92 sq. km) sectors. However, in the second temporal period, there was a considerable increase in edge expansion in the east (5.11 sq. km), south-east (2.59 sq. km) and western sector (0.46 sq. km) as well. However, in the third temporal span, there was an overall increase in edge expansion in every direction except the southern and eastern sectors. There was a decrease in edge expansion by 11.06 and 2.75 sq. km, respectively (Table 7).
Similar to edge expansion, urban growth in the KMDA has been significantly attributed to the outlying expansion. Although there was an overall decline from the first to the second temporal span in areal growth in every direction except in the northeast and eastern sectors, the third temporal span experienced an overall increase in the outlying growth area, except in the northeast, west, and northwest sectors. In this span, the maximum areal expansion as outlying was in the south-western sector, followed by the northern and south-eastern sector (9.62 sq. km, 7.53 sq. km and 7.06 sq. km respectively) (Table 7). A very small portion of the urban area has been accounted for in the infilling type of urban growth. In each of the three temporal periods, there was an overall increasing trend in infilling growth. Specifically, the northern sector experienced the maximum amount of infilling growth, as in the first temporal span, where the infill growth was 0.54 sq. km. The second and third spans were 3.56 and 4.43 sq. km, respectively (Table 7).
Urban Landscape Fragmentation
The spatial arrangement of urban patches in terms of the temporal dynamics of urban built-up land can be best explained by analyzing the fragmentation of the respective landscape through the computation of the area-weighted mean patch fractal dimension (AWMPFD). The value of AWMPFD tends to approach 1 for shapes with simple perimeters, such as circles or squares, and approaches 2 for shapes with complex, plane-filling perimeters, while still adhering to the condition 1 = AWMPFD < 2. AWMPFD is a metric that quantifies a distinct aspect of the structure of urban land use. This metric specifically measures the level of fragmentation within each built-up patch rather than providing an assessment of the overall homogeneity of urban patches, which is the focus of the contagion metric (Herold et al. 2002).
Generally, the outward growth of an urban built-up landscape from the center over time tends to be fragmented, whereas this tends to be clustered towards the center. This overall pattern is not an anomaly in the built-up landscape dynamics of the KMDA, except in the case of a steady-state condition. This can be inferred from the comparative analysis between the distribution of the AWMPFD value and the areal growth of urban built-up land in a temporal manner. In this study, a comprehensive analysis of the metrics for each square block (measuring 2X2 km2) polygon was conducted, encompassing the entirety of the study area.
The temporal AWMPFD index, in its thematic representation, portrays the dispersed growth pattern of urban built-up patches, which exhibit higher values as they extend towards the periphery of the KMDA boundary. Such increasing values of AWMPFD might be due to fragmented growth and the emergence of new towns in and around the KMDA. In contrast, the clustered growth pattern towards the center was characterized by lower values (Fig. 6).
Urban Spatial Pattern Analysis
In this study, urban spatial patterns were measured and analyzed based on the percent coverage of urban pixels per 2 sq. km area. Following percent coverages of =75%, 50% to 75%,25% to 50%, and <25%, the study area was classified into four categories: zone of primary urban core, secondary urban core, suburban fringe, and scattered settlement, respectively (Fig. 7).
The distribution of such typical zonal patterns is very much analogous to the AWMPFD index. From the zone of the primary urban core at the central position to the zone of scattered settlements in an outward situation, there is a perceptible increase in the values of the AWMPFD index. The correspondence between the categorized values of the AWMPFD index in classes such as very high to high, high to moderate, moderate to low, and low to very low, and the typical zonal distribution of the urban spatial pattern can be clearly revealed from the perceptual maps (Figs. 8a-8d) derived from the multiple correspondence analysis (MCA). MCA is a potential tool for determining the optimum ordering of variables for a given set of characteristics (Weller et al. 1990). Homogeneity between row and column elements in terms of categorized AWMPFD index values and the typical zonal distribution of urban spatial patterns specifies the correspondence between them. Such homogeneity could be addressed by the maximum crosstabulated responses between each row and column element (Tables 8a-8d). In addition, the resultant perceptual maps also distinctly describe the correspondence between the row and column elements through their characteristic association. In perceptual maps, the close spacing between the elements depicts the correspondence among them and vice . versa. In this present study, for each period, . close spacing of low to very low level of AWMPFD with urban . . . primary and secondary core, medium to high level of AWMPFD with suburban fringe and scattered settlement, as well as depicting the characteristic associations viz, correspondence between urban spatial patterns and urban patch fragmentation.
However, the temporal dynamics of the typical urban spatial patterns are also noteworthy. Consistent growth was evident in the zones of the urban primary core, secondary core, and suburban fringe area Within the 30 years of the study (Table 10). The zone of urban primary core increased x + from 71.62 sq.km in 1990 to 104.51 sq.km in 2000 (at a growth rate of 3.85%), from 2000 to 2010 that has increased : to 213.15 sq.km (at a growth rate of 7.39%) and to 435.48 sq.km in 2020 (at a growth rate of 7.41%). The zone of urban secondary core, similar to the primary core, also increased from 129.44 sq.km in 1990 to 198.80 sq.km in 2000 (at the growth rate of 4.38%). However, the growth rate slowed in 2010. It increased to 222.53 sq.km at a growth rate of 1.13%, and again increased to 439.41 sq.km in 2020 ata regained growth rate of 7.04%. However, for the zone of - suburban fringe areas, the growth rate was considerably slow, such as from 1990 to 2000, which was 0.51%, from 2000 to 2010, which was 0.08%, and from 2010 to 2020, which was 1.15%. However, there was a distinct decrease in the zone of scattered settlements. From 1990 to 2020, the area decreased from 1262.35 sq.km to 417.38 sq.km (at a growth rate of 3.35%).
DISCUSSION
The planning and development of the Kolkata Metropolitan Area (KMDA), which includes Kolkata city and the surrounding suburban areas, is under the jurisdiction of the Kolkata Metropolitan Development Authority (KMDA). From 1990 to 2020, the KMDA has been involved in major changes brought about by infrastructure expansion, population shifts, and economic reforms. Within these 30 years, three phases of urban growth can be indicated, from 1990 to 2000: the initial expansion stage, from 2000 to 2010: the rapid suburbanization phase and from 2010 to 2020: the consolidation and modernization phase. Urban growth was primarily concentrated in the city core and nearby areas, such as Salt Lake (Bidhannagar), during the first phase (1990-2000), with some early development in areas such as Rajarhat (Mukherjee 2006). Suburban areas, such as South 24 Parganas, Howrah, and Rajarhat, had substantial business and residential development between 2000 and 2010. According to Das & Datta (2002), the New Town project became a significant metropolitan center. Significant development occurred in outlying places, including Baruipur, Sonarpur, and Kalyani, during the last phase (2010-2020). Vertical growth was prioritized for high-rise residential and commercial buildings (Roy 2019).
Between 1990 and 2020, the Kolkata Metropolitan Development Authority (KMDA) region underwent substantial urban changes, typified by polycentric expansion, a pattern in which several hubs of infrastructural, residential, and economic development appear within a metropolitan area. This layout supports a more sustainable and balanced urban landscape by reducing the demand for a conventional urban core.
Urban growth in the northeastern (Bidhannagar, Barasat, and Madhyamgram) and southeastern (Rajpur-Sonarpur and Baruipur) regions of the KMDA accelerated owing to significant shifts in the state's industrial and economic policies in 2000 (Shaw & Satish 2007). Major metropolitan cities and urban areas in India have experienced substantial urbanization as a result of the economic liberalization of 1991 (Mondal et al. 2015). The development of key industrial hubs in the northwestern (Dankuni) and southwestern (SankrailAbada) zones has further spurred this expansion (Vision 2025, KMDA). Notably, the current rate of urban expansion in the KMDA is significantly higher in settlements (census towns and villages) under rural administration than in those under urban governance. This highlights the ongoing trend of informal urbanization, where unregulated development is encouraged in peri-urban areas managed by rural institutions (Roy 2005, 2011, 2016).
The economic liberalization of India accelerated the transition of Kolkata from an industrial to a more diversified economy in the early 1990s. A basis for polycentric growth was established during this period, with an emphasis on the development of satellite townships and improvement of communication within the metropolitan area. Salt Lake City (Bidhannagar), which was first founded in the 1960s, kept growing until a major change occurred with the emergence of Sector V as an IT hub. This attracted professionals and tech businesses, establishing a new economic hub beyond the conventional city center (Chakrabarti et al. 2014). Furthermore, the Eastern Metropolitan Bypass, an important infrastructural project, enhanced connection between Kolkata's eastern and southern edges, enabling the growth of the city's residential and commercial sectors (Mukherjee 2006).
Rapid suburbanization and the emergence of new urban centers were the outcomes of significant economic growth and well-executed urban planning in the first decade of the twenty-first century. Thought of as a contemporary satellite town, New Town, Rajarhat quickly became a well-known urban center with large investments in IT parks, residential complexes, and commercial centers, signifying the urban development of Kolkata (Das & Datta 2002). By generating an efficient economic node outside of the city center, Sector V in Salt Lake maintained its position as a significant IT hub and brought in a large number of technology companies and professionals, which in turn contributed to the larger polycentric pattern (Sengupta 2008). Suburban areas witnessed substantial residential and commercial development, especially in Howrah and South 24 Parganas, located south of the Hooghly River. This development was supported by better connectivity, made possible by bridges such as the Vidyasagar Setu (Roy 2019).
By 2020, the Kolkata metropolitan area had many development centers, signifying the effective implementation of the polycentric growth model. New Town, Rajarhat, was designated a Smart City, emphasizing sustainable development and technology integration, thereby enhancing its status as a leading urban center with smart infrastructure efforts that improve economic productivity and quality of life (Bose 2018). The extension of the Kolkata Metro network, particularly the East-West Metro Corridor between Howrah and Salt Lake, has greatly facilitated polycentric growth by enhancing connectivity and permitting the movement of people and goods (Chatterjee 2018). Over the past decade, suburban regions such as Baruipur, Sonarpur, and Kalyani have experienced both horizontal expansion and vertical development, exemplified by the erection of high-rise residential and commercial edifices in areas such as New Town and along the Eastern Metropolitan Bypass (Chattopadhyay 2011, Dasgupta 2019).
Despite the various advantages of polycentric growth, including equitable economic development and reduced congestion in the urban core, it also has certain disadvantages. Rapid urbanization has precipitated environmental challenges such as wetland and natural habitat degradation (Dutta 2010). Infrastructure development, frequently trailing rapid growth, has resulted in traffic congestion and inadequate public services (Sarkar 2012). Disparities in access to infrastructure and services among various metropolitan areas exemplify the persistent nature of socioeconomic inequality (Mitra 2015).
The KMDA region underwent substantial transformation from 1990 to 2020, marked by the development of polycentric urban areas. This growth pattern mitigated the pressure on Kolkata's historic core by creating numerous growth centers, supported by strategic planning, infrastructure development, and economic diversification. Although the polycentric method has numerous benefits, it is essential to advocate for equitable and sustainable urban growth by continually addressing social, infrastructural, and environmental issues.
This study provides important insights into the differences in intra-urban growth patterns compared to theoretical expectations, highlighting the variations between the inner and outer zones of the Kolkata Urban Agglomeration (KUA). The growth dynamics in these areas are varied and exhibit distinct characteristics that challenge conventional theories of urban growth. The core areas typically experience densification and redevelopment, whereas the outer regions experience significant suburbanization and expansion of urban infrastructure.
Research predominantly uses landscape metrics to objectively evaluate the geographical characteristics and intricacies of urban growth. The metrics demonstrate that coalescence, the merging of smaller urban areas into larger, contiguous ones, has been the primary driver of urban expansion in the KMDA. This process indicates a transition from a dispersed urban growth pattern, known as diffusion, to a cohesive and integrated urban framework.
The increase in patch shape complexity, measured by the Area-Weighted Mean Fractal Dimension (AWMFD), underscores this alteration. As urban regions converge, their borders become progressively irregular and complex, reflecting the integration of diverse land uses and infrastructure. The shift from diffusion to coalescence has been noted in both the center areas and the growing edges of the agglomeration, indicating a widespread change in the urban growth pattern during the research period (Dietzel et al. 2005a, 2005 b).
The urbanized and mixed-use regions of the Kolkata Metropolitan Area (KMDA) have experienced continuous growth, resulting in a decline in undeveloped land (Mithun 2020, Mithun et al. 2016). This extension revealed substantial discrepancies between the KMDA-urban and KMDA-rural areas. The developed and mixed-use areas of KMDA-rural experienced significant expansion owing to accelerated peripheral growth. Conversely, the KMDA-urban experienced an expansion of built-up areas alongside a reduction in mixed-built-up areas, indicating that mixed-built-up land was being transformed into exclusively built-up land.
The most typical land cover type was built-up land, followed by mixed-built-up areas. While non-built-up land is mostly converted to mixed-built-up areas in KMDA-rural, mixed-built-up areas in KMD A-urban are mostly transitioning to built-up areas. This highlights a paradox in urban growth: although urban sprawl transforms undeveloped land into mixed-use areas, infill, expansion, and edge growth processes are transforming existing mixed-use regions into entirely urban built-up areas (Mithun 2020, Mithun et al. 2016).
The typical spatial growth pattern in the KMDA can be categorized into several zones: the urban primary core, urban secondary core, suburban fringe, and scattered settlements. The urban primary core includes central Kolkata, where growth is characterized by high-density development, infill projects, and the redevelopment of existing structures. Areas such as Esplanade, Dalhousie, and Park Street represent this core, exhibiting significant vertical growth and infrastructure modernization. The urban secondary core consists of emerging urban centers within the metropolitan area that function as significant economic and administrative hubs. Salt Lake (Bidhannagar) and Sector V serve as prime examples, with Salt Lake evolving into a major IT and commercial hub and Sector V becoming a critical IT corridor in Kolkata.
The suburban fringe encompasses regions that shift from rural to urban land use, propelled by suburbanization and the emergence of residential developments. Areas such as Rajarhat New Town illustrate this trend, characterized by rapid infrastructure advancement and the emergence of new residential developments that have altered the environment. Dispersed towns demonstrate fragmented urban development and are often situated on the outskirts of metropolitan areas. Areas such as parts of Howrah and South 24 Parganas demonstrate uneven growth, marked by varying densities and land uses.
Understanding these trends is crucial for urban planning because it highlights the dynamic and complex nature of urban development in the KMDA. The results imply that continuous urbanization is causing high-density developed zones to expand beyond formerly moderate- and low-density areas. The rapidly expanding urban center is extending the urban edge into the neighboring rural region. The urban environment evolves as low-density areas advance and retreat from growing cities. The urban-rural border, especially amid rapidly expanding urban agglomerations, is more aptly regarded as a dynamic frontier than as a static zone (Bosch et al. 2020, Dong et al. 2019).
The shift from diffusion to coalescence, differing growth dynamics of inner and outer zones, and transformation of mixed-use regions into developed land underscore the necessity for strategic and sustainable urban development plans. These insights can mitigate the problems of rapid urbanization, including environmental degradation, infrastructure pressure, and social inequality, thereby guaranteeing a balanced and equitable growth trajectory for the metropolitan region.
The dominance of edge expansion, together with the fluctuating growth and decline between infilling and outlying modes, aligns with the theoretical foundations of the three growth mode framework for overall agglomeration. Nonetheless, discrepancies were present among the six subterritories, along with their external and internal boundaries. In Phase I, infilling in the outer sections was negligible, and peripheral expansion in the inner portions was limited. In Phase II, edge expansion dominated the outer regions, restricting outlying growth, whereas infill type-1 prevailed in the inner areas, reducing edge expansion. Throughout the study, infilling increased in the inner sub-territories at the expense of outlying development and edge expansion, indicating a conflicting process. However, in Phase II, significant infilling occurred only in two sub-territories of the outer region. These results show variations from the predicted paths of urban growth, indicating that widely accepted models of urban growth (Bosch et al. 2020, Dahal et al. 2017, Li et al. 2013) need to be reexamined. These growth patterns and trajectories are further accentuated by subdivisions within the city's larger spatial extent.
To examine growth dynamics, this study used a directional method, breaking the Kolkata Metropolitan Area (KMDA) into eight directional sectors: north, northeast, east, south-east, south, south-west, west, north-west, and north. This approach is more successful in handling the complexities of the KMDA than standard zoning solutions, which concentrate on administrative divisions such as wards or municipalities (Punia et al. 2012, Sudhira et al. 2004). This study described typical patterns of urban development, as well as specific growth trajectories and rates. Notable disparities exist between the urban center and rural periphery in large metropolitan systems, such as the KMDA, concerning economic frameworks, infrastructure, urban amenities, local government, and population growth, all of which impact urban planning (Mithun et al. 2016). Different factors impact urban expansion in the core and periphery, necessitating specialized planning and policy approaches. Zoning analysis, which considers the administrative, socioeconomic, and demographic aspects of the KMDA, is therefore a useful instrument for studying urban growth and may be skillfully applied to the creation of policies and plans for urban areas.
To complete, the sprawl in patterns of urban form observed in this study, especially edge expansion or low-density urban sprawl, has grave consequences for environmental sustainability in the campus of the Kolkata Metropolitan Area. A significant decrease in ecological buffers that regulate urban microclimates and water retention has resulted in the conversion of agricultural land, wetlands, and peri-urban green spaces into built-up areas. This conversion has resulted in greater surface runoff, less groundwater recharge, and greater susceptibility to flooding, especially in the low-lying southwestern areas. Furthermore, the fragmentation of green patches and the elimination of vegetative cover have adversely affected local biodiversity and air quality. As urban centers expand, reliance on wheeled propulsion increases, adding emissions and poor air quality to this equation. The findings emphasize the need to integrate ecological considerations into urban planning frameworks, such as greenbelt preservation, compact growth strategies, and sustainable drainage systems, to reduce the environmental costs associated with urban land expansion.
Overall, this study demonstrates the complex, multidirectional manner of urban growth in the Kolkata Metropolitan Development Area over three decades, with a significant predominance of edge expansion and a clear shift in growth direction towards the southwest. The integration of spatial metrics using AUER, UEII, LEI, AWMPFD, and MCA provides detailed insights into the magnitude and pattern of urban sprawl [13]. These findings have practical implications for urban governance. It is in the understanding of this spatial bias towards expansion that planners can target infrastructure that aligns with high-growth zones, particularly in the south-western periphery. In addition, detecting edge-dominated growth patterns indicates the need to implement stronger land-use regulations and more aggressive infill development strategies to limit sprawl. This study reinforces the need for regional planning to align with both transportation infrastructure and socioeconomic dynamics to achieve more balanced and sustainable urban development.
CONCLUSIONS
The land use of the Kolkata Metropolitan territory (KMDA) has changed significantly, especially with the rise in builtup and mixed-use zones and the decline in non-built-up territories. There are considerable differences in these developments across the urban and rural areas of the KMDA. Built-up and mixed-use projects have increased in rural areas owing to rapid peripheral expansion: however, in metropolitan settings, mixed-use land is progressively being converted into fully developed zones, reflecting the dynamics of edge growth, urban sprawl, and infill (Mithun 2020, Mithun et al. 2016).
This study employed a spatial pattern-based approach to examine the relevance of traditional urbanization theories to KMDA"s growth patterns. This reveals that the dense urban core has extended outward, affecting both already urbanized zones and previously undeveloped open spaces. The peripheral areas are the most dynamic, showing significant variations in urban growth metrics across different spatial scales.
At the metropolitan level, a discernible transition from diffusion to coalescence was observed. Inner-city areas primarily exhibit coalescent growth, whereas outer zones show mixed patterns of diffusion and coalescence, varying in intensity across distinct sub-territorial units. These multiscale analyses are essential for understanding intra-urban differences and formulating spatial policies that cater to diverse growth patterns.
The zoning technique used in this study is a robust method for examining urban growth dynamics, with potential applicability to other developing metropolises. This highlights rapid growth, especially in the KMD A°s peripheral areas, underscoring the need for sustainable residential development supported by adequate infrastructure, particularly in the west, southwest, south, southeast, and east directions. Future urban planning should focus on compact growth strategies to curb sprawl, with regular assessments to ensure that growth targets align with urban planning goals, thereby promoting sustainable development.
To effectively manage urban sprawl, this study recommends implementing target-based growth strategies at both the local and metropolitan levels, with periodic evaluations to ensure that actual growth matches planned objectives. Integrating these strategies into urban planning could encourage more compact urban growth and prevent unplanned expansions. Further research across various urban areas and city sizes could enhance urban growth theories and inform policies for sustainable urbanization, focusing on dynamic spatial territories and intra-urban growth patterns to mitigate environmental impacts (Mithun 2020, Mithun et al. 2016).
Policy Implications
The results of this study could be a good reference for urban policymakers and urban planners. This edge growth and urban sprawl towards the southwest requires forwardthinking zoning regulations to impose control on the inordinate amount of peripheral development. Authorities can encourage infill development in cities, for example, to use land more efficiently and ease pressure on infrastructure. The high-growth zones identified may be prioritized for preventative planning of infrastructure, such as networks for transport, drainage, and green spaces. Moreover, incorporating spatial indices, such as LEI and AWMPFD, into regular urban monitoring assists evidence-based planning decisions so that urbanization can be regulated in a suitable amount in accordance with environmental conditions.
LIMITATIONS
Although this study is comprehensive, some limitations should be recognized.
Classification Uncertainties
Landsat-based urban land cover classification may contain erroneous elements because of the medium spatial resolution (30m) and similarity of building and bare soil spectral signatures. Although accuracy assessments were carried out with Google Earth and other ancillary data, there may be some misclassification, especially in mixed land-use areas.
Temporal Resolution of Data
Urban growth was evaluated at decade intervals (1990, 2000, 2010, and 2020). Intermediate variability and short- to mid-term land use transitions may not be captured, missing out on important things such as sudden switches from large policy acts or infrastructure projects.
Model Assumptions
These four spatial indices (AUER, UEII, LEI, AWMPEFD, and MCA) are based on random assumptions regarding landscape structure and urban behavior. These models are commonly used in the study area, but the interpretations were spatially selective and may not accurately represent all areas of all zones of the KMDA.
Absence of Socioeconomic and Policy Variables
This study analyzed spatial metrics and remote sensing data. However, even though some policy and infrastructure input is provided indirectly, the integration of demographic, economic, and policy datasets into the dataset would enhance insights into urban dynamics.
Biases Potential In Ground Reference Data
The use of Google Earth and limited ground-truthing to assess accuracy may induce bias, especially for earlier years (1990, 2000) with sparse or inconsistent historical high-resolution imagery.
Future studies can overcome these restrictions by using higher spatial resolution satellite data (such as Sentinel-2 or PlanetScope), broader temporal scales, and harmonized socio-environmental databases for a comprehensive urban analysis approach.
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