Delineating Urban Growth Boundary Using Remote sensing, ANN-MLP and CA model: A Case Study of Thiruvananthapuram Urban Agglomeration, India

Chettry, Vishal; Surawar, Meenal

doi:10.1007/s12524-021-01401-x

Cited by 27 publications

(6 citation statements)

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“…Although Thiruvananthapuram acts as a service town, where people are primarily engaged in government and institutional activities, post-1990, there has been tremendous growth in industrial units within the UA. The establishment of Technopark at Kazhakootam in 1990 and KINFRA (Kerala Industrial Infrastructure Development Corporation) in 1993 developed small industries park at Thumba and a film and video park at Kazhakoottam (Thiruvananthapuram Corporation 2012 ; Chettry and Surawar 2021b ). There is one industrial estate at Pappanamcode, one mini-industrial estate at Ulloor, and one industrial development center at Kochuveli.…”

Section: Discussionmentioning

confidence: 99%

“…It is derived from the spatial heterogeneity of the surroundings that arises primarily due to the natural aspects such as topography; socioeconomic aspects, such as population and market factors; and risks such as floods and landslides. As per Chettry and Surawar ( 2021a , b , c ), the urban sprawl types can be classified into four major categories such as secondary urban core, urban fringe, ribbon development, and scatter development (Fig. 3 ).…”

Section: Methodsmentioning

confidence: 99%

“…Post-1961, the concept of urban sprawl gained momentum after Jane Jacobs published her famous essay titled “The death and life of great American cities.” Urban sprawl is considered a random and haphazard low-density urban growth pattern that eventually contributes to inefficient usage of resources (Ewing 1997 ; Galster et al 2001 ; Bhatta 2010 ; Kashem et al 2014 ; Kamruzzaman et al 2018 ). The immediate consequence of urban sprawl is a transformation in the land cover of an area, mostly due to a rise in the built-up and impervious surfaces (Morya and Ram 2020 ; Chettry and Surawar 2021a , b , c ). RS has been effectively used in the urban sprawl research to detect urban sprawl and map the temporal patterns of urban sprawl, e.g., Guangzhou, China (Yu and Ng 2007 ); Europe (Arribas-bel et al 2011 ); Italy (Nol et al 2014 ); Teresina, Brazil (Espindola et al 2017 ); Western Cape Province, South Africa (Horn and Eeden 2018 ); and Vijayawada, India (Vani and Prasad 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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Peri-urban area delineation and urban sprawl quantification in Thiruvananthapuram Urban Agglomeration, India, from 2001 to 2021 using geoinformatics

Chettry

2022

Appl Geomat

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In Southeast Asia, rising population, economic growth, and lack of land supply in the city core have led to the widespread irreversible land cover transformation in peri-urban areas. Such extensive and haphazard urban growth in peri-urban areas raises concern about analyzing and promoting planned urban growth. Therefore, this paper attempts to assess land cover changes from 2001 to 2021 and delineate peri-urban areas of a midsized Indian city, i.e., Thiruvananthapuram Urban Agglomeration (UA) using geoinformatics. The maximum likelihood supervised classification tool in ArcGIS 10.3 was used to prepare land cover maps for 2001, 2007, 2014, and 2021. Further, the presence of urban sprawl in the peri-urban areas was detected through Shannon’s entropy index. The urban sprawl typologies in the peri-urban areas were quantified using the adjacent neighborhood relationships concept. The results revealed rapid growth in built-up land cover and decline in non-built-up land cover within Thiruvananthapuram UA during the study period. Peri-urban areas were delineated based on nine indicators, such as total population, population growth, population density, literacy rate, percentage of the male workforce, percentage of the female workforce, agricultural land cover, distance from urban core, and percentage of cultivators to agricultural workers. A rise in Shannon’s entropy index from 1.59 in 2001 to 2.05 in 2021 exhibited the occurrence of rapid urban sprawl in the peri-urban areas. Dominance of discontinuous low-density development, i.e., scatter development typology of urban sprawl, was observed in peri-urban areas of Thiruvananthapuram UA. Such studies using geoinformatics would assist local governments in scientifically formulating sustainable urban policies and plans.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Peri-urban area delineation and urban sprawl quantification in Thiruvananthapuram Urban Agglomeration, India, from 2001 to 2021 using geoinformatics

Chettry

2022

Appl Geomat

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“…As an effective urban planning and management method implemented worldwide (Bengston & Yeo‐Chang, 2006; Gennaio et al., 2009; Han et al., 2009; Jun 2004; Mubarak, 2004), the delimitation of UGBs has been studied for a long time. Currently, most of the UGBs in practice are mainly based on local policies, development needs and experts' experiences (Chettry & Surawar, 2021; Yang, Zhang, et al., 2019). For example, experience‐based UGBs were first qualitatively established and then delineated according to different development needs in Portland, Oregon, USA (The Nature of 2040, 2000).…”

Section: Introductionmentioning

confidence: 99%

Delimiting China's Urban Growth Boundaries Under Localized Shared Socioeconomic Pathways and Various Urban Expansion Modes

et al. 2022

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To limit urban sprawl and reduce the negative impacts of urban expansion, the United States has taken the lead in delimiting urban growth boundaries (UGBs), that is, setting boundaries around cities and limiting urban expansion within such boundaries (Daniels, 2001). This approach can effectively protect the cropland and natural habitat around cities (Wei & Ye, 2014), alleviating the contradiction between urban development and the conservation of resources and environment to realize sustainable development (Jiang, Chen, et al., 2016;Long et al., 2013;Tayyebi et al., 2011). In China, where the urban land area (ULA) is increasing rapidly, delimiting UGBs has

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“…In recent years, a number of scholars have given a more neutral definition of urban expansion, arguing that urban construction land [8], changes in urban land and population size [9], built-up areas [10], impervious surfaces [11,12], etc., are the main contents of urban expansion. Thus, scholars mostly use remote sensing monitoring [1,6], GIS spatial analysis [13,14], fractal theory [15,16], landscape index measurement [17,18], cellular automata model [19,20], and other methods to study the phenomenon of urban expansion. From a macro perspective, they extract impervious surface area, monitor the change of urban construction land area, analyze urban land use efficiency, study the scale, space, and morphological characteristics of expansion, and predict the urban development pattern; from a micro perspective, they analyze the landscape pattern characteristics of urban expansion, and judge the mode of urban spatial expansion [21,22] (infill, expansion, enclave, and linear [23]), and thus provide a scientific basis for promoting green urbanization and sustainable development [24,25].…”

Section: Introductionmentioning

confidence: 99%

Urban Expansion Assessment Based on Optimal Granularity in the Huaihe River Basin of China

et al. 2022

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Determining the optimal granularity, which has often been ignored in the analysis of urban expansion and its landscape pattern, is the core problem in landscape ecology research. Here, we calculate the optimal granularities for differently sized cities in the Huaihe River Basin of China based on scale transformation and area loss evaluation. Accordingly, we construct a landscape index and urban land density function to analyze urban expansion and landscape pattern. The results can be summarized as follows. (1) Within the first scale domain of the landscape indices, the optimal granularities of Zhengzhou, Xuzhou, Yancheng, Xinyang, and Bozhou are 60 m, 50 m, 40 m, 40 m, and 40 m, respectively, which are the optimal units in the study of urban expansion. (2) The urban land density decreases from the urban center to the outskirts, the urban core of each city is more compact than the outskirts, and the land density curve parameter α of Zhengzhou is the largest at 4.693 and its urban core the most compact. (3) There are significant spatial and temporal differences in the urban land densities of differently sized cities. The urban land density functions of different cities are similar before 2000; after that, they are similar to the standard inverse S-shaped function and the land use density curve of large cities is closer to the standard inverse S-shaped function than that of small- and medium-sized cities. (4) Large cities have faster expansion, much larger land density curve parameter c than medium- and small-cities, stronger linkage development with surrounding areas, and a higher degree of urban centralization. Urban expansion compactness was influenced by urban locations and functions except for urban sizes. This study offers a method for identifying the optimal granularities for differently sized cities and also provides information for the decision-making efforts that concern the rapid urbanization in major grain-producing areas of China.

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Delineating Urban Growth Boundary Using Remote sensing, ANN-MLP and CA model: A Case Study of Thiruvananthapuram Urban Agglomeration, India

Cited by 27 publications

References 75 publications

Peri-urban area delineation and urban sprawl quantification in Thiruvananthapuram Urban Agglomeration, India, from 2001 to 2021 using geoinformatics

Peri-urban area delineation and urban sprawl quantification in Thiruvananthapuram Urban Agglomeration, India, from 2001 to 2021 using geoinformatics

Delimiting China's Urban Growth Boundaries Under Localized Shared Socioeconomic Pathways and Various Urban Expansion Modes

Urban Expansion Assessment Based on Optimal Granularity in the Huaihe River Basin of China

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