Spatio-temporal patterns of urbanization in the Kolkata Urban Agglomeration: A dynamic spatial territory-based approach

Chakraborty, Saurav; Maity, Indranil; Patel, Priyank Pravin; Dadashpoor, Hashem; Pramanik, Suvamoy; Follmann, Alexander; Novotný, Josef; Roy, Utpal

doi:10.1016/j.scs.2021.102715

Cited by 66 publications

(19 citation statements)

References 54 publications

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“…In addition, given the iterative operation of most CA‐based models (including the LP‐CA model used to simulate the scenarios), the algorithm for calculating vLEI could be improved by applying a multi‐temporal methodology, adapting the Multi‐order Landscape Expansion Index (MLEI) (Jiao et al., 2015) to the dynamic visualization of particular types of growth over time in vector land‐use maps. This could lead to oscillating spatiotemporal patterns during the urban expansion process (Chakraborty et al., 2021; He et al., 2017; Xu et al., 2007), in which urban growth comes in alternate phases of dispersion and aggregation, with changes in scale as the city increases in size (Dietzel et al., 2005).…”

Section: Resultsmentioning

confidence: 99%

Adapting moving‐window metrics to vector datasets for the characterization and comparison of simulated urban scenarios

Molinero‐Parejo,

Aguilera‐Benavente,

Gómez‐Delgado

2024

Transactions in GIS

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Descriptive scenarios about the possible evolution of land use in our cities are essential instruments in urban planning. Although the simulation of these scenarios has enormous potential, further characterization is needed in order to be able to evaluate and compare them so as to provide more effective support for public policy. One of the most commonly used tools for assessing these scenarios is spatial moving‐window metrics, a useful mechanism for extracting accurate information from simulated land‐use maps on urban diversity and urban growth patterns. This article seeks to explore this question further and has two main aims. First, to develop and implement vSHEI and vLEI, two multiscale composition and configuration vector moving‐window metrics for calculating urban diversity and urban growth patterns. Second, to test these metrics using the spatially explicit simulation of three prospective scenarios in the Henares Corridor (Spain), comparing the results and analyzing how well the scenario narratives match their spatial configuration, as measured using vSHEI and vLEI. Via the implementation of vSHEI and vLEI, we obtained urban diversity and urban expansion values at a local level, offering more precise and more realistic, mappable information on the composition and configuration of urban land use than that provided by raster metrics or by vector Patch‐Matrix model metrics. We also used these metrics to test whether the simulated scenarios matched their description in the narrative storylines. Our results demonstrate the potential of vector moving‐window metrics for characterizing the urban patterns that might develop under different scenarios at the parcel level.

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Section: Resultsmentioning

confidence: 99%

Adapting moving‐window metrics to vector datasets for the characterization and comparison of simulated urban scenarios

Molinero‐Parejo,

Aguilera‐Benavente,

Gómez‐Delgado

2024

Transactions in GIS

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“…Rapid development in urban expansion is gradually diminishing the boundaries between urban and rural areas. Particularly, the expansion of urban areas and the modernization of rural areas have increased the appearance of urban characteristics in rural areas ( (Chakraborty et al, 2021;Wang et al, 2020). Therefore, identifying the urban and rural areas through practical methods and approaches is important for harmonious urban-rural development (Yang et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Using Food Delivery Data to Identify Urban -Rural Areas: A Case Study of Guangzhou, China

Zhou

et al. 2022

Front. Earth Sci.

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It is an important task for planners and decision makers to determine whether a certain region is urban or rural since the accurate identification of these areas is of considerable significance to further study differences in urbanization development and balance contradictions between them. However, at present, there are problems of low efficiency and considerable subjectivity in the identification of urban and rural areas based on nighttime light data. Therefore, this study proposes a new method to identify urban and rural areas based on the differences between them. Taking Guangzhou as an example, this study simulated food delivery scenarios to identify urban and rural areas in Guangzhou. The findings indicated that using food delivery data to identify urban and rural areas is highly precise, with an accuracy rate of 92.4% and Kappa value of 0.79. This study provides a new method to identify urban and rural areas accurately and objectively, contributes to the study of urban-rural differences in urbanization and providing a feasible method for subsequent urban and rural planning.

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“…Tumwesigye [78] stated that urbanization in sub-Saharan Africa witnessed rapid urbanization and analyzed the spatial pattern of urbanization in Uganda based on the distribution of registered population size. Chakraborty [79] analyzed the spatio-temporal patterns of urban growth for the Kolkata Urban Agglomeration in eastern India using dynamic spatial territorial extents and compared the results with existing popular hypotheses of urbanization patterns. Gangopadhyay [80] analyzed the development of urbanization in Cambodia and analyzed the primary driving factors of urbanization by autoregressive distributed lag (ARDL) analysis and nonlinear autoregressive distributed lag (NARDL) models, pointing out that foreign direct investment flows have asymmetric effects.…”

Section: Research Scalementioning

confidence: 99%

Spatio-Temporal Evolution and Driving Mechanism of Urbanization in Small Cities: Case Study from Guangxi

Zhao

Wang

et al. 2022

Land

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Urbanization has an abundant connotation in dimensions such as population, economy, land, and society and is an important sign to measure regional economic development and social progress. The use of Night Light Data from remote sensing satellites as a proxy variable can significantly improve the accuracy and comprehensiveness of the measurement of urbanization development dynamics. Based on the Night Light Data and statistical data from 2015 to 2019, this paper quantitatively analyzes the spatio-temporal evolution pattern of urbanization in Guangxi and its driving mechanism using exploratory time-space data analysis, GeoDetector and Matrix: Boston Consulting Group, providing an important basis for sustainable urban development planning and scientific decision-making by the government. The findings show that (1) there is a high level of spatial heterogeneity and spatial autocorrelation of urbanization in Guangxi, with the Gini index of urban night light index and urban night light expansion vitality index always greater than 0.5, the global Moran’s I greater than 0.17, the spatial differentiation converging but the spatial correlation increasing. (2) The spatial pattern of urbanization in Guangxi has long been solidified, but there is a differentiation in urban development trend, with the coexistence of urban expansion and shrinkage, requiring differentiated policy design for urban governance. (3) The development and evolution of urbanization in Guangxi present a complex intertwined dynamic mechanism of action, with interaction effects of bifactor enhancement and non-linear enhancement among factors. It should be noted that the influence of factors varies greatly, with the added value of the tertiary industry, gross domestic product, total retail sales of social consumer goods having the strongest direct effect on the urban night light index, while the added value of secondary industry, per capita GDP, gross domestic product having the strongest direct effect on the urban night light expansion vitality index. All of them are key factors, followed by some significant influence factors such as government revenue, population urbanization rate, per government revenue, population urbanization rate, per capita disposable income of urban and rural residents that should not be ignored, and the rest that play indirect roles mainly by interaction.

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Spatio-temporal patterns of urbanization in the Kolkata Urban Agglomeration: A dynamic spatial territory-based approach

Cited by 66 publications

References 54 publications

Adapting moving‐window metrics to vector datasets for the characterization and comparison of simulated urban scenarios

Adapting moving‐window metrics to vector datasets for the characterization and comparison of simulated urban scenarios

Using Food Delivery Data to Identify Urban -Rural Areas: A Case Study of Guangzhou, China

Spatio-Temporal Evolution and Driving Mechanism of Urbanization in Small Cities: Case Study from Guangxi

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