Analysing spatial patterns and trend of future urban expansion using SLEUTH

Chandan, M. C.; Nimish, G.; Hariharan, Bharath

doi:10.1007/s41324-019-00262-4

Cited by 28 publications

(4 citation statements)

References 38 publications

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“…It represented the comparison between shape and form of the modelled and actual urban growth. The Lee-Sallee values of more than 0.4 are acceptable and indicates near perfection calibration (Chandan et al 2020). The results of calibration indicated that the coefficients for growth modelling characterises matched with the actual growth pattern and could be used for the further prediction for LULC of the Prayagraj.…”

Section: Model Calibration and Validationmentioning

confidence: 82%

Urban modelling and forecasting of landuse using SLEUTH model

Kumar

Agrawal

2022

Int. J. Environ. Sci. Technol.

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The present study performs landcover modelling using the SLEUTH model. The urban land use changing factors are calibrated to predict the Land Use Land Cover (LULC) for a densely populated and developing smart city, Prayagraj, India. This research aims to use the SLEUTH model for simulating the future urban growth with the help of historical LULC (1990LULC ( -2020, road network and elevation data. The influence of road gravity and slope resistant coefficients is very significant in this study's outcome. The built-up area of the region increased from 40.22 km 2 (5.10% of total area) in 1990 to 85.89 km 2 (10.89%) in 2020. According to prediction results, in the next 20 years, the built-up growth rate would be 1.9% and approximated built-up area would be 118.66 km 2 (14.98%) in the year 2040. The quality of the result has been quantified in terms of best fit value of Optimal SLEUTH Metric (OSM) and validated against the existing LULC. The study utilises a spatially explicit urban growth model with 30 m resolution remote sensing data and provides future landuse of Prayagraj city.

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Section: Model Calibration and Validationmentioning

confidence: 82%

Urban modelling and forecasting of landuse using SLEUTH model

Kumar

Agrawal

2022

Int. J. Environ. Sci. Technol.

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“…The aforementioned layers were compiled according to the specifications provided by Dietzel & Clarke (2004). Layers were exported greyscale images with various spatial resolutions: 120m for coarse phase, 60m for fine phase and 30m for full phase calibration (Chandan et al, 2019). Obtained layers were named according to specified format to ensure data compatibility during the model calibration process.…”

Section: Future Land-use Transitions and Estimationmentioning

confidence: 99%

Impact assessment of Corridor Oriented development

Chandrashekar

Aithal

2021

IRSPSD International

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Rapid urbanisation has been a factor affecting cities negatively and irreversibly in developing countries like India, adversely leading to depleting natural resources and promoting unbalanced and uneven urbanism. To handle the influx of population into core urban regions and to promote holistic, sustainable development, government and planning agencies are now looking upon regional development. Developing countries like India has laid plans for future urban corridor-oriented development. This study aims to understand the urban growth of two major developing cities influenced by transport corridor through a methodological approach using multi-temporal satellite data and its position in India's network of cities. Land use analysis was validated with the aid of measures such as overall accuracy and kappa statistics, with good values of more than 85% and 0.75 respectively were achieved. The hierarchical network analysis indicated five different clusters based on the urban growth rate. Among these clusters, Bangalore, Ahmedabad and Pune cluster was further shortlisted for analysis based on the urban transport corridor affecting the growth of these cities. Cellular automata-based SLEUTH model was adopted in this work to carefully observe sub-division level details of the region under the influence of the corridor. Exhaustive calibration, with three phases of coarse, fine and final, validation procedure along with statistical fit measures reveal urban expansion for Ahmedabad region has witnessed an increase from 497.50 km 2 (2017) to 826.24 km 2 (2025) while Pune region has experienced tremendous urban area transformation of 901.11 km 2 in the year 2025 against 497.27 km 2 in 2017. Results of this analysis would help policymakers and planners to inculcate decisions concerning future urban trends accommodating safer, healthier, sustainable and liveable urban ecosystem.

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“…SLEUTH is a CA-based urban growth model, started as open source, has witnessed numerous applications over different parts of the world. The acronym of SLEUTH stands for slope, land use, excluded, urban, transportation and hillshade, the layers used as input for the model, consists of C-language, using UNIX or UNIX based operating systems (Nimish et al, 2018;Chandan et al, 2019). It has an ability to predict urban/non-urban land use dynamics based on two sub models: urban growth model (UGM) and Deltron land model (DLM) (Dietzel & Clarke, 2004 to Gorgan area of Iran.…”

Section: Figure 1 Trends Of Urbanization In India 1901-2011mentioning

confidence: 99%

Machine learning for building extraction and integration of particle swarm optimization with sleuth for urban growth pattern visualization for liveable cities

Chanden¹,

Aadithyaa²,

Prakash³

et al. 2019

Proceedings of the 55th ISOCARP World Planning Congress

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Rapidly increasing population and migration from rural areas to nearby urban agglomerations develop tremendous pressure on system of the existing cities without compromising socioeconomic and cultural linkages. Policy interventions, both at global and local scale, have created newer avenues for the researchers to explore real-time solutions for problems world-wide. For instance, the outcome of 2015 United Nations agenda for the achievement of the Sustainable Development Goals (SDGs) by the year 2030 primarily focuses on urbanization issues and probabilistic modelling of future scenarios to obtain a robust alternative for resource utilization and further for maximizing sustainability through land use pattern analysis. This is the clear indication toward the very important role of “ever dormant” urban planning, especially in the case of a rapidly developing country such as India. Remote sensing and geo informatics along with Machine learning can provide extremely relevant information about the pattern change in cities and as input to visualize the future growth pockets. In this context, potential of cellular automata (CA) in urban modelling has been explored by various researchers across the globe. In the recent past, models have been drawing majority of the attention along with geographic CA processes about urban growth and urban sprawl studies. Most recent approaches include optimization of transition rules based on machine learning techniques and evolutionary algorithms that follow nature-inspired mechanism such as Genetic Algorithm, Ant colony optimization, Particle Swarm Optimization (PSO), simulated annealing, Grey Wolf optimizer etc. Irrespective of any modelling technique, model calibration remains one of the challenging and most crucial steps towards obtaining realistic results. This research communication tries to demonstrate a novel idea of integrating PSO with SLEUTH post calibration of the spatial-temporal footprint of urban growth from the year 1990 to 2017 for Kolkata, a historical megacity of Eastern India. Results were evaluated and validated using statistical fit measuresreveals PSO-SLEUTH performed substantially better compared to traditional Brute Force calibration method (BFM). Another significant development was in terms of computation time of optimized values from days (BFM) to hours (PSO). The study identifies Kolkata region to be sensitive to spread and road gravity coefficients during calibration procedure. Results indicate growth along the transport corridors with multiple agents fuelling the growth. Further, with the aid of high spatial resolution data, buildings were extracted to understand the growth parameters incorporating neural networks. Using the results, renewable energy aspects were explored to harness and provide a suitable local solution for energy issues in energy gobbling cities. Pattern of landscape change, development of better process of modeling and extraction of building from machine learning techniques for planning smart cities with self-sustaining energy is presented in this research work.

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Analysing spatial patterns and trend of future urban expansion using SLEUTH

Cited by 28 publications

References 38 publications

Urban modelling and forecasting of landuse using SLEUTH model

Urban modelling and forecasting of landuse using SLEUTH model

Impact assessment of Corridor Oriented development

Machine learning for building extraction and integration of particle swarm optimization with sleuth for urban growth pattern visualization for liveable cities

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