Urban growth modeling of Kathmandu metropolitan region, Nepal

Thapa, Rajesh; Murayama, Yuji

doi:10.1016/j.compenvurbsys.2010.07.005

Cited by 118 publications

(80 citation statements)

References 18 publications

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“…The accuracy assessment of the simulation results differs slightly from that of the image classification results (accuracy assessment for image classification rely on ground-truth points for each class) [39]. A LU/LC dynamics model is usually validated by comparing the predicted result to the reference layer to determine the prediction quality of the model [82]. To assess the validity of the models in this study, different components of the kappa index of agreement (KIA), including the kappa for no information (Kno), kappa for grid-cell level location (Klocation), and kappa for stratum-level location (KlocationStrata), as introduced by Pontius et al (2000) [83], were used in addition to Cohen's (1960) [84] kappa standard (Kstandard) (equivalent to kappa, i.e., the proportion assigned correctly versus the proportion that is correct by chance) [66].…”

Section: Validation Of Simulation Resultsmentioning

confidence: 99%

Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

Öztürk

2015

Remote Sensing

142

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Abstract:In this study, urban growth of the Atakum District in Samsun, Turkey, was simulated by Cellular Automata-Markov Chain (CA-MC) and Multi-layer Perceptron-Markov Chain (MLP-MC) hybrid models in a geographical information system (GIS) environment. Historical land use/land cover (LU/LC) data were extracted from 1989, 2000 and 2013 Landsat TM/ETM+/OLI images. Using the LU/LC data for the years 1989 and 2000, the urban growth for 2013 was simulated using the CA-MC and MLP-MC models. The simulation results were compared with the 2013 LU/LC data to assess the validity of the simulation. The MLP-MC method provided the best results according to the validation based on the kappa index of agreement. Based on this result, the urban growth for the year 2025 was simulated using MLP-MC. The simulation estimated an urban growth rate of 35.2% between 2013 and 2025, an increase in the area of artificial surfaces from 1681.9 ha to 2274.3 ha and the destruction of 511.7 ha of agricultural land and 4.4 ha of forest. The results of this study demonstrate that the urban growth models provide a better understanding of the current patterns and temporal dynamics and can predict future changes according to past and current dynamics. The results also show that simulations are most accurate when using a model that best conforms to the changes in the given study area.

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Section: Validation Of Simulation Resultsmentioning

confidence: 99%

Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

Öztürk

2015

Remote Sensing

142

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“…While considered to be one of the most influential factors impacting urban growth (Thapa and Murayama (2011)), the human behaviour factor was generally overlooked on the CA articles or was not well represented (Benenson, 1999). Lauf et al (2012) mentioned that the human behaviour factor improves the variability, adapting to the stochastic nature of predicting urban changes, and improving the dynamic presentation of the spatial changes.…”

Section: Discussionmentioning

confidence: 99%

“…The examples of this early application were in Amherst and San Francisco Bay, USA, and in Guangzhou, China (Batty and Xie, 1994;Clarke1997). In the period after 1997, the increasing applications of CA in urban modelling in Europe and other Asian countries, such as in Japan, Korea, Malaysia, and Nepal have been observed (Guan et al, 2011;Naimah et al, 2011;Thapa and Murayama, 2011). In Nepal for instance, the combination of CA with the Bayesian approach has been successfully implemented to predict the future expansion of urban areas in Kathmandu.…”

Section: The Spatial Coverage Of Ca Urban Model Applicationsmentioning

confidence: 99%

“…In other Asian countries, existing developed areas emerged as a key driving factor in CA urban models. In this region, people tend to live near the already developed areas to experience the benefits of the existing infrastructure and proximity to their workplaces (Maithani, 2010;Thapa and Murayama, 2011). In most Asian cities in developing countries, faced with unreliable public transportation and facilities, typical urban development near existing urban areas is common practice (Hudalah and Firman, 2012).…”

Section: The Selection Of Factors In Different Regionsmentioning

confidence: 99%

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Cellular Automata for Urban Growth Modelling:

Wahyudi

Liu

2016

IRSPSD International

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Abstract:Urban growth modelling has attracted considerable attention over the past two decades. This article reviews the driving factors that have been identified and studied in cellular automata (CA); one of the popular methods in urban growth modelling. Over a hundred articles published between 1993 and 2012 were selected and reviewed. We extracted the driving factors from CA transition rules and arranged them in a list. The list contributes to early spatial research for the selection of factors in CA models. Our analyses show that studies between 1993 and 2000 mainly focused on using earth's physical factors in predicting urban growths, while recent studies combined them with socioeconomic factors, resulting with models with a greater number of inputs. Nevertheless, the human-behaviour factors impacting urban growth were generally under-represented. Geographically, more applications of the CA urban growth models have been seen in the developed countries compared with those in the developing countries, suggesting substantial work is needed to address issues in understanding and modelling rapid urban growth processes in developing countries.

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“…Six city-and-town-concentrated areas (approximate to urban agglomeration) have also been formed: Central Shanxi Province, Central Hunan Province, Central Plains, 2 Discrete Dynamics in Nature and Society systematic dynamics [12] have been used to describe spatial expansion of cities. The growth of urban systems is also influenced by the scale of urban space, traffic network, and complexity of the terrain [13][14][15]. A mechanism for the growth of the city has been proposed [16] but modeling the spatial and temporal dynamics of urban formation and evolution remains difficult [17].…”

Section: Introductionmentioning

confidence: 99%

Spatial Pattern and Evolution of Urban System Based on Gravity Model and Whole Network Analysis in the Huaihe River Basin of China

Fan

Zhang²,

et al. 2018

Discrete Dynamics in Nature and Society

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The spatial pattern and evolution of urban system have been hot research issues in the field of urban research. In this paper, the network analysis method based on the gravity model and the related measurements were used to reveal the properties of the spatial pattern and evolution of the urban system in the HRB (Huaihe River Basin) of China. The findings of this study are as follows: During the period from 2006 to 2014, the economic contact between the HRB cities has been strengthened, but the differences between cities have been expanding. In general, the HRB cities have not yet formed a close network structure, and a trend of economic integration has not been found. This paper expresses the spatial pattern and evolution of urban system in an intuitive way and helps to explain the evolution mechanism of urban system. The method was confirmed by empirical research. Because of the operational and visual expression, this method has broad application prospects in the urban system research.

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Urban growth modeling of Kathmandu metropolitan region, Nepal

Cited by 118 publications

References 18 publications

Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

Cellular Automata for Urban Growth Modelling:

Spatial Pattern and Evolution of Urban System Based on Gravity Model and Whole Network Analysis in the Huaihe River Basin of China

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