A multi-scale modeling approach for simulating urbanization in a metropolitan region

Saad Saleem Bhatti; Nitin Kumar Tripathi; Vilas Nitivattananon; Irfan Ahmad Rana; Chitrini Mozumder

doi:10.1016/j.habitatint.2015.09.005

A multi-scale modeling approach for simulating urbanization in a metropolitan region

Saad Saleem Bhatti, Nitin Kumar Tripathi, Vilas Nitivattananon, Irfan Ahmad Rana, Chitrini Mozumder

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62 Citations (Scopus)

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Abstract

Metropolitan regions worldwide are experiencing rapid urban growth and the planners often employ prediction models to forecast the future expansion for improving the land management policies and practices. These regions are a mix of urban, peri-urban and rural areas where each sector has its unique expansion properties. This study examines the differences in urban and peri-urban growth characteristics, and their impact at different stages of prediction modeling, in city district Lahore, Pakistan. The analysis of multi-temporal land use/land cover maps revealed that the associations between major land transitions and the factors governing land changes were unique at city district, urban and peri-urban scales. A multilayer perceptron neural network was employed for modeling urbanization, and it was found that the sub-models developed for urban and peri-urban subsets returned better accuracies than those produced at the city district scale. The prediction maps of 2021 and 2035 were also produced through this approach.

Original language	English
Pages (from-to)	354-365
Number of pages	12
Journal	Habitat International
Volume	50
Early online date	29 Sept 2015
DOIs	https://doi.org/10.1016/j.habitatint.2015.09.005
Publication status	Published (in print/issue) - 1 Dec 2015

Funding

The authors gratefully acknowledge the support from the Asian Institute of Technology, Thailand , and the Japanese Government for carrying out this research. We would also like to thank the Earth Resources Observation and Science Center, United States Geological Survey for providing Landsat satellite data free of charge for this study, and The Urban Unit, Lahore, Bureau of Statistics, Lahore, Department of City and Regional Planning (CRP), University of Engineering & Technology (UET), Lahore and Metropolitan Wing, Lahore Development Authority (LDA), Lahore for their support and providing the secondary data for this research. The authors would also like to thank the reviewers for their insightful comments and valuable suggestions.

Keywords

Driving factors
Land use/land cover change
Multiple scenarios
Neural network
Peri-urban
Urban growth modeling

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.habitatint.2015.09.005

A multi-scale modeling approach...Author Accepted Manuscript -, 1.33 MB

Cite this

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title = "A multi-scale modeling approach for simulating urbanization in a metropolitan region",

abstract = "Metropolitan regions worldwide are experiencing rapid urban growth and the planners often employ prediction models to forecast the future expansion for improving the land management policies and practices. These regions are a mix of urban, peri-urban and rural areas where each sector has its unique expansion properties. This study examines the differences in urban and peri-urban growth characteristics, and their impact at different stages of prediction modeling, in city district Lahore, Pakistan. The analysis of multi-temporal land use/land cover maps revealed that the associations between major land transitions and the factors governing land changes were unique at city district, urban and peri-urban scales. A multilayer perceptron neural network was employed for modeling urbanization, and it was found that the sub-models developed for urban and peri-urban subsets returned better accuracies than those produced at the city district scale. The prediction maps of 2021 and 2035 were also produced through this approach.",

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AU - Bhatti, Saad Saleem

AU - Tripathi, Nitin Kumar

AU - Nitivattananon, Vilas

AU - Rana, Irfan Ahmad

AU - Mozumder, Chitrini

PY - 2015/12/1

Y1 - 2015/12/1

N2 - Metropolitan regions worldwide are experiencing rapid urban growth and the planners often employ prediction models to forecast the future expansion for improving the land management policies and practices. These regions are a mix of urban, peri-urban and rural areas where each sector has its unique expansion properties. This study examines the differences in urban and peri-urban growth characteristics, and their impact at different stages of prediction modeling, in city district Lahore, Pakistan. The analysis of multi-temporal land use/land cover maps revealed that the associations between major land transitions and the factors governing land changes were unique at city district, urban and peri-urban scales. A multilayer perceptron neural network was employed for modeling urbanization, and it was found that the sub-models developed for urban and peri-urban subsets returned better accuracies than those produced at the city district scale. The prediction maps of 2021 and 2035 were also produced through this approach.

AB - Metropolitan regions worldwide are experiencing rapid urban growth and the planners often employ prediction models to forecast the future expansion for improving the land management policies and practices. These regions are a mix of urban, peri-urban and rural areas where each sector has its unique expansion properties. This study examines the differences in urban and peri-urban growth characteristics, and their impact at different stages of prediction modeling, in city district Lahore, Pakistan. The analysis of multi-temporal land use/land cover maps revealed that the associations between major land transitions and the factors governing land changes were unique at city district, urban and peri-urban scales. A multilayer perceptron neural network was employed for modeling urbanization, and it was found that the sub-models developed for urban and peri-urban subsets returned better accuracies than those produced at the city district scale. The prediction maps of 2021 and 2035 were also produced through this approach.

KW - Driving factors

KW - Land use/land cover change

KW - Multiple scenarios

KW - Neural network

KW - Peri-urban

KW - Urban growth modeling

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JO - Habitat International

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ER -

A multi-scale modeling approach for simulating urbanization in a metropolitan region

Abstract

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Keywords

UN SDGs

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Saad Bhatti

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A multi-scale modeling approach for simulating urbanization in a metropolitan region

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UN SDGs

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Saad Bhatti

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