TY - JOUR
T1 - A multi-scale modeling approach for simulating urbanization in a metropolitan region
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
UR - http://www.scopus.com/inward/record.url?scp=84942747299&partnerID=8YFLogxK
U2 - 10.1016/j.habitatint.2015.09.005
DO - 10.1016/j.habitatint.2015.09.005
M3 - Article
AN - SCOPUS:84942747299
SN - 0197-3975
VL - 50
SP - 354
EP - 365
JO - Habitat International
JF - Habitat International
ER -