title: Scenario prediction of emerging coastal city using CA modeling under different environmental conditions: a case study of Lingang New City, China.
abstract: The world's coastal regions are experiencing rapid urbanization coupled with increased risk of ecological damage and storm surge related to global climate and sea level rising. This urban development issue is particularly important in China, where many emerging coastal cities are being developed. Lingang New City, southeast of Shanghai, is an excellent example of a coastal city that is increasingly vulnerable to environmental change. Sustainable urban development requires planning that classifies and allocates coastal lands using objective procedures that incorporate changing environmental conditions. In this paper, we applied cellular automata (CA) modeling based on self-adaptive genetic algorithm (SAGA) to predict future scenarios and explore sustainable urban development options for Lingang. The CA model was calibrated using the 2005 initial status, 2015 final status, and a set of spatial variables. We implemented specific ecological and environmental conditions as spatial constraints for the model and predicted four 2030 scenarios: (a) an urban planning-oriented Plan Scenario; (b) an ecosystem protection-oriented Eco Scenario; (c) a storm surge-affected Storm Scenario; and (d) a scenario incorporating both ecosystem protection and the effects of storm surge, called the Ecostorm Scenario. The Plan Scenario has been taken as the baseline, with the Lingang urban area increasing from 45.8 km(2) in 2015 to 66.8 km(2) in 2030, accounting for 23.9 % of the entire study area. The simulated urban land size of the Plan Scenario in 2030 was taken as the target to accommodate the projected population increase in this city, which was then applied in the remaining three development scenarios. We used CA modeling to reallocate the urban cells to other unconstrained areas in response to changing spatial constraints. Our predictions should be helpful not only in assessing and adjusting the urban planning schemes for Lingang but also for evaluating urban planning in coastal cities elsewhere.
authors: College of Marine Sciences, Shanghai Ocean University, Shanghai, 201306, China. yjfeng@shou.edu.cn.|The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources (Ministry of Education), Shanghai Ocean University, Shanghai, 201306, China. yjfeng@shou.edu.cn.
authors: Yongjiu
authors: http://orcid.org/0000-0001-8772-7218
authors: Y
authors: Feng
authors: School of Geography, Planning and Environmental Management, The University of Queensland, Brisbane, QLD, 4072, Australia.
authors: Yan
authors: Y
authors: Liu
mesh_terms: D002681:China; D002947:Cities; D002958:City Planning / Q000379:methods*; D002980:Climate; D003247:Conservation of Natural Resources; D004463:Ecology; D017753:Ecosystem; D005544:Forecasting; D008962:Models, Theoretical; D014506:Urban Renewal; D014507:Urbanization
keywords: Cellular automata (CA); Coastal city; Lingang New City; Scenario prediction; Self-adaptive genetic algorithm (SAGA); Spatial constraints