打印

作者

Jianxin Yang;Shengbing Yang;Jingjing Li;Jian Gong;Man Yuan;Jingye Li;Yunzhe Dai;Jing Ye

来源

CITIES,Vol.135,Issue1,Article 104156

语言

英文

关键字

作者单位

Department of Land Resource Management, School of Public Administration, China University of Geosciences, Wuhan 430074, China;Key Labs of Law Evaluation of Ministry of Land and Resources of China, 388 Lumo Road, Hongshan District, Wuhan 430074, China;Collaborative Innovation Center for Emissions Trading system Co-constructed by the Province and Ministry, Hubei University of Economics, Wuhan 430205, China,;School of Low Carbon Economics, Hubei University of Economics, Wuhan 430205, China;Department of Land Resource Management, School of Public Administration, China University of Geosciences, Wuhan 430074, China;Key Labs of Law Evaluation of Ministry of Land and Resources of China, 388 Lumo Road, Hongshan District, Wuhan 430074, China;Collaborative Innovation Center for Emissions Trading system Co-constructed by the Province and Ministry, Hubei University of Economics, Wuhan 430205, China,;School of Low Carbon Economics, Hubei University of Economics, Wuhan 430205, China;State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China;Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;National Tibetan Plateau Data Center, Beijing 100101, China;State Key laboratory of urban and regional Ecology, Research Center for Eco-environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China;University of Chinese Academy of Sciences, Beijing 100049, China;Beijing-Tianjin-Hebei Urban Megaregion National Observation and Research Station for Eco-Environmental Change, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China;Xiongan Institute of Innovation, Xiongan New Area, 071000, China;The Bartlett School of Planning, University College London, Central House, 14 Upper Woburn Place, London WC1H 0NN, UK;School of Urban Design, Wuhan University, No. 299 Bayi Road, Wuchang District, Wuhan 430072, China;Hubei Habitat Environment Research Centre of Engineering and Technology, Wuhan, China;Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8568, Japan;LocationMind Inc., 3-5-2 Iwamotocho, Chiyoda-ku, Tokyo 101-0032, Japan;SUSTech-UTokyo Joint Research Center on Super Smart City, Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China;College of Management and Economics, Tianjin University, Tianjin, China;School of Urban Planning & Design, Peking University, Shenzhen, China;Center for Chinese Public Administration Research, School of Government, Sun Yat-sen University, Guangzhou, Guangdong, China;Department of Management, Macquarie Business School, Macquarie University, Sydney, New South Wales, Australia;School of Economics, Peking University, 5 Summer Palace Road Street, Beijing 100871, China

摘要

Data and skill gaps are common in exploring future urban growth scenarios, especially in developing countries. This study demonstrates how to use distance-related laws regarding process and pattern of urban development to bypass the heavy data and skills required in traditional urban expansion models that rely on various driving factors and machine-learning algorithms. We proposed an urban simulation model that uses three distance-driven components alone to simulate urban expansion by regulating urban morphology at multiple levels. The landscape-level component discriminates the spatiotemporal distribution of urban demand based on rules linked to distance to city centers. The class-level component applies an exponential model with the distance to pre-urbanized patches to control where new urban development may expand. The patch-level component creates urban patches of given sizes with shapes controlled by their distances to the initial patch seeds. Application of the model in Wuhan, China confirms the efficacy of the model and its distance-driven components, as supported by both cell-level agreement and pattern-level similarity. The overarching contribution of DisUSIM lies in providing a distance-driven framework for convenient exploration of urban dynamics in situations where datasets and skills on processing and analyzing driving factors and mechanisms of urban expansion are not readily available.