“三规合一”服务的空间信息技术:地理模拟与优化

“三规合一”服务的空间信息技术:地理模拟与优化 Spatial information technology for facilitating "three-plan integration" using geographical simulation and optimization 黎夏 LI Xia 1 first-author 刘小平 LIU Xiaoping 1 中山大学地理科学与规划学院广东省城市化与地理环境空间模拟重点实验室 中山大学地理科学与规划学院广东省城市化与地理环境空间模拟重点实验室 Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning,Sun Yat-sen University Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning,Sun Yat-sen University 中国的国民经济和社会发展规划、土地利用总体规划以及城乡规划都是法定规划,但由于规划主体、技术标准和编制办法、实施手段和监督机制等的不同,导致"三规分离"、各个规划之间相互冲突的问题较为突出。虽然国家为了消除冲突,正在开展"三规合一"的有关工作,但缺乏有关技术手段的支持。本文以地理信息科学为出发点,对地理过程建模在国内外研究中的应用进行了总结,阐述了地理模拟与优化的框架体系可以成为目前中国正在进行的"三规合一"工作的重要理论和方法支撑。 Economic and social development planning, urban planning, and land use planning are statutory plans in China. However, China faces a "three planning separation" problem because these plans have different planning principles, technical standards, and approaches,thereby resulting in conflicts. To eliminate such conflicts, the "three-plan integration" program was introduced in China in 2013. Many cities, such as Guangzhou, Shanghai, and Xiamen, have attempted to achieve such integration. As a basic technology for solving problems related to "three planning separation," the geographic information system has been very helpful in the quantitative analyses of spatial information. Apart from traditional spatial analysis, combining GIS with the location-allocation model, cellular automata（CA）, or multi-agent model provides an innovative alternative in the quantitative analysis of the decisions made in urban planning. The geographical simulation andoptimization applications not only simulate and optimize the land use systems in complex environments but also provide sufficient information for preparing planning scenarios. However, because of the lack of theoretical and practical support, these applications remain in the primary stage. Therefore, advanced GIS analytic models must be developed to devise effective methodologies for integrating the three aforementioned plans. In this paper, we summarized the geographical simulation and optimization applications from the perspective of geographic information science from national and international studies. The Geographical Simulation and Optimization System（GeoSOS） comprises three components, namely, CA, Multi-Agent Systems（MAS）, and swarm intelligence. This system compensates for the weakness of the general GIS software, which cannot perform advanced spatial analyses, and satisfies the demands of complex simulation and optimization. We reviewed several geographical simulation and optimization methods, including CA, MAS, ant colony optimization, and Particle Swarm Optimization（PSO）. We also summarized the GeoSOS applications related to planning ecological control, urban growth boundary, and permanent basic farmland protection. GeoSOS technologies have been proven to be capable of solving the problems encountered in these applications. The framework associated with geographical simulation and optimization has been used as the theoretical and methodological support of the "three-plan integration." This framework aims to provide various techniques, such as the Pareto strategy, Pareto simulated annealing,non-dominated sorting genetic algorithm, multi-objective PSO, and multi-objective immune system algorithm, for solving the multi-objective optimization problem in "three-plan integration." High-resolution land use imageries have been increasingly used for solving planning problems. A very large data volume must be used when various sources of spatial data are used in implementing large-scale simulation. However, previous studies have utilized high-performance computation techniques for geographical simulation and optimization, for establishing eco-designated line of control, and for generating predictions and early warnings of illegal development. 三规合一 元胞自动机 多智能体 地理模拟与优化系统（GeoSOS） three-plan integration cellular automata multi-agents Geographical Simulation and Optimization System（GeoSOS） TU98;P208 国家自然科学基金（编号:41531176,41371376）~~ 2021-06-09 5 20