地理科学 ›› 2021, Vol. 41 ›› Issue (5): 797-803.doi: 10.13249/j.cnki.sgs.2021.05.007

• • 上一篇    下一篇

国外城市系统智能体模型的科学计量分析

李鲁奇1,2(), 孔翔2,*()   

  1. 1.浙江工业大学中国住房和房地产研究院,浙江 杭州 310023
    2.华东师范大学中国现代城市研究中心/华东师范大学城市与区域科学学院,上海 200062
  • 收稿日期:2020-02-16 修回日期:2020-07-11 出版日期:2021-05-10 发布日期:2021-07-15
  • 通讯作者: 孔翔 E-mail:liluqi@outlook.com;xkong@bs.ecnu.edu.cn
  • 作者简介:李鲁奇(1991−),男,山东宁阳人,博士,主要从事城市网络与空间模拟研究。E-mail: liluqi@outlook.com
  • 基金资助:
    国家自然科学基金项目(41771156);中央高校基本科研业务费项目——华东师范大学共享交叉基金(人文社会科学)项目资助(2019ECNU-GXJC002)

A Scientometric Analysis on the Agent-based Modelling of Cities as Complex Systems

Li Luqi1,2(), Kong Xiang2,*()   

  1. 1. China Academy of Housing and Real Estate, Zhejiang University of Technology, Hangzhou 310023, Zhejiang, China
    2. The Centre for Modern Chinese City Studies/School of Urban and Regional Science, East China Normal University, Shanghai 200062, China
  • Received:2020-02-16 Revised:2020-07-11 Online:2021-05-10 Published:2021-07-15
  • Contact: Kong Xiang E-mail:liluqi@outlook.com;xkong@bs.ecnu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(41771156);Shared Cross Fund of the Fundamental Research Funds for the Central Universities and East China Normal University (Humanities and Social Sciences)(2019ECNU-GXJC002)

摘要:

智能体模型用于自下而上模拟城市系统。当前综述性研究多关注其原理和缺陷等,而对研究内容演化的梳理尚不够细致。故运用主路径和冲积图分析,基于文献引用网络和关键词共现网络,梳理了国外城市系统智能体模型的研究脉络。结果表明,土地利用是核心研究领域,居住隔离、城市增长和交通等亦是重要应用主题;元胞自动机、网络分析等方法在2008年前即与该模型结合,遗传算法、大数据分析等在2016—2019年亦得到较多关注。未来可结合韧性城市、收缩城市等热点问题,以及开发区、城中村等中国特色城市问题扩展应用领域,并深化与人工智能算法和各学科传统方法的结合。

关键词: 智能体模型, 科学计量, 城市, 复杂系统, 土地利用

Abstract:

Agent-based modelling is a method for simulating urban systems from the bottom up. It allows for the simulation of human behaviours in certain urban environments, as well as the change of the urban environment caused by these behaviours. In this context, it has been widely used to investigate various problems pertaining to urban systems, such as urban land development, socio-spatial differentiation, and new town construction. A few literature reviews can be found, but they focus more on the principles, techniques, and deficiencies of ABM in studying urban systems, rather than a detailed and quantitative description of the research trajectories. Consequently, the application prospects of this method in urban systems are difficult to be further revealed. Against this backdrop, this study uses scientometric methods such as main path analysis and alluvial diagram analysis, as well as the data of literature citation network and keyword co-occurrence network to depict the research trajectories of agent-based modelling of urban systems. The result shows that land use is the primary focus of these studies. This research field consists of three stages: preliminary simulation of land use, the detailed simulation of the land market mechanism, and diversified applications. In the first stage, scholars began to use agent-based modelling to analyse urban land use, but the modelling approach was diverse and the rules for the agents’ behaviours were relatively simple. At the same time, they have yet not focused on the mechanism behind the land market. In the second stage, researchers focused more on the in-depth analysis and detailed simulation of the mechanism of the land markets. In the third stage, the studies have been becoming diversified again, focusing on the simulation of the land market, the reflection of agent-based modelling and the development of simulation systems for planners and the public. As for the research fields of agent-based modelling, recent literature from 2016 to 2019 focuses primarily on the following categories: Planning and industries, land use and urban growth, complexity and transportation, cellular automate and residential space, disaster and network, and others. These research fields are by no means fixed. Rather, they are highly flexible, intersecting with each other and having been undergoing continuous reconstruction. Among them, segregation and land use have received attention in early studies; urban growth, planning, transportation and disaster were studied in subsequent research; creative industries, accessibility, etc. are emerging research fields that received attention in recent years. In terms of the methods that have been combined with agent-based modelling, cellular automata, GIS and network analysis were combined with agent-based modelling in earlier studies. Besides, genetic algorithms, reinforcement learning models, and big data have also been used in agent-based simulation recently. In future research, studies on problems such as resilience city and the shrinking city can be integrated with agent-based modelling; some phenomenon specific to China, such as development zones and urban villages, can also be studied using this method. At the same time, future research can further focus on the combination of agent-based modelling with various artificial intelligence algorithms and traditional methods from pertinent subjects.

Key words: agent-based modelling, scientometrics, cities, complex system, land use