精细刻画城镇自组织扩展过程的CA-ABM耦合模拟及预测模型

doi:10.11947/j.AGCS.2024.20230410

测绘学报 ›› 2024, Vol. 53 ›› Issue (7): 1429-1443.doi: 10.11947/j.AGCS.2024.20230410

精细刻画城镇自组织扩展过程的CA-ABM耦合模拟及预测模型

张彬¹^,²^,³(), 胡守庚¹^,²(), 王海军⁴, 郭颖⁵, 童陆亿¹^,², 夏天顺¹

^1.中国地质大学（武汉）公共管理学院，湖北　武汉　430074
^2.自然资源部法治研究重点实验室，湖北　武汉　430074
^3.粤港澳智慧城市联合实验室，广东　深圳　518000
^4.武汉大学资源与环境科学学院，湖北　武汉　430079
^5.武汉大学图书馆，湖北　武汉　430072

收稿日期:2023-09-14 发布日期:2024-08-12
通讯作者: 胡守庚 E-mail:binzhang94@163.com;hushougeng@cug.edu.cn
作者简介:张彬（1994—），男，博士，副教授，研究方向为土地利用变化分析与模拟。E-mail：binzhang94@163.com
基金资助:
湖北省自然科学基金(2023AFB022);教育部人文社会科学研究项目(23YJC630223);广东省科技创新战略专项资金（粤港澳联合实验室）(2020B1212030009);国家自然科学基金(42171272);中国地质大学（武汉）“地大学者”人才岗位科研启动经费资助(2022129)

A CA-ABM-coupled simulation and prediction model for finely depicting the local self-organization process of urban expansion

Bin ZHANG¹^,²^,³(), Shougeng HU¹^,²(), Haijun WANG⁴, Ying GUO⁵, Luyi TONG¹^,², Tianshun XIA¹

^1.School of Public Administration, China University of Geosciences, Wuhan 430074, China
^2.Key Laboratory of the Ministry of Natural Resources for Research on Rule of Law, Wuhan 430074, China
^3.Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen 518000, China
^4.School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
^5.Wuhan University Library, Wuhan 430072, China

Received:2023-09-14 Published:2024-08-12
Contact: Shougeng HU E-mail:binzhang94@163.com;hushougeng@cug.edu.cn
About author:ZHANG Bin (1994—), male, PhD, associate professor, majors in land use change analysis and simulation. E-mail: binzhang94@163.com
Supported by:
Natural Science Foundation of Hubei Province(2023AFB022);Ministry of Education of Humanities and Social Science Project(23YJC630223);Guangdong Science and Technology Strategic Innovation Fund (the Guangdong-Hong Kong-Macau Joint Laboratory Program)(2020B1212030009);The National Natural Science Foundation of China(42171272);The “CUG Scholar” Scientific Research Funds at China University of Geosciences (Wuhan)(2022129)

摘要/Abstract

摘要：

城镇扩展模拟预测是支撑国土空间规划、实现城镇化高质量发展的重要手段，提高其科学性和实用性有助于精确把握城镇扩展趋势，对国土资源的合理和可持续利用意义重大。现有以元胞自动机（CA）为基础的模型着重于刻画空间因子驱动的城镇自然扩展过程，智能体模型（ABM）则在模拟城镇自组织扩展方面具有理论优势，而且已有的耦合方法多为基于模拟步骤的模型级联，难以实现二者紧密耦合以充分发挥其在模拟城镇自然和自组织扩展方面的优势。本文基于CA的结构开放性和ABM的理论优势，以可达性为媒介表征城镇扩展局部自组织过程的范围差异，并依据博弈论设计利益相关者之间的局部自组织规则，将ABM刻画的人地交互决策融入CA邻域构建，建立一种可以精细刻画城镇自组织扩展过程的CA-ABM耦合模拟及预测模型（CA-ABM-LSO），实现CA和ABM在底层结构的深度集成和城镇自然与自组织扩展过程的耦合模拟。以武汉市为例对其进行应用，结果表明：基于CA和ABM的优势互补特点进行耦合建模，能够充分发挥二者在刻画城镇自然和自组织扩展方面的优势，显著提升城镇扩展模拟精度，并改善模拟城镇斑块的景观形态；基于博弈论的局部自组织规则可有效实现宏观经济政策对微观智能体交互决策的指导，提高城镇扩展模拟的科学性和规划实用性；预计至2035年，武汉市城镇扩展的重点区域集中在高新科技园区和运输集散中心附近，这符合“武鄂黄黄”都市圈的发展预期，可为其空间规划提供决策支持。

关键词: 城镇扩展, 元胞自动机, 智能体, 自组织

Abstract:

Urban expansion simulation and prediction are vital for supporting national spatial planning and promoting sustainable urbanization. Improving its scientific and practical applicability is essential to accurately capturing urban expansion trends and sustainable land resource utilization. Present cellular automata (CA) models focus on describing spatially driven urban expansion, while those using the agent-based model (ABM) approach provide theoretical benefits in simulating self-organized urban expansion. Moreover, existing coupling methods predominantly cascade these models based on simulation steps, presenting challenges in deeply integrating them to unleash their potential for simulating both natural and self-organized urban expansion. This study is grounded in the structural openness of CA and the theoretical strengths of ABM. It uses accessibility as an intermediary to define scope variations in local self-organized processes during urban expansion. Additionally, it devises rules for local self-organization to model stakeholder interactions using game theory principles. Then this study integrates the human-land interactions portrayed by ABM, guided by the defined scope and rules, into the CA neighborhood construction. This approach leads to the creation of a CA-ABM-coupled urban expansion simulation and prediction model with a fine depiction of local self-organization processes (CA-ABM-LSO). This model revolves around finely defined localized self-organization and achieves a deep integration of CA and ABM within the foundational structure, which enables a coupled simulation of natural and self-organizational urban expansion processes. Using Wuhan as a case study, the results show that the CA-ABM-LSO effectively leverages its capabilities to depict both natural and self-organized urban expansion. This enhancement significantly improves urban expansion simulation accuracy and refines the landscape patterns of simulated urban patches. Rules based on game theory that govern local self-organization can effectively guide the behaviors of micro-agents through macro-economic policies, which can strengthen the scientific robustness and planning viability of urban expansion simulations. Expected by 2035, the key areas for urban expansion in Wuhan are predicted to concentrate near high-tech zones and transportation hubs, which aligns with the planning of the “Wu-E-Huang-Huang” metropolis and would provide valuable foundational insights for its land resource management.

Key words: urban expansion, cellular automata, agent, self-organization

中图分类号:

P208

张彬, 胡守庚, 王海军, 郭颖, 童陆亿, 夏天顺. 精细刻画城镇自组织扩展过程的CA-ABM耦合模拟及预测模型[J]. 测绘学报, 2024, 53(7): 1429-1443.

Bin ZHANG, Shougeng HU, Haijun WANG, Ying GUO, Luyi TONG, Tianshun XIA. A CA-ABM-coupled simulation and prediction model for finely depicting the local self-organization process of urban expansion[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(7): 1429-1443.

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数据类别	数据格式	时间/年	来源
土地利用	栅格	2000、2020	http://www.globeland30.org
市/区县/乡镇行政中心	矢量点	2020	http://map.baidu.com
路网	矢量线	2020	http://www.openstreetmap.org
海拔/坡度	栅格	2019	http://www.earthdata.nasa.gov
兴趣点	矢量点	2020	http://map.baidu.com
住宅小区房价	矢量面	2023	http://wuhan.fang.com
规划图集	图件	2018—2035	http://zrzyhgh.wuhan.gov.cn

空间驱动变量	变量含义
dis2CC	距市中心的距离
dis2DC	距区中心的距离
dis2TC	距乡镇中心的距离
dis2TRW	距主干道的距离
dis2PRW	距一级道路的距离
dis2SEW	距二级道路的距离
dis2TEW	距三级道路的距离
dis2RST	距火车站的距离
dis2BST	距长途汽车站的距离
dis2MST	距地铁站的距离
dis2BSP	距公交车站的距离
dis2WAB	距水域的距离
ELE	海拔高度
SLP	坡度

交通相关评价变量	变量含义	评价权重
den_TRW	主干道路密度	0.073 1
den_PRW	一级道路密度	0.051 1
den_SEW	二级道路密度	0.041 4
den_TEW	三级道路密度	0.034 6
den_RST	火车站密度	0.110 1
den_BST	长途汽车站密度	0.089 9
den_MST	地铁站密度	0.089 4
den_BSP	公交车站密度	0.032 9
den_EEW	快速路出入口密度	0.095 9
den_ESW	高速出入口密度	0.101 3
den_CRO	十字路口密度	0.050 8
den_FLY	立交桥密度	0.089 4
den_GST	加油站密度	0.063 1
den_PAL	停车场密度	0.077 0

评价维度	层次分析法权重	熵权法权重	最终权重
生活服务（密度）	0.324 4	0.510 4	0.417 4
交通服务（密度）	0.081 6	0.473 4	0.277 5
教育机构（距离）	0.042 6	0.004 2	0.023 4
医疗机构（距离）	0.187 9	0.005 5	0.096 7
城市绿地（距离）	0.363 5	0.006 5	0.185 0

模型	FoM	NP	LPI	LSI
CA	0.353 7	10 172	0.037 1	41.242 6
ABM	0.354 3	7060	0.036 5	31.889 8
CA-ABM	0.419 0	6721	0.041 9	29.418 1
CA-ABM-LSO	0.469 2	6648	0.065 4	28.265 0

精细刻画城镇自组织扩展过程的CA-ABM耦合模拟及预测模型

A CA-ABM-coupled simulation and prediction model for finely depicting the local self-organization process of urban expansion

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K（α=β=γ=0.5）	发生交互决策的迭代次数	α（β=0.5，γ=0.5）	改变政府决策的新增城镇元胞数量	β（α=0.5）	改变开发商决策的新增城镇元胞数量	γ（α=0.5）	改变居民决策的新增城镇元胞数量
1	40	0.1	37 5870	0.9	166 019	0.1	20 821
3	40	0.2	382 874	0.8	165 300	0.2	21 988
5	40	0.3	384 946	0.7	166 350	0.3	22 030
7	40	0.4	386 965	0.6	166 183	0.4	22 074
9	36	0.5	387 971	0.5	166 971	0.5	22 065
11	32	0.6	387 970	0.4	166 483	0.6	22 295
13	27	0.7	389 124	0.3	165 453	0.7	22 359
15	14	0.8	389 122	0.2	163 405	0.8	22 802
17	3	0.9	389 627	0.1	159 049	0.9	22 917
19	0	1.0	389 082	—	—	—	—

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