顾及空间多尺度邻域效应和时间依赖性的城市扩展模拟

doi:10.11947/j.AGCS.2023.20220244

测绘学报 ›› 2023, Vol. 52 ›› Issue (10): 1772-1783.doi: 10.11947/j.AGCS.2023.20220244

顾及空间多尺度邻域效应和时间依赖性的城市扩展模拟

王海军^1,2, 常瑞寒¹, 李启源¹, 周晓艳¹, 王权¹, 曾浩然¹, 刘一宁^2,3, 岳照溪^2,3

1. 武汉大学资源与环境科学学院, 湖北武汉 430079;
2. 自然资源部超大城市自然资源时空大数据分析应用重点实验室, 上海 200063;
3. 上海市测绘院, 上海 200063

收稿日期:2022-04-11 修回日期:2022-08-23 发布日期:2023-10-31
通讯作者: 常瑞寒 E-mail:3103138932@qq.com
作者简介:王海军(1972-),男,博士,教授,主要从事地理模拟、国土空间规划、城市规划和土地资源评价研究。E-mail:landgiswhj@163.com
基金资助:
自然资源部超大城市自然资源时空大数据分析应用重点实验室开放基金(KFKT-2022-10);国家自然科学基金(42171411)

Considering the spatial multi-scale neighborhood effect and time dependence into cellular automata model for urban growth simulation

WANG Haijun^1,2, CHANG Ruihan¹, LI Qiyuan¹, ZHOU Xiaoyan¹, WANG Quan¹, ZENG Haoran¹, LIU Yining^2,3, YUE Zhaoxi^2,3

1. School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China;
2. Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, MNR, Shanghai 200063, China;
3. Shanghai Surveying and Mapping Institute, Shanghai 200063, China

Received:2022-04-11 Revised:2022-08-23 Published:2023-10-31
Supported by:
Funded by Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, MNR (No. KFKT-2022-10);The National Natural Science Foundation of China (No. 42171411)

摘要/Abstract

摘要： 在推进新型城镇化和实施新时代国土空间规划的战略背景下,城市扩展研究逐渐成为热点问题。当前基于元胞自动机(CA)的城市扩展模拟对城市空间多尺度邻域效应解析不足,且在转换规则中对城市长时间演变过程的时间依赖性影响表达不够完善,简化了城市扩展的时空依赖性,无法真实模拟推演未来规划实施情景以服务于国土空间规划。针对上述问题,本文构建一种兼顾空间多尺度邻域效应(3DCNN)和时间依赖性(ConvLSTM)的城市扩展深度学习CA模型(下文称“Deep-CA”)。首先通过组合普通卷积和空洞卷积的3DCNN来提取城市空间多尺度邻域效应,再利用ConvLSTM神经网络将历史信息同化,考虑长时间序列的时间依赖性,从而得到城市扩展的适宜性概率。北京市1995—2015年的土地利用数据及其驱动因素数据用于验证所提CA模型的科学性与适用性,1995—2010年数据用于模型训练,模拟2015年的城市范围。同时将模拟结果精度与ANN-CA、LR-CA和ME-CA 3种传统方法进行对比。与传统CA模型相比,Deep-CA的北京市2015年模拟FoM指数提高了4%左右,且对于城市全局和局部形态模拟效果较好,斑块破碎度低。试验结果表明,Deep-CA可以准确获取长期时空依赖关系,从而进一步提高城市扩展CA模型的模拟真实性。

关键词: 城市扩展, 空间多尺度邻域效应, 时间依赖性, 深度学习, 元胞自动机

Abstract: Under the strategic background of promoting new-type urbanization and implementing territorial space planning in the new era, urban growth research has gradually become a hot issue. The current urban growth simulation based on cellular automata (CA) lacks the analysis of multi-scale neighborhood effect of urban space, and the expression of time-dependent influence of long-term urban evolution process in transformation rules is not perfect, which simplifies the spatio-temporal dependence of urban growth. Future planning implementation scenarios cannot be simulated and deduced to serve national spatial planning. To solve the above problems, this paper constructs a CA model of urban growth (hereinafter referred to as Deep-CA), which takes spatial multi-scale neighborhood effect (3DCNN) and time dependence into account (ConvLSTM). Firstly, 3DCNN combining ordinary convolution and empty convolution is used to extract the multi-scale neighborhood effect of urban space. Then, ConvLSTM neural network is used to assimilate historical information, and the time dependence of long time series is considered. Thus, the suitability probability of urban growth is obtained. The land use data and its driving factors in Beijing from 1995 to 2015 are used to verify the scientific nature and applicability of the proposed CA model. The data from 1995 to 2010 are used for model training to simulate the urban scope in 2015. At the same time, the accuracy of simulation results is compared with the three traditional methods: artificial neural network-CA, logistic regression-CA and maximum entropy-CA. Compared with the traditional CA model, the simulated FoM index of Deep-CA in Beijing increases by about 4% in 2015, the simulation effect of Deep-CA on urban global and local morphology is good, and the patch fragmentation is low. The experimental results show that Deep-CA can accurately obtain long-term spatio-temporal dependence, thus further improving the simulation authenticity of urban growth CA model.

Key words: urban growth, spatial multi-scale neighborhood effect, time dependence, deep learning, cellular automata

中图分类号:

P208

王海军, 常瑞寒, 李启源, 周晓艳, 王权, 曾浩然, 刘一宁, 岳照溪. 顾及空间多尺度邻域效应和时间依赖性的城市扩展模拟[J]. 测绘学报, 2023, 52(10): 1772-1783.

WANG Haijun, CHANG Ruihan, LI Qiyuan, ZHOU Xiaoyan, WANG Quan, ZENG Haoran, LIU Yining, YUE Zhaoxi. Considering the spatial multi-scale neighborhood effect and time dependence into cellular automata model for urban growth simulation[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(10): 1772-1783.

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顾及空间多尺度邻域效应和时间依赖性的城市扩展模拟

Considering the spatial multi-scale neighborhood effect and time dependence into cellular automata model for urban growth simulation

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