耦合FLUS和Markov的快速发展城市土地利用空间格局模拟方法

doi:10.12082/dqxxkx.2022.210359

地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (1): 100-113.doi: 10.12082/dqxxkx.2022.210359

• 地球信息科学理论与方法 • 上一篇下一篇

耦合FLUS和Markov的快速发展城市土地利用空间格局模拟方法

王旭东¹(), 姚尧¹^,²^,^*(), 任书良¹, 史绪国¹

1.中国地质大学（武汉）地理与信息工程学院,武汉 430078
2.阿里巴巴集团,杭州 311121

收稿日期:2021-06-28 修回日期:2021-09-22 出版日期:2022-01-25 发布日期:2022-03-25
通讯作者: * 姚尧（1987—）,男,广东梅州人,副教授,研究方向为空间大数据和城市计算。E-mail: yaoy@cug.edu.cn
作者简介:王旭东（1998— ）,男,湖北荆州人,硕士生,主要从事地面沉降和城市发展驱动机制研究。E-mail: wxd@cug.edu.cn
基金资助:
国家自然科学基金项目(41801306);国家重点研发计划项目(2019YFB2102903)

A Coupled FLUS and Markov Approach to Simulate the Spatial Pattern of Land Use in Rapidly Developing Cities

WANG Xudong¹(), YAO Yao¹^,²^,^*(), REN Shuliang¹, SHI Xuguo¹

1. School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
2. Alibaba Group, Hangzhou 311121, China

Received:2021-06-28 Revised:2021-09-22 Online:2022-01-25 Published:2022-03-25
Contact: YAO Yao
Supported by:
National Natural Science Foundation of China(41801306);National Key Research and Development Program of China(2019YFB2102903)

摘要/Abstract

摘要：

模拟城市土地利用空间变化格局的研究,对未来区域规划以及实现可持续发展具有十分积极的作用。以往基于FLUS的研究栅格尺度较大,如何模拟快速发展中城市的复杂土地利用变化过程,挖掘土地利用变化驱动机制值得进一步探讨。本文构建了耦合FLUS和Markov的城市土地利用格局拟合框架,创新性地引入房价指标表征社会经济属性,以深圳为研究区,基于30 m空间分辨率小栅格尺度的土地利用分类数据和基础地理、路网河网、感兴趣点等多源空间变量,模拟不同发展情景下的未来城市土地利用空间格局,并通过随机森林进行土地利用变化驱动因素分析。研究结果表明:本文提出的耦合FLUS和Markov方法相较于传统CA模型（RFA-CA和Logistic-CA）精度更高（FoM=0.22）,能更准确地模拟快速发展中城市的土地利用变化过程;多情景土地利用格局制图结果验证了城市发展过程中生态控制线的重要性,进一步说明本文拟合框架在未来城市规划布局中的参考价值;医院、娱乐场所等的基础设施和公交、路网密度等的基础交通比自然因素（高程、坡度）对城市发展的影响更大,到海岸线距离会在一定程度上限制深圳内部土地利用变化过程。本研究所构建模型及精细制图结果,可为城市区域规划和空间格局模拟等相关研究提供参考依据和理论基础。

关键词: 多源数据, 快速发展中城市, 土地利用格局, 马尔可夫模型, 未来土地利用模型, 情景设置, 土地利用空间格局模拟, 土地利用变化驱动机制

Abstract:

Modeling urban land use change is important for future regional planning and sustainable development. Previous FLUS-based studies are mostly based on larger grid scales. How to simulate the complex land use change processes in rapidly developing cities and explore the driving mechanisms of land use change still need further exploration. This paper constructs an urban land use pattern simulation framework coupled with FLUS and Markov and innovatively introduces house price to characterize socio-economic attributes. We take Shenzhen as the study area to simulate future urban land use spatial patterns under different development scenarios based on small grid scale (30 m) land use classification data and multi-source spatial variables such as basic geography data, road and river networks, and point-of-interest data. Finally, we analyze the land use change drivers using random forest models. The results show that the coupled FLUS and Markov method proposed in this paper has higher accuracy (FoM = 0.22) and simulate the land use change processes more accurately in rapidly developing cities, compared to traditional CA models (RFA-CA and Logistic-CA). The mapping results of multi-scenario land use patterns verify the importance of ecological control lines in the process of urban development, further illustrating the reference value of the proposed simulation framework for future urban planning layout. Hospital infrastructure, entertainment venues, and bus stop, road network density have a greater impact on urban development than natural factors (e.g., elevation, slope), while the distance to coastline limits land use change processes to some extent within Shenzhen. The model constructed in this study and the fine mapping results can provide a reference basis and theoretical foundation for related research on urban regional planning and spatial pattern simulation.

Key words: multi-source data, rapidly developing cities, land use patterns, Markov model, FLUS model, scenario settings, simulation of land use spatial patterns, land use change driving mechanisms

王旭东, 姚尧, 任书良, 史绪国. 耦合FLUS和Markov的快速发展城市土地利用空间格局模拟方法[J]. 地球信息科学学报, 2022, 24(1): 100-113.DOI:10.12082/dqxxkx.2022.210359

WANG Xudong, YAO Yao, REN Shuliang, SHI Xuguo. A Coupled FLUS and Markov Approach to Simulate the Spatial Pattern of Land Use in Rapidly Developing Cities[J]. Journal of Geo-information Science, 2022, 24(1): 100-113.DOI:10.12082/dqxxkx.2022.210359

图/表 13

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参考文献 40

[1]	De Palma A, Sanchez-Ortiz K, Martin P A, et al. Chapter four - challenges with inferring how land-use affects terrestrial biodiversity: study design, time, space and synthesis[A]. In: Bohan D A, Dumbrell A J, Woodward G, et al. Advances in Ecological Research[M]. Salt Lake City: Academic Press, 2018, 58:163-199. DOI: 10.1016/bs.aecr.2017.12.004 doi: 10.1016/bs.aecr.2017.12.004
[2]	Galbraith S M, Hall T E, Tavárez H S, et al. Local ecological knowledge reveals effects of policy-driven land use and cover change on beekeepers in Costa Rica[J]. Land Use Policy, 2017, 69:112-122. DOI: 10.1016/j.landusepol.2017.08.032 doi: 10.1016/j.landusepol.2017.08.032
[3]	Jayne T S, Chamberlin J, Headey D D. Land pressures, the evolution of farming systems, and development strategies in Africa: a synjournal[J]. Food Policy, 2014, 48:1-17. DOI: 10.1016/j.foodpol.2014.05.014 doi: 10.1016/j.foodpol.2014.05.014
[4]	Brodie J, Wolanski E, Lewis S, et al. An assessment of residence times of land-sourced contaminants in the Great Barrier Reef lagoon and the implications for management and reef recovery[J]. Marine Pollution Bulletin, 2012, 65(4-9):267-279. DOI: 10.1016/j.marpolbul.2011.12.011 doi: 10.1016/j.marpolbul.2011.12.011 pmid: 22284702
[5]	Liu X P, Ma L, Li X, et al. Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata[J]. International Journal of Geographical Information Science, 2014, 28(1):148-163. DOI: 10.1080/13658816.2013.831097 doi: 10.1080/13658816.2013.831097
[6]	White R, Engelen G. High-resolution integrated modelling of the spatial dynamics of urban and regional systems[J]. Computers, Environment and Urban Systems, 2000, 24(5):383-400. DOI: 10.1016/S0198-9715(00)00012-0 doi: 10.1016/S0198-9715(00)00012-0
[7]	Clarke K C, Hoppen S, Gaydos L. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay Area[J]. Environment and Planning B: Planning and Design, 1997, 24(2):247-261. DOI: 10.1068/b240247 doi: 10.1068/b240247
[8]	Li X, Yeh A G. Modelling sustainable urban development by the integration of constrained cellular automata and GIS[J]. International Journal of Geographical Information Science, 2000, 14(2):131-152. DOI: 10.1080/136588100240886 doi: 10.1080/136588100240886
[9]	Mitsova D, Shuster W, Wang X H. A cellular automata model of land cover change to integrate urban growth with open space conservation[J]. Landscape and Urban Planning, 2011, 99(2):141-153. DOI: 10.1016/j.landurbplan.2010.10.001 doi: 10.1016/j.landurbplan.2010.10.001
[10]	Lau K H, Kam B H. A cellular automata model for urban land-use simulation[J]. Environment and Planning B: Planning and Design, 2005, 32(2):247-263. DOI: 10.1068/b31110 doi: 10.1068/b31110
[11]	Wu D Q, Liu J, Wang S J, et al. Simulating urban expansion by coupling a stochastic cellular automata model and socioeconomic indicators[J]. Stochastic Environmental Research and Risk Assessment, 2010, 24(2):235-245. DOI: 10.1007/s00477-009-0313-3 doi: 10.1007/s00477-009-0313-3
[12]	Li X, Yeh A G. Neural-network-based cellular automata for simulating multiple land use changes using GIS[J]. International Journal of Geographical Information Science, 2002, 16(4):323-343. DOI: 10.1080/13658810210137004 doi: 10.1080/13658810210137004
[13]	Kamusoko C, Gamba J. Simulating urban growth using a Random Forest-cellular Automata (RF-CA) model[J]. ISPRS International Journal of Geo-Information, 2015, 4(2):447-470. DOI: 10.3390/ijgi4020447 doi: 10.3390/ijgi4020447
[14]	Li X, Lin J Y, Chen Y M, et al. Calibrating cellular automata based on landscape metrics by using genetic algorithms[J]. International Journal of Geographical Information Science, 2013, 27(3):594-613. DOI: 10.1080/13658816.2012.698391 doi: 10.1080/13658816.2012.698391
[15]	Feng Y J, Liu Y, Batty M. Modeling urban growth with GIS based cellular automata and least squares SVM rules: A case study in Qingpu: Songjiang area of Shanghai, China[J]. Stochastic Environmental Research and Risk Assessment, 2016, 30(5):1387-1400. DOI: 10.1007/s00477-015-1128-z doi: 10.1007/s00477-015-1128-z
[16]	Lin J Y, Li X. Simulating urban growth in a metropolitan area based on weighted urban flows by using web search engine[J]. International Journal of Geographical Information Science, 2015, 29(10):1721-1736. DOI: 10.1080/13658816.2015.1034721 doi: 10.1080/13658816.2015.1034721
[17]	Liu X P, Liang X, Li X, et al. A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects[J]. Landscape and Urban Planning, 2017, 168:94-116. DOI: 10.1016/j.landurbplan.2017.09.019 doi: 10.1016/j.landurbplan.2017.09.019
[18]	朱寿红, 舒帮荣, 马晓冬, 等. 基于"反规划"理念及FLUS模型的城镇用地增长边界划定研究——以徐州市贾汪区为例[J]. 地理与地理信息科学, 2017, 33(5):80-86.
	[ Zhu S H, Shu B R, Ma X D, et al. The delimitation of urban growth boundary based on the idea of “anti-planning” and FLUS model: A case study of Jiawang district, Xuzhou city[J]. Geography and Geo-Information Science, 2017, 33(5):80-86. ] DOI: 10.3969/j.issn.1672-0504.2017.05.013 doi: 10.3969/j.issn.1672-0504.2017.05.013
[19]	Liang X, Liu X P, Li X, et al. Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method[J]. Landscape and Urban Planning, 2018, 177:47-63. DOI: 10.1016/j.landurbplan.2018.04.016 doi: 10.1016/j.landurbplan.2018.04.016
[20]	张亚飞, 廖和平, 李义龙. 基于反规划与FLUS模型的城市增长边界划定研究——以重庆市渝北区为例[J]. 长江流域资源与环境, 2019, 28(4):21-31.
	[ Zhang Y F, Liao H P, Li Y L. Delimitaion of urban growth boundary based on anti-planning and FLUS model: A case study of Yubei district, Chongqing, China[J]. Resources and Environment in the Yangtze Basin, 2019, 28(4):21-31. ] DOI: 10.11870/cjlyzyyhj201904003 doi: 10.11870/cjlyzyyhj201904003
[21]	Chen Z Z, Huang M, Zhu D Y, et al. Integrating remote sensing and a Markov-FLUS model to simulate future land use changes in Hokkaido, Japan[J]. Remote Sensing, 2021, 13(13):2621. DOI: 10.3390/rs13132621 doi: 10.3390/rs13132621
[22]	Feng D R, Bao W K, Fu M C, et al. Current and future land use characters of a national central city in Eco-Fragile Region: A case study in Xi'an city based on FLUS model[J]. Land, 2021, 10(3):286. DOI: 10.3390/land10030286 doi: 10.3390/land10030286
[23]	Huang Y C, Nian P H, Zhang W X. The prediction of interregional land use differences in Beijing: A Markov model[J]. Environmental Earth Sciences, 2015, 73(8):4077-4090. DOI: 10.1007/s12665-014-3693-8 doi: 10.1007/s12665-014-3693-8
[24]	Yang X, Zheng X Q, Chen R. A land use change model: integrating landscape pattern indexes and Markov-CA[J]. Ecological Modelling, 2014, 283:1-7. DOI: 10.1016/j.ecolmodel.2014.03.011 doi: 10.1016/j.ecolmodel.2014.03.011
[25]	Li X, Chen G Z, Liu X P, et al. A new global land-use and land-cover change product at a 1-km resolution for 2010 to 2100 based on human-environment interactions[J]. Annals of the American Association of Geographers, 2017, 107(5):1040-1059. DOI: 10.1080/24694452.2017.1303357 doi: 10.1080/24694452.2017.1303357
[26]	Chen D S, Zhang Y T, Yao Y, et al. Exploring the spatial differentiation of urbanization on two sides of the Hu Huanyong Line: based on nighttime light data and cellular automata[J]. Applied Geography, 2019, 112:102081. DOI: 10.1016/j.apgeog.2019.102081 doi: 10.1016/j.apgeog.2019.102081
[27]	Jokar Arsanjani J, Helbich M, Kainz W, et al. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion[J]. International Journal of Applied Earth Observation and Geoinformation, 2013, 21:265-275. DOI: 10.1016/j.jag.2011.12.014 doi: 10.1016/j.jag.2011.12.014
[28]	Lin Y P, Chu H J, Wu C F, et al. Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling: A case study[J]. International Journal of Geographical Information Science, 2011, 25(1):65-87. DOI: 10.1080/13658811003752332 doi: 10.1080/13658811003752332
[29]	Chen Y M, Li X, Liu X P, et al. Capturing the varying effects of driving forces over time for the simulation of urban growth by using survival analysis and cellular automata[J]. Landscape and Urban Planning, 2016, 152:59-71. DOI: 10.1016/j.landurbplan.2016.03.011 doi: 10.1016/j.landurbplan.2016.03.011
[30]	He J L, Li X, Yao Y, et al. Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques[J]. International Journal of Geographical Information Science, 2018, 32(10):2076-2097. DOI: 10.1080/13658816.2018.1480783 doi: 10.1080/13658816.2018.1480783
[31]	李秋萍, 刘逸诗, 巩诗瑶, 等. 基于居民出行活动特征的个体经济水平推断方法[J]. 武汉大学学报·信息科学版, 2019, 44(10):1575-1580.
	[ Li Q P, Liu Y S, Gong S Y, et al. Individual income level inference method based on travel behavior of urban residents[J]. Geomatics and Information Science of Wuhan University, 2019, 44(10):1575-1580. ] DOI: 10.13203/j.whugis20170426 doi: 10.13203/j.whugis20170426
[32]	Grömping U. Variable importance assessment in regression: linear regression versus random forest[J]. The American Statistician, 2009, 63(4):308-319. DOI: 10.1198/tast.2009.08199 doi: 10.1198/tast.2009.08199
[33]	深圳市统计局, 国家统计局深圳调查队. 深圳统计年鉴[M]. 北京: 中国统计出版社, 2020.
	[ Shenzhen Bureau of Statistics, Shenzhen Survey Team of National Bureau of Statistics. Shenzhen Statistical Yearbook[M]. Beijing: China Statistics Press, 2020. ]
[34]	Yao Y, Li X, Liu X P, et al. Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model[J]. International Journal of Geographical Information Science, 2017, 31(4):825-848. DOI: 10.1080/13658816.2016.1244608 doi: 10.1080/13658816.2016.1244608
[35]	何媛婷, 王石英, 袁再健, 等. 珠江三角洲土地利用变化及其对城市化发展的响应[J]. 生态环境学报, 2020, 29(2):303-310.
	[ He Y T, Wang S Y, Yuan Z J, et al. Land use change and its response to urbanization in the Pearl River Delta[J]. Ecology and Environmental Sciences, 2020, 29(2):303-310. ] DOI: 10.16258/j.cnki.1674-5906.2020.02.011 doi: 10.16258/j.cnki.1674-5906.2020.02.011
[36]	王二红, 冯长春, 张爱华. 深圳市建设用地扩展分析[J]. 城市发展研究, 2015, 22(5):38-44.
	[ Wang E H, Feng C C, Zhang A H. Study on Shenzhen construction land expansion[J]. Urban Development Studies, 2015, 22(5):38-44. ] DOI: CNKI:SUN:CSFY.0.2015-05-008 doi: CNKI:SUN:CSFY.0.2015-05-008
[37]	Zhang D C, Liu X P, Wu X Y, et al. Multiple intra-urban land use simulations and driving factors analysis: A case study in Huicheng, China[J]. GIScience & Remote Sensing, 2019, 56(2):282-308. DOI: 10.1080/15481603.2018.1507074 doi: 10.1080/15481603.2018.1507074
[38]	Yao Y, Liu X P, Li X, et al. Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata[J]. International Journal of Geographical Information Science, 2017, 31(12):2452-2479. DOI: 10.1080/13658816.2017.13 60494 doi: 10.1080/13658816.2017.13 60494
[39]	Yao Y, Zhang J B, Hong Y, et al. Mapping fine-scale urban housing prices by fusing remotely sensed imagery and social media data[J]. Transactions in GIS, 2018, 22(2):561-581. DOI: 10.1111/tgis.12330 doi: 10.1111/tgis.12330
[40]	姚尧, 任书良, 王君毅, 等. 卷积神经网络和随机森林的城市房价微观尺度制图方法[J]. 地球信息科学学报, 2019, 21(2):168-177. doi: 10.12082/dqxxkx.2019.180508
	[ Yao Y, Ren S L, Wang J Y, et al. Mapping the fine-scale housing price distribution by integrating a convolutional neural network and random forest[J]. Journal of Geo-information Science, 2019, 21(2):168-177. ] DOI: 10.12082/dqxxkx.2019.180508 doi: 10.12082/dqxxkx.2019.180508

类别	空间变量	分辨率/m	含义
自然因素	高程/m	30	地形高程条件
自然因素	坡度/°		地形坡度条件
	到河流距离/m		像元几何中心到最近河流的欧氏距离
	到海岸线距离/m		像元几何中心到最近海岸线的欧氏距离
交通因素	到主要道路距离/m		像元几何中心到最近主要道路的欧氏距离
交通因素	到铁路距离/m		像元几何中心到最近铁路的欧氏距离
	路网密度/(km/km²)		每个像元周边的道路分布密度
	到地铁站距离/m	30	像元几何中心到最近地铁站的欧氏距离
	公交站分布密度/(个/km²)		每个像元周边的公交站分布密度
社会经济因素	到医疗设施距离/m		像元几何中心到最近医疗设施的欧氏距离
社会经济因素	到区县中心距离/m		像元几何中心到最近区县中心的欧氏距离
	到学校距离/m		像元几何中心到最近学校的欧氏距离
	到娱乐场所距离/m		像元几何中心到最近娱乐场所的欧氏距离
	房屋价格分布/元	30	每个像元所在位置的房价精细制图

	RFA-CA	Logistic-CA	FLUS
总体精度	0.842	0.843	0.850
Kappa系数	0.744	0.746	0.757
FoM	0.198	0.198	0.220

	城市用地	植被	水体	裸地
2030年	1074.441	787.715	293.127	38.749
2040年	1151.236	712.587	291.338	38.872

耦合FLUS和Markov的快速发展城市土地利用空间格局模拟方法

A Coupled FLUS and Markov Approach to Simulate the Spatial Pattern of Land Use in Rapidly Developing Cities

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