多源大数据支持的土地储备智能决策模型集研究

doi:10.12082/dqxxkx.2022.210079

地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (2): 299-309.doi: 10.12082/dqxxkx.2022.210079

• 地球信息技术在国土空间规划中的应用 • 上一篇下一篇

多源大数据支持的土地储备智能决策模型集研究

李军¹(), 刘举庆¹^,^*(), 游林², 董恒³, 俞艳³, 张晓盼³, 钟文军⁴, 杨典华⁵

1.中国矿业大学（北京）地球科学与测绘工程学院,北京 100083
2.浙江时空智子大数据有限公司,宁波 315101
3.武汉理工大学资源与环境工程学院,武汉 430070
4.宁波市自然资源和规划大数据中心,宁波 315042
5.京师天启（北京）科技有限公司,北京 100086

收稿日期:2021-02-18 修回日期:2021-03-28 出版日期:2022-02-25 发布日期:2022-04-25
通讯作者: *刘举庆（1995— ）,男,山东临沂人,博士研究生,主要研究方向为地理大数据分析与挖掘。E-mail: liujvq@163.com
作者简介:李军（1987— ）,男,湖北汉川人,副教授,主要从事地理信息科学理论与方法应用研究。E-mail: junli@cumtb.edu.cn
基金资助:
国家自然科学基金项目(41971355);国家重点研发计划项目(2018YFB0505405);中国矿业大学（北京）越崎青年学者资助计划;中央高校基本科研业务费专项资金资助(2015QD01)

An Intelligent Decision Model Set for Land Reserve based on Multi-source Big Data

LI Jun¹(), LIU Juqing¹^,^*(), YOU Lin², DONG Heng³, YU Yan³, ZHANG Xiaopan³, ZHONG Wenjun⁴, YANG Dianhua⁵

1. College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
2. Zhejiang Space-time Sophon Big Data Co., Ltd., Ningbo 315101, China
3. School of Resource and Environment Engineering, Wuhan University of Technology, Wuhan 430070, China
4. Ningbo Natural Resources and Planning Big Data Center, Ningbo 315042, China
5. Image Sky Beijing technology Co., Ltd., Beijing 100086, China

Received:2021-02-18 Revised:2021-03-28 Online:2022-02-25 Published:2022-04-25
Supported by:
National Natural Science Foundation of China(41971355);National Key Research and Development Program of China(2018YFB0505405);the Yueqi Young Scholar Project of China University of Mining and Technology at Beijing;the Fundamental Research Funds for the Central Universities(2015QD01)

摘要/Abstract

摘要：

伴随着城镇化的快速推进,城乡建设用地资源日益紧张,但目前土地储备决策缺乏精准科学依据,无法有效地进行资源配置和宏观调控。针对此问题,本文深入剖析土地储备基本业务与决策环节,研究了一套面向土地储备的智能决策模型集,包括存量土地监测模型、收储成本预测模型、出让价格预测模型、储备平衡分析模型、相似地块分析模型、开发时序分析模型及病态地块识别模型,旨在将土地储备决策环节科学化、定量化和模型化,并重点为土地储备总量、效益、规模、结构、布局、时序的统筹安排提供建议。另外,该模型集具有体系化、高效灵活、智能化的特点,能够服务于储备业务全链条,满足即时决策应用需求和实现模型自主更新与进化,保证模型的时效性。最后,该模型集已经工程化应用于宁波市土地储备智能决策支持平台,实践验证了以上决策模型具有较高的准确度和实用性,表明模型集能够为土地储备的科学决策提供理论依据,有利于土地资源的集约利用和高效配置。

关键词: 国土空间规划, 土地储备, 多源大数据, 数据挖掘, 人工智能, 智能决策, 综合评价, 定量预测

Abstract:

With the rapid development of urbanization, urban construction land is becoming increasingly scarce. Therefore, as a macro-regulation policy for the intensive utilization and optimal allocation of land resources, land reserve is playing an increasingly important role. However, at present, land reserve decision-making lacks scientific basis and cannot effectively carry out resource allocation. In order to solve this problem, this paper puts forward seven intelligent decision-making models for land reserve through in-depth analysis of the basic services and decision-making processes of land reserves. The models are listed below. Firstly, Stock Land Monitoring Model based on the comprehensive quantitative evaluation method, which can dynamically monitor and discover the city stock land and then make recommendations for land reserve objects. Secondly, Land Reserve Cost Prediction Model based on the market comparison method, which can carry out a large range and efficiently predict the cost of stock land. Thirdly, Land Sale Price Prediction Model based on the Support Vector Machine (SVM), which can predict the reserve income of the land to be sold. Fourthly, Land Reserve Balance Analysis Model based on the gray forecast model, which can predict the amount of land reserve to promote coordinated regional development. Fifthly, Similar Land Query Model based on the comprehensive quantitative evaluation method, which can promote large-scale land development to form an agglomeration effect. Sixthly, Development Sequence Analysis Model based on the comprehensive quantitative evaluation method, which can optimize the spatial structure and formulate a reasonable development sequence to promote the continuous rolling of funds. Seventhly, Abnormal Land Identification Model based on spatial overlay analysis, which can improve the detection efficiency of various problematic plots. The purpose of this model set is to make the land reserve decision-making process scientific, quantitative, and model-based, which focuses on providing instructions for the overall arrangement of total land reserve, benefit, scale, structure, layout, and time sequence. In addition, through theoretical analysis and practical verification, we found that the model set has the characteristics of systematization, high efficiency, flexibility, and intelligence. It can serve the entire chain of land reserve service, meet the needs of real-time decision-making applications, and realize the independent update and evolution to ensure the timeliness of model computation. Finally, the model set has been engineered and applied to the Ningbo Land Reserve Intelligent Decision Support Platform. The effectiveness and practicality of the above decision-making models have been verified by simulating the entire land reserve decision-making processes based on this platform, indicating that the model set can provide a theoretical basis for the scientific decision-making of land reserves.

Key words: territorial spatial planning, land reserve, multi-source big data, data mining, artificial intelligence, intelligent decision, comprehensive evaluation, quantitative prediction

李军, 刘举庆, 游林, 董恒, 俞艳, 张晓盼, 钟文军, 杨典华. 多源大数据支持的土地储备智能决策模型集研究[J]. 地球信息科学学报, 2022, 24(2): 299-309.DOI:10.12082/dqxxkx.2022.210079

LI Jun, LIU Juqing, YOU Lin, DONG Heng, YU Yan, ZHANG Xiaopan, ZHONG Wenjun, YANG Dianhua. An Intelligent Decision Model Set for Land Reserve based on Multi-source Big Data[J]. Journal of Geo-information Science, 2022, 24(2): 299-309.DOI:10.12082/dqxxkx.2022.210079

图/表 5

参考文献 43

[1]	国家统计局. 2018年国民经济和社会发展统计公报[EB/OL]. http://www.stats.gov.cn/tjsj/zxfb/201902/t20190228_1651265.html, 2019-02-28.
	[National Bureau of Statistics. 2018 statistical bulletin on national economic and social development[EB/OL]. http://www.stats.gov.cn/tjsj/zxfb/201902/t20190228_1651265.html, 2019-02-28. ]
[2]	夏柱智. 城市化进程中的土地制度改革比较研究—— 基于苏南和珠三角的经验[J]. 社会科学, 2019(2):81-88.
	[ Xia Z Z. A comparative study of land system reform in the process of urbanization: Based on the experience of south of Jiangsu and the Pearl River Delta region[J]. Journal of Social Sciences, 2019(2):81-88. ] DOI: 10.13644/j.cnki.cn31-1112.2019.02.008 doi: 10.13644/j.cnki.cn31-1112.2019.02.008
[3]	Sheng X W, Cao Y G, Zhou W, et al. Multiple scenario simulations of land use changes and countermeasures for collaborative development mode in Chaobai River region of Jing-Jin-Ji, China[J]. Habitat International, 2018, 82:38-47. DOI: 10.1016/j.habitatint.2018.10.008 doi: 10.1016/j.habitatint.2018.10.008
[4]	李明超. 城市土地征收、储备、出让改革联动述论[J]. 西部论坛, 2017, 27(5):54-63.
	[ Li M C. Review of urban land expropriation, reserve, transfer and management reform of China[J]. West Forum, 2017, 27(5):54-63. ] DOI: 10.3969/j.issn.1674-8131.2017.05.009 doi: 10.3969/j.issn.1674-8131.2017.05.009
[5]	高霞, 朱德米. 中国土地储备政策演进的结构特征[J]. 城市问题, 2017(12):72-80.
	[ Gao X, Zhu D M. Structual features of the evolution of China's land conservation policies[J]. Urban Problems, 2017(12):72-80. ] DOI: 10.13239/j.bjsshkxy.cswt.171210 doi: 10.13239/j.bjsshkxy.cswt.171210
[6]	黄海, 祝国瑞. 基于模糊神经网络的土地合理储备量预测研究[J]. 武汉大学学报·信息科学版, 2006, 31(6):561-563.
	[ Huang H, Zhu G R. Application of fuzzy neural networks to land reserve prediction[J]. Geomatics and Information Science of Wuhan University, 2006, 31(6):561-563. ] DOI: 10.3969/j.issn.1671-8860.2006.06.024 doi: 10.3969/j.issn.1671-8860.2006.06.024
[7]	Wang Y J, Li L J. Forecasts of urban construction land scale based on driving force analysis[C]//Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2017). Paris: Atlantis Press, 2017:453-456. DOI: 10.2991/icmmcce-17.2017.86 doi: 10.2991/icmmcce-17.2017.86
[8]	Xiong Y, Chen Y, Peng F, et al. Analog simulation of urban construction land supply and demand in Chang-Zhu-Tan Urban Agglomeration based on land intensive use[J]. Journal of Geographical Sciences, 2019, 29(8):1346-1362. DOI: 10.1007/s11442-019-1663-5 doi: 10.1007/s11442-019-1663-5
[9]	刘鹏, 关丽, 罗晓燕. 基于GIS的城市建设用地资源潜力评价初探[J]. 地理与地理信息科学, 2011, 27(5):69-73.
	[ Liu P, Guan L, Luo X Y. Potential resource evaluation of urban construction land based on GIS[J]. Geography and Geo-information Science, 2011, 27(5):69-73. ] DOI: CNKI:SUN:DLGT.0.2011-05-016 doi: CNKI:SUN:DLGT.0.2011-05-016
[10]	Xu Y, Tang Q, Fan J, et al. Assessing construction land potential and its spatial pattern in China[J]. Landscape and Urban Planning, 2011, 103(2):207-216. DOI: 10.1016/j.landurbplan.2011.07.013 doi: 10.1016/j.landurbplan.2011.07.013
[11]	董冠鹏, 张文忠, 武文杰, 等. 北京城市住宅土地市场空间异质性模拟与预测[J]. 地理学报, 2011, 66(6):750-760.
	[ Dong G P, Zhang W Z, Wu W J, et al. Spatial heterogeneity in determinants of residential land price: Simulation and prediction[J]. Acta Geographica Sinica, 2011, 66(6):750-760. ] DOI: 10.11821/xb201106004 doi: 10.11821/xb201106004
[12]	Wang Z Y, Fu M C, Yu Y L, et al. Prediction of urban land price based on Grey-Markov model[C]// Proceedings of 2011 International Conference on Computer Science and Network Technology. IEEE, 2011:708-712. DOI: 10.1109/ICCSNT.2011.6182064 doi: 10.1109/ICCSNT.2011.6182064
[13]	Sampathkumar V, Santhi M H, Vanjinathan J. Forecasting the land price using statistical and neural network software[J]. Procedia Computer Science, 2015, 57:112-121. DOI: 10.1016/j.procs.2015.07.377 doi: 10.1016/j.procs.2015.07.377
[14]	Li L B, Chen Y M, Yu C. Study on the price and economic impact of residential land based on system dynamics[C]// ICCREM 2017. Reston, VA: American Society of Civil Engineers, 2017:187-196. DOI: 10.1061/9780784481073.021 doi: 10.1061/9780784481073.021
[15]	李德仁, 马军, 邵振峰. 论时空大数据及其应用[J]. 卫星应用, 2015(9):7-11.
	[ Li D R, Ma J, Shao Z F. Discussion on spatio-temporal big data and its application[J]. Satellite Application, 2015(9):7-11. ] DOI: CNKI:SUN:WXYG.0.2015-09-005 doi: CNKI:SUN:WXYG.0.2015-09-005
[16]	Xie H L, He Y F, Xie X. Exploring the factors influencing ecological land change for China's Beijing-Tianjin-Hebei Region using big data[J]. Journal of Cleaner Production, 2017, 142:677-687. DOI: 10.1016/j.jclepro.2016.03.064 doi: 10.1016/j.jclepro.2016.03.064
[17]	吴志峰, 曹峥, 宋松, 等. 粤港澳大湾区湿地遥感监测与评估:现状、挑战及展望[J]. 生态学报, 2020, 40(23):8440-8450.
	[ Wu Z F, Cao Z, Song S, et al. Wetland remote sensing monitoring and assessment in Guangdong-Hong Kong-Macao Greater Bay Area: Current status, challenges and future perspectives[J]. Acta Ecologica Sinica, 2020, 40(23):8440-8450. ] DOI: 10.5846/stxb202004150888 doi: 10.5846/stxb202004150888
[18]	Ding L, Shao Z F, Zhang H C, et al. A comprehensive evaluation of urban sustainable development in China based on the TOPSIS-entropy method[J]. Sustainability, 2016, 8(8):746. DOI: 10.3390/su8080746 doi: 10.3390/su8080746
[19]	陈能成, 刘迎冰, 盛浩, 等. 智慧城市时空信息综合决策关键技术与系统[J]. 武汉大学学报·信息科学版, 2018, 43(12):2278-2286.
	[ Chen N C, Liu Y B, Sheng H, et al. Key techniques and system for comprehensive decision-making of spatio-temporal information in smart city[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12):2278-2286. ] DOI: 10.13203/j.whugis20180198 doi: 10.13203/j.whugis20180198
[20]	Li J, Li Q Q, Zhu Y, et al. An automatic extraction method of coach operation information from historical trajectory data[J]. Journal of Advanced Transportation, 2019, 2019:1-15. DOI: 10.1155/2019/3634942 doi: 10.1155/2019/3634942
[21]	Tu W, Zhu T T, Xia J Z, et al. Portraying the spatial dynamics of urban vibrancy using multisource urban big data[J]. Computers, Environment and Urban Systems, 2020, 80:101428. DOI: 10.1016/j.compenvurbsys.2019.101428 doi: 10.1016/j.compenvurbsys.2019.101428
[22]	秦萧, 甄峰, 李亚奇, 等. 国土空间规划大数据应用方法框架探讨[J]. 自然资源学报, 2019, 34(10):2134-2149.
	[ Qin X, Zhen F, Li Y Q, et al. Discussion on the application framework of big data in territorial spatial planning[J]. Journal of Natural Resources, 2019, 34(10):2134-2149. ] DOI: 10.31497/zrzyxb.20191010 doi: 10.31497/zrzyxb.20191010
[23]	Batty M. Big data, smart cities and city planning[J]. Dialogues in Human Geography, 2013, 3(3):274-279. DOI: 10.1177/2043820613513390 doi: 10.1177/2043820613513390
[24]	李燕萍, 虞虎, 王昊, 等. 面向大数据时代的城市规划研究响应与应对方略[J]. 城市发展研究, 2017, 24(10):1-10.
	[ Li Y P, Yu H, Wang H, et al. The response and countermeasures of urban planning research in the era of big data[J]. Urban Development Studies, 2017, 24(10):1-10. ] DOI: 10.3969/j.issn.1006-3862.2017.10.001 doi: 10.3969/j.issn.1006-3862.2017.10.001
[25]	喻文承, 李晓烨, 高娜, 等. 北京国土空间规划“一张图”建设实践[J]. 规划师, 2020, 36(2):59-64,77.
	[ Yu W C, Li X Y, Gao N, et al. “one map” construction in national land-space plan, Beijing[J]. Planners, 2020, 36(2):59-64,77. ] DOI: 10.3969/j.issn.1006-0022.2020.02.009 doi: 10.3969/j.issn.1006-0022.2020.02.009
[26]	李满春, 陈振杰, 周琛, 等. 面向“一张图”的国土空间规划数据库研究[J]. 中国土地科学, 2020, 34(5):69-75.
	[ Li M C, Chen Z J, Zhou C, et al. “one map” oriented database investigation for territorial space planning[J]. China Land Science, 2020, 34(5):69-75. ] DOI: 10.11994/zgtdkx.20200514.144131 doi: 10.11994/zgtdkx.20200514.144131
[27]	Cai J X, Huang B, Song Y M. Using multi-source geospatial big data to identify the structure of polycentric cities[J]. Remote Sensing of Environment, 2017, 202:210-221. DOI: 10.1016/j.rse.2017.06.039 doi: 10.1016/j.rse.2017.06.039
[28]	蒋希冀, 王静, 王兰. 基于多源数据的城市功能布局优化研究:以上海市宝山区为例[J]. 现代城市研究, 2020, 35(5):2-9,37.
	[ Jiang X J, Wang J, Wang L. Research on optimization of urban functional layout based on multi-source data: A case study of Baoshan district, Shanghai[J]. Modern Urban Research, 2020, 35(5):2-9,37. ] DOI: 10.3969/j.issn.1009-6000.2020.05.001 doi: 10.3969/j.issn.1009-6000.2020.05.001
[29]	Gao X, Cai J. Optimization analysis of urban function regional planning based on big data and GIS Technology[J]. Boletin Tecnico/Technical Bulletin, 2017, 55(11):344-351.
[30]	杨振山, 苏锦华, 杨航, 等. 基于多源数据的城市功能区精细化研究——以北京为例[J]. 地理研究, 2021, 40(2):477-494. doi: 10.11821/dlyj020200074
	[ Yang Z S, Su J H, Yang H, et al. Exploring urban functional areas based on multi-source data: A case study of Beijing[J]. Geographical Research, 2021, 40(2):477-494. ] DOI: 10.11821/dlyj020200074 doi: 10.11821/dlyj020200074
[31]	屠李, 赵鹏军, 张超荣, 等. 面向新一代人工智能的城市规划决策系统优化[J]. 城市发展研究, 2019, 26(1):54-59.
	[ Tu L, Zhao P J, Zhang C R, et al. Optimization of urban planning decision system based on the new generation of artificial intelligence[J]. Urban Development Studies, 2019, 26(1):54-59. ] DOI: 10.3969/j.issn.1006-3862.2019.01.009 doi: 10.3969/j.issn.1006-3862.2019.01.009
[32]	张芳芳, 沈少青, 陈学业, 等. 智慧城市规划云平台的设计与实现[J]. 测绘通报, 2019(1):123-126.
	[ Zhang F F, Shen S Q, Chen X Y, et al. Design and implementation of smart city planning cloud platform[J]. Bulletin of Surveying and Mapping, 2019(1):123-126. ] DOI: 10.13474/j.cnki.11-22 46.2019.0025 doi: 10.13474/j.cnki.11-22 46.2019.0025
[33]	李军, 刘举庆, 游林, 等. 时空大数据支持的土地储备智能决策体系与应用研究[J]. 中国土地科学, 2019, 33(9):111-120.
	[ Li J, Liu J Q, You L, et al. Intelligent decision system and application of land reserve supported by spatiotemporal big data[J]. China Land Science, 2019, 33(9):111-120. ] DOI: 10.11994/zgtdkx.20190910.091057 doi: 10.11994/zgtdkx.20190910.091057
[34]	卢新海, 邓中明. 对我国城市土地储备制度的评析[J]. 城市规划汇刊, 2004(6):27-33,95.
	[ Lu X H, Deng Z M. Comment on urban land banking system in our country[J]. Urban Planning Forum, 2004(6):27-33,95. ] DOI: 10.M3969/j.issn.1000-3363.2004.06.007 doi: 10.M3969/j.issn.1000-3363.2004.06.007
[35]	崔建远, 陈进. 土地储备制度的现状与完善[M]. 北京: 中国人民大学出版社, 2014.12-14.
	[ Cui J Y, Chen J. Land reserve system in China: status in quo and its perfection[M]. Beijing: China Renmin University Press, 2014:12-14. ]
[36]	姜华, 王蔚炫, 毛勇龙, 等. 多规融合导向下宁波土地储备规划方法研究[J]. 规划师, 2018, 34(7):106-109,115.
	[ Jiang H, Wang W X, Mao Y L, et al. Multi-plan integration oriented land reserve planning, Ningbo[J]. Planners, 2018, 34(7):106-109,115. ] DOI: 10.3969/j.issn.1006-0022.2018.07.016 doi: 10.3969/j.issn.1006-0022.2018.07.016
[37]	鲍艳, 胡振琪, 王建峰, 等. 层次分析法在土地开发中的适宜性评价[J]. 西安科技大学学报, 2005, 25(2):179-182.
	[ Bao Y, Hu Z Q, Wang J F, et al. Analytical hierarchy process based on GIS assessment of suitability in land exploitation and consolidation[J]. Journal of Xi'an University of Science & Technology, 2005, 25(2):179-182. ] DOI: 10.3969/j.issn.1672-9315.2005.02.012 doi: 10.3969/j.issn.1672-9315.2005.02.012
[38]	Cheng E W L, Li H, Ho D C K. Analytic hierarchy process (AHP)[J]. Measuring Business Excellence, 2002, 6(4):33-37. DOI: 10.1108/13683040210451697 doi: 10.1108/13683040210451697
[39]	王秀丽, 李恒凯, 刘小生. 基于GIS的房地产市场比较法评估模型研究[J]. 中国土地科学, 2011, 25(10):70-76,97.
	[ Wang X L, Li H K, Liu X S. Study on the market sales comparison model in real estate appraisal based on GIS[J]. China Land Science, 2011, 25(10):70-76,97. ] DOI: 10.3969/j.issn.1001-8158.2011.10.012 doi: 10.3969/j.issn.1001-8158.2011.10.012
[40]	Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20(3):273-297. DOI: 10.1007/BF00994018 doi: 10.1007/BF00994018
[41]	刘思峰, 党耀国, 方志耕. 灰色系统理论及其应用[M].5版. 北京: 科学出版社, 2010.
	[ Liu S F, Dang Y G, Fang Z G. Grey systems: theory and application[M]. Beijing: Science Press, 2010. ]
[42]	李鹏山, 吕雅慧, 张超, 等. 基于核密度估计的京津冀地区耕地破碎化分析[J]. 农业机械学报, 2016, 47(5):281-287.
	[ Li P S, Lü Y H, Zhang C, et al. Analysis of cultivated land fragmentation in Beijing-Tianjin-Hebei region based on kernel density estimation[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(5):281-287. ] DOI: 10.6041/j.issn.1000-1298.2016.05.038 doi: 10.6041/j.issn.1000-1298.2016.05.038
[43]	Davies D L, Bouldin D W. A cluster separation measure[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1979, PAMI-1(2):224-227. DOI: 10.1109/TPAMI.1979.4766909 doi: 10.1109/TPAMI.1979.4766909

精度等级	优	良	中	差
P	>0.95	>0.8	>0.7	≤0.7
C	<0.35	<0.5	<0.65	≥0.65

多源大数据支持的土地储备智能决策模型集研究

An Intelligent Decision Model Set for Land Reserve based on Multi-source Big Data

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 5

参考文献 43

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	袁源, 毛磊, 李洪庆, 赵小风. 基于位置大数据的城市居住用地效率指标构建及评价研究[J]. 地球信息科学学报, 2022, 24(2): 235-248.
[2]	陈霆, 徐伟铭, 吴升, 刘洁. 国土空间规划视角下的城镇开发边界划定和空间管控体系构建[J]. 地球信息科学学报, 2022, 24(2): 263-279.
[3]	谢花林, 温家明, 陈倩茹, 何亚芬. 地球信息科学技术在国土空间规划中的应用研究进展[J]. 地球信息科学学报, 2022, 24(2): 202-219.
[4]	陈伟杰, 赵楠, 张婕姝, 宋炳良. AIS数据在集装箱港口服务效率的应用研究[J]. 地球信息科学学报, 2022, 24(1): 153-164.
[5]	黄隆杨, 王静, 李泽慧, 赵晓东, 刘晶晶, 方莹. 基于自然资源大数据的城市多功能景观识别与国土空间规划分区[J]. 地球信息科学学报, 2021, 23(9): 1617-1631.
[6]	胡添, 刘涛, 杜萍, 余贝贝, 张萌生. 空间同位模式支持下城市服务业关联发现及特征分析[J]. 地球信息科学学报, 2021, 23(6): 969-978.
[7]	李雨欣, 王瑛, 马庆媛, 刘天雪, 司丽丽, 俞海洋. 基于DTW与K-means算法的河北场雨及雨型分区特征研究[J]. 地球信息科学学报, 2021, 23(5): 860-868.
[8]	刘稳, 詹庆明, 赵中元, 林苏靖, 肖琨, 李荣. 面向自然资源统一管理的多源土地利用信息一致性分析评价[J]. 地球信息科学学报, 2021, 23(3): 365-376.
[9]	谢聪慧, 吴世新, 张晨, 孙文涛, 何海芳, 裴韬, 罗格平. 基于谱系聚类的全球各国新冠疫情时间序列特征分析[J]. 地球信息科学学报, 2021, 23(2): 236-245.
[10]	尹凌, 刘康, 张浩, 奚桂锴, 李璇, 李子垠, 薛建章. 耦合人群移动的COVID-19传染病模型研究进展[J]. 地球信息科学学报, 2021, 23(11): 1894-1909.
[11]	覃湘栋, 庞治国, 江威, 冯天时, 付俊娥. 土壤水分微波反演方法进展和发展趋势[J]. 地球信息科学学报, 2021, 23(10): 1728-1742.
[12]	宋关福, 陈勇, 罗强, 武梦瑶. GIS基础软件技术体系发展及展望[J]. 地球信息科学学报, 2021, 23(1): 2-15.
[13]	陈芳淼, 黄慧萍, 贾坤. 时空大数据在城市群建设与管理中的应用研究进展[J]. 地球信息科学学报, 2020, 22(6): 1307-1319.
[14]	胡最. 传统聚落景观基因的地理信息特征及其理解[J]. 地球信息科学学报, 2020, 22(5): 1083-1094.
[15]	柯新利, 肖邦勇, 郑伟伟, 马艳春, 李红艳. 城镇-农业-生态空间划定的多情景模拟[J]. 地球信息科学学报, 2020, 22(3): 580-591.