帮助 关于我们

返回检索结果

地理大数据挖掘的本质
Principle of big geodata mining

查看参考文献81篇

裴韬 1,2   刘亚溪 1,2   郭思慧 1,2   舒华 1,2   杜云艳 1,2   马廷 1,2   周成虎 1,2  
文摘 针对地理大数据的内在本质以及地理大数据挖掘对于地理学研究的意义,本文解释了地理大数据的含义,并在大数据“5V”特征的基础上提出了粒度、广度、密度、偏度和精度等“5度”的特征,揭示了地理大数据的本质特点。在此基础上,从地理大数据的表达方式、地理大数据挖掘的目标、地理模式的叠加与尺度性、地理大数据挖掘与地理学的关系等4个方面阐述了地理大数据挖掘的本质与作用,并从挖掘目标的角度对地理大数据挖掘方法进行分类。未来地理大数据挖掘的研究将面临地理大数据的聚合、挖掘结果的有效性评价以及发现有价值的知识而非常识等几方面的挑战。
其他语种文摘 This paper reveals the principle of geographic big data mining and its significance to geographic research. In this paper, big geodata are first categorized into two domains: earth observation big data and human behavior big data. Then, another five attributes except for "5V",including granularity, scope, density, skewness and precision, are summarized regarding big geodata. Based on this, the essence and effect of big geodata mining are uncovered by the following four aspects. First, as the burst of human behavior big data, flow space, where the OD flow is the basic unit instead of the point in traditional space, will become a new presentation form for big geodata. Second, the target of big geodata mining is defined as revealing the spatial pattern and the spatial relationship. Third, spatio-temporal distributions of big geodata can be seen as the overlay of multiple geographic patterns and the patterns may be changed with scale. Fourth, big geodata mining can be viewed as a tool for discovering geographic patterns while the revealed patterns are finally attributed to the outcome of humanland relationship. Big geodata mining methods are categorized into two types in light of mining target, i.e. classification mining and relationship mining. The future research will be facing the following challenges, namely, the aggregation and connection of big geodata, the effective evaluation of mining result and mining "true and useful" knowledge.
来源 地理学报 ,2019,74(3):586-598 【核心库】
DOI 10.11821/dlxb201903014
关键词 空间模式 ; 空间关系 ; 空间分布 ; 流空间 ; 时空异质性 ; 知识发现
地址

1. 中国科学院地理科学与资源研究所, 资源与环境信息系统国家重点实验室, 北京, 100101  

2. 中国科学院大学, 北京, 100049

语种 中文
文献类型 研究性论文
ISSN 0375-5444
基金 国家自然科学基金项目
文献收藏号 CSCD:6447518

参考文献 共 81 共5页

1.  李德仁. 从GIS数据库中发现知识. 测绘学报,1995,24(1):37-44 被引 27    
2.  Harvey J M. Geographic Data Mining and Knowledge Discovery,2009 被引 3    
3.  Song C. Limits of predictability in human mobility. Science,2010,327(5968):1018-1021 被引 100    
4.  Ginsberg J. Detecting influenza epidemics using search engine query data. Nature,2009,457(7232):1012-1015 被引 152    
5.  Silver D. Mastering the game of Go with deep neural networks and tree search. Nature,2016,529(7587):484-489 被引 681    
6.  Silver D. Mastering the game of Go without human knowledge. Nature,2017,550(7676):354-359 被引 376    
7.  Mayer-Schonberger V. Big Data:A Revolution That Will Transform How We Live, Work, and Think,2013 被引 6    
8.  Marr B. Big Data:Using SMART Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance,2015 被引 1    
9.  刘瑜. 社会感知视角下的若干人文地理学基本问题再思考. 地理学报,2016,71(4):564-575 被引 45    
10.  Liu Z. Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records. Transactions in GIS,2018,22(2):494-513 被引 5    
11.  Zheng Y. Urban computing with taxicabs. Proceedings of the 13th International Conference on Ubiquitous Computing,2011:89-98 被引 19    
12.  Castro P S. Urban traffic modelling and prediction using large scale taxi GPS traces. Proceeding of Pervasive'12 Proceedings of the 10th International Conference on Pervasive Computing,2012:57-72 被引 1    
13.  Kong X. Urban traffic congestion estimation and prediction based on floating car trajectory data. Future Generation Computer Systems:The International Journal of EScience,2016,61:97-107 被引 7    
14.  Niu N. Integrating multi-source big data to infer building functions. International Journal of Geographical Information Science,2017,31(9):1871-1890 被引 8    
15.  Newing A. The role of digital trace data in supporting the collection of population statistics-the case for smart metered electricity consumption data. Population, Space and Place,2016,22(8):849-863 被引 2    
16.  NASA. New night lights maps open up possible real-time applications,2017 被引 2    
17.  Chen J. China:Open access to Earth land-cover map. Nature,2015,514(7523):434 被引 5    
18.  刘洋. 基于LTDR AVHRR和MODIS观测的全球长时间序列叶面积指数遥感反演. 地球信息科学学报,2015,17(11):1304-1312 被引 7    
19.  Oliver M A. Kriging:A method of interpolation for geographical information systems. International Journal of Geographical Information System,1990,4(3):313-332 被引 100    
20.  Stein M L. Interpolation of Spatial Data:Some Theory for Kriging,2012 被引 3    
引证文献 54

1 张一诺 时空连续数据支持下的空域资源配置研究:评述与展望 地球科学进展,2019,34(9):912-921
被引 2

2 邓敏 多模态地理大数据时空分析方法 地球信息科学学报,2020,22(1):41-56
被引 15

显示所有54篇文献

论文科学数据集

1. 中国2555个传统村落空间分布数据集

2. 中国再增2666个传统村落空间分布数据集

3. 2018-2019年基于OMI对四川、重庆地区卫星遥感反演NO2二级数据

数据来源:
国家对地观测科学数据中心
PlumX Metrics
相关文献

 作者相关
 关键词相关
 参考文献相关

版权所有 ©2008 中国科学院文献情报中心 制作维护:中国科学院文献情报中心
地址:北京中关村北四环西路33号 邮政编码:100190 联系电话:(010)82627496 E-mail:cscd@mail.las.ac.cn 京ICP备05002861号-4 | 京公网安备11010802043238号