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

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