地理学报

地理学报 ›› 2014, Vol. 69 ›› Issue (9): 1326-1345.doi: 10.11821/dlxb201409007

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地理学时空数据分析方法

王劲峰1(), 葛咏1, 李连发1, 孟斌2, 武继磊3, 柏延臣4, 杜世宏5, 廖一兰1, 胡茂桂1, 徐成东1   

  1. 1. 中国科学院地理科学与资源研究所, 北京 100101
    2. 北京联合大学应用文理学院, 北京 100191
    3. 北京大学人口研究所, 北京 100871
    4. 北京师范大学遥感与地理学院, 北京 100875
    5. 北京大学地球与空间科学学院, 北京 100871
  • 收稿日期:2014-07-08 修回日期:2014-07-27 出版日期:2014-09-17 发布日期:2014-09-17
  • 作者简介:

    作者简介：王劲峰 (1965-), 男, 研究员, 中国地理学会会员 (BJ1566), 从事地理信息科学的理论创新和实践。E-mail: wangjf@igsnrr.ac.cn

  • 基金资助:
    国家自然科学基金 (41023010);973课题 (2012CB955503);National Natural Science Foundation of China, No.41023010;The National Basic Research Program of China, No.2012CB955503

Spatiotemporal data analysis in geography

Jinfeng WANG1(), Yong GE1, Lianfa LI1, Bin MENG2, Jilei WU3, Yanchen BO4, Shihong DU5, Yilan LIAO1, Maogui HU1, Chengdong XU1   

  1. 1. LREIS, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. College of Applied Arts & Sciences of Beijing Union University, Beijing 100191, China
    3. Institute of Population Research, Peking University, Beijing 100871, China
    4. School of Geography, Beijing Normal University, Beijing 100875, China
    5. School of Earth and Space Science, Peking University, Beijing 100871, China
  • Received:2014-07-08 Revised:2014-07-27 Published:2014-09-17 Online:2014-09-17

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被引次数

106 | 36

摘要：

随着地理空间观测数据的多年积累,地球环境、社会和健康数据监测能力的增强,地理信息系统和计算机网络的发展,时空数据集大量生成,时空数据分析实践呈现快速增长。本文对此进行了分析和归纳,总结了时空数据分析的7类主要方法,包括：时空数据可视化,目的是通过视觉启发假设和选择分析模型;空间统计指标的时序分析,反映空间格局随时间变化;时空变化指标,体现时空变化的综合统计量;时空格局和异常探测,揭示时空过程的不变和变化部分;时空插值,以获得未抽样点的数值;时空回归,建立因变量和解释变量之间的统计关系;时空过程建模,建立时空过程的机理数学模型;时空演化树,利用空间数据重建时空演化路径。通过简述这些方法的基本原理、输入输出、适用条件以及软件实现,为时空数据分析提供工具和方法手段。

关键词: 时空数据, 时空格局, 时空过程, 时空机理, 样本, 对象总体, 大数据

Abstract:

Following the emergence of large numbers of spatiotemporal datasets, the literatures related to spatiotemporal data analysis increase rapidly in recent years. This paper reviews the literatures and practices in spatiotemporal data analysis, and classifies the methods available for spatiotemporal data analysis into seven categories: including geovisualization of spatiotemporal data, time series analysis of spatial statistical indicators, coupling spatial and temporal change indicators, detection of spatiotemporal pattern and abnormality, spatiotemporal interpolation, spatiotemporal regression, spatiotemporal process modelling, and spatiotemporal evolution tree. We summarized the principles, input and output, assumptions and computer software of the methods that would be helpful for users to make a choice from the toolbox in spatiotemporal data analysis. When we handle spatiotemporal big data, spatial sampling appears to be one of the core methods, because (1) information in a big data is often too big to be mastered by human physical brain, so has to be summarized by statistics understandable; (2) the users of Weibo, Twitter, internet, mobile phone, mobile vehicles are neither the total population nor a random sample of the total population, therefore, the big data sample is usually biased from the population, and the bias has to be remedied to make a correct inference; (3) the data quality is usually inconsistent within a big data, so there should be a balance between the variances of inferences made by using data with various quality and by using small but high quality data.

Key words: spatiotemporal data, spatiotemporal pattern, spatiotemporal process, spatiotemporal mechanism, sample, target population, big data