地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (1): 41-56.doi: 10.12082/dqxxkx.2020.190491

• 专辑:地理智能 • 上一篇    下一篇

多模态地理大数据时空分析方法

邓敏1, 蔡建南1,*(), 杨文涛2, 唐建波1, 杨学习1, 刘启亮1, 石岩1   

  1. 1. 中南大学地理信息系,长沙 410083
    2. 湖南科技大学地理空间信息技术国家地方联合工程实验室,湘潭 411100
  • 收稿日期:2019-09-04 修回日期:2019-11-25 出版日期:2020-01-25 发布日期:2020-04-08
  • 通讯作者: 蔡建南 E-mail:jiannan.cai@csu.edu.cn
  • 作者简介:邓 敏(1974— ),男,江西临川人,教授,博士生导师,从事时空数据挖掘与信息服务研究。E-mail:dengmin@csu.edu.cn
  • 基金资助:
    国家自然科学基金重点项目(41730105);国家重点研发计划项目(2016YFB0502303)

Spatio-temporal Analysis Methods for Multi-modal Geographic Big Data

DENG Min1, CAI Jiannan1,*(), YANG Wentao2, TANG Jianbo1, YANG Xuexi1, LIU Qiliang1, SHI Yan1   

  1. 1. Department of Geo-information, Central South University, Changsha 410083, China
    2. National-Local Joint Engineering Laboratory of Geospatial Information Technology, Hunan University of Science and Technology, Xiangtan 411100, China
  • Received:2019-09-04 Revised:2019-11-25 Online:2020-01-25 Published:2020-04-08
  • Contact: CAI Jiannan E-mail:jiannan.cai@csu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(41730105);National Key Research and Development Foundation of China(2016YFB0502303)

摘要:

多模态地理大数据时空分析旨在融合地理大数据的多模态信息发现有价值的时空分布规律、异常表现、关联模式与变化趋势,是全空间信息系统的核心研究内容,并有望成为推进地理学人地关系研究的重要突破口。为应对地理大数据时代的新机遇与挑战,本文围绕4类核心的时空分析方法(时空聚类分析、时空异常分析、时空关联分析与时空预测分析),系统归纳了国内外研究现状,探讨了时空分析中多尺度建模、多视角协同、多特征认知与多特性表达的研究难点。进而,介绍了多模态地理大数据时空聚类、异常、关联与预测分析模型,更加全面、客观、精准地认知与理解时空大数据中潜在的地理知识,并且能够在气象环境监测、公共安全管理、城市设施规划等多个应用领域发挥关键作用。

关键词: 全空间信息系统, 地理大数据, 多模态特征, 时空分析, 时空聚类分析, 时空异常分析, 时空关联分析, 时空预测分析

Abstract:

Multi-modal spatio-temporal analysis is aimed at discovering valuable knowledge about the spatio-temporal distributions, associations and revolutions underlying the multi-modal geographic big data. It is a core task of the pan-spatial information system, and is expected to facilitate the study of relationship between human and space. With emerging opportunities and challenges in an era of geographic big data, we systematically summarized four main methods for spatial-temporal analysis based on previous study, including spatio-temporal cluster analysis, spatio-temporal outlier detection, spatio-temporal association mining and spatio-temporal prediction. We discussed the challenges when applying the four methods in multi-scale modeling, multi-view fusion, multi-characteristic cognition, and multi-characteristic expression for spatial-temporal analysis. First, two types of scales (including data scale and analysis scale) are of great importance in the spatio-temporal clustering task. Given the data scale, the best analysis scale for detecting spatio-temporal clusters can be determined using a permutation test method by evaluating the significance of clusters. Second, in the spatio-temporal outlier detection method, the cross-outliers in the context of two types of points are known as the abnormal associations between different types of points and the validity of cross-outliers is assessed through significance tests under the null hypothesis of complete spatial randomness. Third, in the spatio-temporal association mining method, the multi-modal distribution characteristics of each feature quantitatively described in the observed dataset are employed to construct the null hypothesis that the spatio-temporal distributions of different features are independent of each other, and then the evaluation of spatio-temporal associations is modeled as a significance test problem under the null hypothesis of independence. Finally, in the spatio-temporal prediction model, the effects of multiple characteristics of spatio-temporal data (e.g., spatio-temporal auto-correlation and heterogeneity) on the prediction results are fully considered using a space-time support vector regression model. These methods can reveal the geographic knowledge in a more comprehensive, objective, and accurate way, and play a key role in supporting the smart city applications, such as meteorological and environmental monitoring, public safety management, and urban facility planning. For example, the spatio-temporal clustering method can be used to identify the meteorological division, the spatio-temporal outliers can contribute to the detection of the abnormal distribution of urban facilities, the spatio-temporal association mining method can help discover and understand the relationship among different types of crimes, and the spatio-temporal prediction method can be employed to predict the concentration of air pollutants.

Key words: pan-spatial information system, geographic big data, multi-model characteristics, spatio-temporal analysis, spatio-temporal clustering, spatio-temporal outlier detection, spatio-temporal association mining, spatio-temporal prediction