多源地理大数据支撑下的地理事件建模与分析  

Modeling and analysis of geographic events supported by multi-source geographic big data

作　　者：杜云艳 易嘉伟[1,2] 薛存金 千家乐 裴韬 DU Yunyan;YI Jiawei;XUE Cunjin;QIAN Jiale;PEI Tao(Institute of Geographic Sciences and Natural Resources Research,CAS,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China;Aerospace Information Research Institute,CAS,Beijing 100101,China)

机构地区：[1]中国科学院地理科学与资源研究所,北京100101 [2]中国科学院大学,北京100049 [3]中国科学院空天信息创新研究院,北京100101

出　　处：《地理学报》2021年第11期2853-2866,共14页Acta Geographica Sinica

基　　金：国家重点研发计划(2017YFB0503605);国家自然科学基金项目(41901395,41671401)。

摘　　要：地理事件作为描述地理过程的基本单元,逐渐成为地理信息科学(GIS)核心研究内容。由于受人类活动数据获取限制,GIS对地理事件的建模和分析主要关注事件所引起的地理空间要素变化及要素之间的相互影响与作用机制。然而,近年来随着基于位置服务数据(LBS)爆炸式的增长和人类活动大数据定量刻画手段的快速发展,地理事件对人类活动的影响以及公众对地理事件的网络参与度都引起了多个领域的广泛关注,对地理事件的时空认知、建模方法和分析框架提出了巨大的挑战。对此,本文首先深入分析了大数据时代地理事件的概念与分类体系;其次,基于地理事件的时空语义给出了基于图模型的事件数据建模,建立了事件本体及其次生或级联事件的"节点—边"表达结构,开展了事件自身时空演化及其前"因"后"果"的形式化描述;第三,从时空数据分析与挖掘的角度,给出了大数据时代地理事件建模与分析的整体框架,拟突破传统"地理实体空间"事件探测与分析方法的局限性,融合"虚拟空间"事件发现与传播模拟思路,实现多源地理大数据支撑下的面向地理事件的人类活动多尺度时空响应与区域差异分析;最后,本文以城市暴雨事件为例诠释了本文所提出的地理事件建模与分析方法,从城市和城市内部两个尺度进行了暴雨事件与人类活动的一致性响应及区域差异分析,得到了明确的结论,验证了前文分析框架的可行性与实用性。Geographic events, as a basic construct in geographic process description, have become a core content of geographic information system(GIS). Due to the limitation of acquiring human activity data, GIS modeling and analysis of geographic events has long been focused on the event-induced changes of geospatial objects and the interaction between the objects. However, in recent years, with the explosive growth of location-based service data and the rapid development of quantitative depiction of human activities, the impact of geographic events on human activities and online social participation in geographic events have aroused wide concern in many fields, which poses great challenges to the space-time cognition,modeling methods and analysis framework of geographic events. In this regard, this study discussed the conceptualization and categorization of geographic events in the context of big data, and then introduced the space-time semantics and graph-based data model for geographic events. The "node-edge" graph data structure is used to establish event ontology, the secondary or cascading events, the evolution process, and the "cause-effect" interaction. The spatiotemporal data mining approaches for geographical events were also summarized, which are limited to conventional event detection in "physical space". Integrating "virtual space" event discovery and propagation simulation ideas into data mining approaches is essential for recognizing multi-scale spatiotemporal responses and understanding regional difference of human activities under diverse geographic events. Finally, the study used urban rainstorm events as an example to examine the conceptualization and modeling method of geographic events. Social responses to urban rainstorms and regional differences were examined at interurban and intra-urban scales. The case study proved the concept and verified the feasibility and practicability of the proposed framework.

关 键 词：地理事件 图数据模型 地理大数据 人类活动响应 

分 类 号：P208[天文地球—地图制图学与地理信息工程]