时空异常探测：从数据驱动到知识驱动的内涵转变与实现路径

doi:10.11947/j.AGCS.2024.20230341

测绘学报 ›› 2024, Vol. 53 ›› Issue (8): 1493-1504.doi: 10.11947/j.AGCS.2024.20230341

• 时空大数据的地理认知专栏 • 上一篇下一篇

时空异常探测：从数据驱动到知识驱动的内涵转变与实现路径

石岩¹^,²(), 王达¹, 邓敏¹^,²^,³(), 杨学习¹^,²

^1.中南大学地球科学与信息物理学院，湖南　长沙　410083
^2.湖南省地理空间信息工程技术研究中心，湖南　长沙　410018
^3.江西师范大学地理与环境学院，江西　南昌　330022

收稿日期:2023-08-22 发布日期:2024-09-25
通讯作者: 邓敏 E-mail:csu_shiy@csu.edu.cn;csu_shiy@csu.edu.cn;dengmin@csu.edu.cn
作者简介:石岩（1988—），男，博士，教授，研究方向为地理大数据挖掘及其在国土空间规划、城市公共安全、智慧交通管控、地质灾害预警等领域的应用。E-mail：csu_shiy@csu.edu.cn
基金资助:
国家重点研发计划(2021YFB3900904);国家自然科学基金(42071452);湖南省自然科学基金(2022JJ20059);湖南省科技创新计划(2023RC3032);中南大学创新驱动计划(2023CXQD013);中南大学前沿交叉研究项目(2023QYJC002);江西省“双千计划”第三批引进类创新领军人才短期项目(jxsq2020102062)

Spatio-temporal anomaly detection: connotation transformation and implementation path from data-driven to knowledge-driven modeling

Yan SHI¹^,²(), Da WANG¹, Min DENG¹^,²^,³(), Xuexi YANG¹^,²

^1.School of Geosciences and Info-physics, Central South University, Changsha 410083, China
^2.Hunan Geospatial Information Engineering and Technology Research Center, Changsha 410018, China
^3.School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China

Received:2023-08-22 Published:2024-09-25
Contact: Min DENG E-mail:csu_shiy@csu.edu.cn;csu_shiy@csu.edu.cn;dengmin@csu.edu.cn
About author:SHI Yan (1988—), male, PhD, professor, majors in geographical big data mining and its application in territorial spatial planning, urban public security, intelligent traffic management, geological disaster warning and so on. E-mail: csu_shiy@csu.edu.cn
Supported by:
The National Key Research and Development Program of China(2021YFB3900904);The National Natural Science Foundation of China(42071452);The Hunan Provincial Natural Science Foundation of China(2022JJ20059);The Science and Technology Innovation Program of Hunan Province(2023RC3032);Central South University Innovation-driven Research Program(2023CXQD013);The Frontier Cross Research Project of Central South University(2023QYJC002);The Third Batch of Short-term Projects for Introducing Innovative Leading Talents in Jiangxi Province's “Double Thousand Plan”(jxsq2020102062)

摘要/Abstract

摘要：

时空异常探测是地理空间数据挖掘的核心技术之一，可为深层揭示地理过程演化机理提供关键突破口。在大数据与人工智能技术的推动下，从数据驱动向知识驱动转变是地理大数据时空异常智能探测的发展趋势。本文系统梳理了时空异常探测研究的发展历程与主流研究思路，剖析了数据、信息与知识间的辩证关系，并从“地理变量、空间基底、时空关系、知识类型”四位一体的视角构建了时空知识的统一描述框架。进而，结合案例阐述了时空知识与时空异常的“双向驱动”内涵，提出了“时空知识关联建模→时空异常智能识别→时空知识动态更新”的时空异常智能探测实现路径，支撑时空异常可靠探测与时空知识可信服务。

关键词: 时空异常, 地理大数据, 时空知识, 知识图谱, 深度学习

Abstract:

As one of the critical technologies of geo-spatial data mining, spatio-temporal anomaly detection has the capacity of providing key breakthroughs for deeply revealing the evolution mechanism of geographic processes. Promoted by the big data and artificial intelligence technology, the transformation from data-driven to knowledge-driven modeling is the development tendency for the intelligent detection of spatio-temporal anomalies from geographic big data. This paper systematically sorts out the development process and the mainstream study ideas of current spatio-temporal anomaly detection. Through analyzing the dialectical relationships among data, information and knowledge, a unified description framework of spatio-temporal knowledge is constructed by integrating geographic variables, space basis, spatio-temporal relationships and knowledge types. Then, the connotation of bidirectional driving between spatio-temporal knowledge and spatio-temporal anomalies is elaborated with the help of practical cases. The implementation path for intelligent detection of spatio-temporal anomalies is further proposed, which includes spatio-temporal knowledge correlation modeling, spatio-temporal anomaly intelligent detection and spatio-temporal anomaly-based knowledge dynamic updating, so as to support both the reliable spatio-temporal anomaly detection and the credible spatio-temporal knowledge services.

Key words: spatio-temporal anomaly, geographical big data, spatio-temporal knowledge, knowledge graph, deep learning

中图分类号:

P208

石岩, 王达, 邓敏, 杨学习. 时空异常探测：从数据驱动到知识驱动的内涵转变与实现路径[J]. 测绘学报, 2024, 53(8): 1493-1504.

Yan SHI, Da WANG, Min DENG, Xuexi YANG. Spatio-temporal anomaly detection: connotation transformation and implementation path from data-driven to knowledge-driven modeling[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(8): 1493-1504.

参考文献 19

[1]	王劲峰, 葛咏, 李连发, 等. 地理学时空数据分析方法[J]. 地理学报, 2014, 69(9): 1326-1345.
	WANG Jinfeng, GE Yong, LI Lianfa, et al. Spatiotemporal data analysis in geography[J]. Acta Geographica Sinica, 2014, 69(9): 1326-1345.
[2]	裴韬, 刘亚溪, 郭思慧, 等. 地理大数据挖掘的本质[J]. 地理学报, 2019, 74(3): 586-598.
	PEI Tao, LIU Yaxi, GUO Sihui, et al. Principle of big geodata mining[J]. Acta Geographica Sinica, 2019, 74(3): 586-598.
[3]	李德仁. 论时空大数据的智能处理与服务[J]. 地球信息科学学报, 2019, 21(12): 1825-1831.
	LI Deren. The intelligent processing and service of spatiotemporal big data[J]. Journal of Geo-information Science, 2019, 21(12): 1825-1831.
[4]	HAWKINS D M. Identification of outliers[M]. London: Chapman and Hall, 1980.
[5]	李德仁. 误差处理和可靠性理论：摄影测量平差的近代发展[M]. 北京: 测绘出版社, 1988.
	LI Deren. Error processing and reliability theory: the recent development of photogrammetric adjustment[M]. Beijing: Surveying and Mapping Press, 1988.
[6]	TAN Pangning, STEINBACH M, KUMAR V. Introduction to data mining[M]. Boston: Addison Wesley, 2006.
[7]	HAN Jiawei, KAMBER M, PEI Jian. Data mining: concepts and techniques[M]. 3rd ed. San Francisco: Morgan Kaufman, 2012.
[8]	徐冠华. 全社会要高度关注“数字地球”[J]. 中国测绘, 1999(3): 7-8.
	XU Guanhua. Pay much attention to the digital earth[J]. China Surveying and Mapping, 1999(3): 7-8.
[9]	陆锋, 张恒才. 大数据与广义GIS[J]. 武汉大学学报(信息科学版), 2014, 39(6): 645-654.
	LU Feng, ZHANG Hengcai. Big data and generalized GIS[J]. Geomatics and Information Science of Wuhan University, 2014, 39(6): 645-654.
[10]	ANSELIN L. Local indicators of spatial association—LISA[J]. Geographical Analysis, 1995, 27(2): 93-115.
[11]	SHEKHAR S, LU C T, ZHANG Pusheng. Detecting graph-based spatial outliers: algorithms and applications[C]//Proceedings of the 7th ACM SIG KDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM Press, 2001: 371-376.
[12]	TOBLER W R. A computer movie simulating urban growth in the Detroit region[J]. Economic Geography, 1970, 46(sup1): 234-240.
[13]	刘耀林, 刘启亮, 邓敏, 等. 地理大数据挖掘研究进展与挑战[J]. 测绘学报, 2022, 51(7): 1544-1560. DOI: 10.11947/j.AGCS.2022.20220068.
	LIU Yaolin, LIU Qiliang, DENG Min, et al. Recent advance and challenge in geospatial big data mining[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7): 1544-1560. DOI: 10.11947/j.AGCS.2022.20220068.
[14]	SHANNON C E. A mathematical theory of communication[J]. Bell System Technical Journal, 1948, 27(4): 623-656.
[15]	FAYYAD U M, PIATETSKY-SHAPIRO G, SMYTH P. From data mining to knowledge discovery in databases[J]. AI Magazine, 1996, 17(3): 37-54.
[16]	CHEN Jun, LI Zhilin, LI Songnian, et al. From digitalized to intelligentized surveying and mapping: fundamental issues and research agenda[J]. Journal of Geodesy and Geoinformation Science, 2022, 5(2): 148-160.
[17]	吴信东, 何进, 陆汝钤, 等. 从大数据到大知识：HACE+BigKE[J]. 自动化学报, 2016, 42(7): 965-982.
	WU Xindong, HE Jin, LU Ruqian, et al. From big data to big knowledge: HACE+BigKE[J]. Acta Automatica Sinica, 2016, 42(7): 965-982.
[18]	NONAKA I. A dynamic theory of organizational knowledge creation[J]. Organization Science, 1994, 5(1): 14-37.
[19]	刘瑜, 郭浩, 李海峰, 等. 从地理规律到地理空间人工智能[J]. 测绘学报, 2022, 51(6): 1062-1069. DOI: 10.11947/j.AGCS.2022.20220125.
	LIU Yu, GUO Hao, LI Haifeng, et al. A note on GeoAI from the perspective of geographical laws[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(6): 1062-1069. DOI: 10.11947/j.AGCS.2022.20220125.

时空异常探测：从数据驱动到知识驱动的内涵转变与实现路径

Spatio-temporal anomaly detection: connotation transformation and implementation path from data-driven to knowledge-driven modeling

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献 19

相关文章 15

编辑推荐

Metrics

本文评价

[1]	鄢薪, 慎利, 潘俊杰, 戴延帅, 王继成, 郑晓莉, 李志林. 多尺度特征融合与空间优化的弱监督高分遥感建筑变化检测[J]. 测绘学报, 2024, 53(8): 1586-1597.
[2]	布金伟, 余科根, 汪秋兰, 李玲惠, 刘馨雨, 左小清, 常军. 融合星载GNSS-R数据和多变量参数全球海洋有效波高深度学习反演法[J]. 测绘学报, 2024, 53(7): 1321-1335.
[3]	江利明, 邵益, 周志伟, 马培峰, 王腾. 智能化InSAR数据处理研究进展、挑战与展望[J]. 测绘学报, 2024, 53(6): 1037-1056.
[4]	郭迟, 刘阳, 罗亚荣, 刘经南, 张全. 图像语义信息在视觉SLAM中的应用研究进展[J]. 测绘学报, 2024, 53(6): 1057-1076.
[5]	龚循强, 汪宏宇, 鲁铁定, 游为. 高铁桥墩沉降的通用渐进分解长期预测网络模型[J]. 测绘学报, 2024, 53(6): 1113-1127.
[6]	顾海燕, 杨懿, 李海涛, 孙立坚, 丁少鹏, 刘世琦. 高分辨率遥感影像样本库动态构建与智能解译应用[J]. 测绘学报, 2024, 53(6): 1165-1179.
[7]	丁少鹏, 卢秀山, 刘如飞, 杨懿, 顾海燕, 李海涛. 联合目标特征引导与多重注意力的建筑物变化检测[J]. 测绘学报, 2024, 53(6): 1224-1235.
[8]	李彦胜, 吴敏郎, 张永军. 知识图谱约束深度网络的高分辨率遥感影像场景分类[J]. 测绘学报, 2024, 53(4): 677-688.
[9]	刘慧敏, 张陈为, 谌恺祺, 邓敏, 彭翀. 基于深度学习的城市PM2.5浓度时空分布预测及不确定性评估[J]. 测绘学报, 2024, 53(4): 750-760.
[10]	孙传猛, 魏宇, 李欣宇, 马铁华, 武志博. 复杂场景下无水尺水位的影像水位反演智能检测方法[J]. 测绘学报, 2024, 53(3): 558-568.
[11]	廖钊宏, 张依晨, 杨飚, 林明春, 孙文博, 高智. 基于Swin Transformer-CNN的单目遥感影像高程估计方法及其在公路建设场景中的应用[J]. 测绘学报, 2024, 53(2): 344-352.
[12]	林云浩, 王艳军, 李少春, 蔡恒藩. 一种耦合DeepLab与Transformer的农作物种植类型遥感精细分类方法[J]. 测绘学报, 2024, 53(2): 353-366.
[13]	江宝得, 黄威, 许少芬, 巫勇. 融合分散自适应注意力机制的多尺度遥感影像建筑物实例细化提取[J]. 测绘学报, 2023, 52(9): 1504-1514.
[14]	曹兴文, 郑宏伟, 刘英, 吴孟泉, 王灵玥, 包安明, 陈曦. 多行人轨迹多视角三维仿真视频学习预测法[J]. 测绘学报, 2023, 52(9): 1595-1608.
[15]	魏智威, 肖屹, 童莹, 许文嘉, 王洋. 基于知识图谱的直线型建筑物模式识别方法[J]. 测绘学报, 2023, 52(8): 1355-1363.