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

doi:10.11947/j.AGCS.2024.20230341

Acta Geodaetica et Cartographica Sinica ›› 2024, Vol. 53 ›› Issue (8): 1493-1504.doi: 10.11947/j.AGCS.2024.20230341

• The Geographical Cognition of Spatio-temporal Big Data • Previous Articles Next Articles

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)

Abstract

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

CLC Number:

P208

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.

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

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