Visualized analysis framework on big geo-information data oriented spatio-temporal events

doi:10.13474/j.cnki.11-2246.2019.0395

Abstract

Abstract: Rich spatio-temporal relations are hidden in types of big geo-data, which represent the evolution of individual or collective spatio-temporal events and play a decisive role in intelligent applications such as natural resources, environments, transportation and other fields. However, there are many difficulties in digging the spatio-temporal relationship, due to the large number of attributes embedded in multi-scale and multi-semantic big geo-data. It is a technical challenge to effectively construct multi-scale and multi semantic spatio-temporal and as well as to visualize the relations. In this paper, we design an analysis framework to visualize the spatio-temporal relations among spatio-temporal events. we design a multi-view collaboration to visualize the spatio-temporal relations among events by describing a general definition for spatio-temporal relations in spatio-temporal events. A visual interface is presented for users to interactively select or filter spatial and temporal extents to guide the knowledge discovery process. Followed by experimental application on spatio-temporal trajectory data shows that this method can explore the spatio-temporal relationship between spatio-temporal events and context, and realize the interactive visual analysis of spatio-temporal events.

Key words: big geo-information data, visual analysis, spatio-temporal relations, spatio-temporal events, trajectory data

CLC Number:

P208

LI Jinlei, CI Yuyao, ZHENG Kun, GUO Shaolong, YAN Jihong, CHEN Yuping, WU Yanmin, LI Shiyi. Visualized analysis framework on big geo-information data oriented spatio-temporal events[J]. Bulletin of Surveying and Mapping, 2019, 0(12): 101-104.

References

[1] 朱递,刘瑜. 多源地理信息大数据视角下的城市动态研究[J]. 科研信息化技术与应用, 2017,8(3):7-17.
[2] 刘瑜, 詹朝晖, 朱递, 等. 集成多元地理信息大数据感知城市空间分异格局[J].武汉大学学报(信息科学版), 2018, 43(3):327-355.
[3] 朱庆, 付萧. 多模态时空大数据可视分析方法综述[J]. 测绘学报, 2017,46(10):474-479.
[4] WANG H, LI Q, YI F, et al. Influential spatial facility prediction over large scale cyber-physical vehicles in smart city[J]. Eurasip Journal on Wireless Communications and Networking, 2016,2016(1):103.
[5] 李清泉, 李德仁. 大数据GIS[J]. 武汉大学学报(信息科学版), 2014,39(6):641-644.
[6] XIAOKE H, YE Z, JING Y, et al. TrajGraph:a graph-based visual analytics approach to studying urban network centralities using taxi Trajectory data[J]. Visualization and Computer Graphics, IEEE Transactions on, 2016,22(1):160-169.
[7] LU M, WANG Z, YUAN X. Trajrank:exploring travel behaviour on a route by trajectory ranking[C]//Visualization Symposium (PacificVis).[S.l.]:IEEE, 2015:311-318.
[8] GUPTA S, DUMAS M, MCGUFFIN M J, et al. Movement-Slicer:Better Gantt charts for visualizing behaviors and meetings in movement data[C]//2016 IEEE Pacific Visualization Symposium (PacificVis).[S.l.]:IEEE, 2016:168-175.
[9] ANDRIENKO G, ANDRIENKO N, BAK P, et al. Visual analytics of movement[M].[S.l.]:Springer Science & Business Media, 2013.
[10] BOYANDIN I, BERTINI E, BAK P, et al. Flowstrates:an approach for visual exploration of temporal origin-destination data[C]//Eurographics IEEE-Vgtc Conference on Visualization.[S.l.]:IEEE, 2011:971-980.
[11] ANDRIENKO G, ANDRIENKO N, HEURICH M. An event-based conceptual model for context-aware movement analysis[J]. International Journal of Geographical Information Science, 2011,25(9):1347-1370.
[12] BAK P, MANSMANN F, JANETZKO H, et al. Spatiot-emporal analysis of sensor logs using growth ring maps[J]. IEEE Transactions on Visualization & Computer Graphics, 2009,15(6):913-920.
[13] ZHENG K, GU D P, FANG F L, et al. Visualization of spatio-temporal relations in movement event using multi-view[J]. 2017, XLII-2/W7:1469-1476.
[14] STEED C A, RICCIUTO D M, SHIPMAN G, et al. Big data visual analytics for exploratory earth system simulation analysis[J]. Computers & Geosciences, 2013,61:71-82.
[15] 刘家良, 孙立双. 结合空间相关性的城市热点路段提取方法[J]. 测绘通报, 2018(6):80-84.