Clustering and visualization of indoor position trajectory

doi:10.13474/j.cnki.11-2246.2019.0142

Abstract

Abstract: The analysis of indoor mobile object trajectory data is an application foundation with important commercial value such as retail promotion, indoor space planning, and advertising bidding. It is also an indispensable part in public safety and emergency solutions. In recent years, it has received more and more attention from researchers. In order to realize the cluster analysis of indoor moving object trajectory, this paper proposes a comprehensive analysis method combining DBSCAN clustering algorithm with visualization.This paper uses DBSCAN algorithm to cluster the indoor trajectory data based on mobile phone wifi information collected in a shopping mall building, analyzes the obtained clustering results and information, and provides certain reference information for the layout planning and store adjustment of the mall.Finally, the indoor trajectory data of the mall building is visualized and displayed, and the display effect is compared with the clustering result to verify each other.

Key words: cluster analysis, DBSCAN algorithm, heat map, visualization

CLC Number:

P228

YUAN Debao, WANG Bingling, YAN Yu, ZHOU Shiqiang, LIANG Chen. Clustering and visualization of indoor position trajectory[J]. 测绘通报, 2019, 0(5): 21-24.

References

[1] 黄川林. 室内移动对象轨迹分析研究[D]. 合肥:中国科学技术大学,2014.
[2] 牟乃夏,徐玉静,张恒才,等.移动轨迹聚类方法研究综述[J].测绘通报,2018(1):1-7.
[3] 袁冠. 移动对象轨迹数据挖掘方法研究[D]. 徐州:中国矿业大学,2012.
[4] LEE J G,HAN J W,WHANG K Y. Trajectory clustering:apartition-and-group framework[C]//Proceedings of the ACM SIGMOD International Conference on Management of Data. Beijing:[s.n.], 2007:593-604.
[5] KREVELD MV,LUO J. The definition and computation of trajectory and sub-trajectory similarity[C]//Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems. Seattle, Washington:[s.n.],2007:324-327.
[6] DYKES J A, MOUNTAIN D M. Seeking structure in records of spatio-temporal behaviour:visualization issues,efforts and applications[J]. Computational Statistics & Data Analysis, 2003,43(4):581-603.
[7] CAMARGO S J, ROBERTSONA, GAFFNEY S, et al. Cluster analysis of western North Pacific tropical cyclone tracks[C]//Proceedings of the 26th Conference on Hurricanes and Tropical Meteorology.[S.l.]:American Geophysical Union, 2004:250-251.
[8] CHEN Ping,HUO Qiuyan,XU Xuezhou.Clustering network-constrained uncertain trajectories[C]//Proceedings of the 8th International Conference on Fuzzy Systems and Knowledge Discovery.[S.l.]:IEEE, 2011:1657-1662.
[9] 冯锦海,杨连贺,蒋鑫龙. 基于多模式的室内兴趣点推荐系统设计与实现[J]. 计算机工程,2015,41(8):273-278,285.
[10] 杨国清,祝国瑞,喻国荣.可视化与现代地图学的发展[J].测绘通报,2004(6):40-42.
[11] 杨名. 室内定位数据可视化技术研究与实现[D]. 上海:上海交通大学, 2012.
[12] 权宇澄,吴健平.基于改进DBSCAN方法的移动对象轨迹停留分析[J].测绘与空间地理信息,2016,39(10):140-143,146.
[13] 王芳. 传统聚类方法的分析及改进[D].长沙:中南大学,2007.

[1]	HE Biao, GUO Renzhong, ZHANG Chen, MA Ding, WANG Weixi, HONG Wuyang, CHEN Yebin. A natural scene construction method for digital twin cities [J]. Bulletin of Surveying and Mapping, 2022, 0(7): 87-92.
[2]	MENG Qiuyu, SONG Ziang, WANG Jin, GE Zhijin. Detection and extraction of urban road intersections using GPS trajectories of floating vehicles [J]. Bulletin of Surveying and Mapping, 2021, 0(9): 59-63.
[3]	CHEN Huanxin, MA Chao, ZHANG Jicai, CHEN Changlin, QIN Zhiqiang, MA Jingzhen, WANG Yaqun. The visual analysis of multi-source vector spatial data usability evaluation [J]. Bulletin of Surveying and Mapping, 2021, 0(9): 74-78.
[4]	LAN Guiwen, TANG Yulong, DU Yongliang, PAN Zhuguang, MO Xuqi. Multi-scale geometric and semantic modeling of drainage network [J]. Bulletin of Surveying and Mapping, 2021, 0(3): 123-127.
[5]	REN Lifu. Application analysis of underground pipeline modeling based on BIM technology [J]. Bulletin of Surveying and Mapping, 2021, 0(2): 149-152.
[6]	ZHANG Yingdong, GENG Zengtao, WANG Haiyin, QIAO Xin, ZHOU Shengchuan. Dynamic ship vector plotting method for large-scale real-time data [J]. Bulletin of Surveying and Mapping, 2021, 0(12): 140-144.
[7]	ZHANG Hao, ZHANG Jianqin, GUO Xiaogang, LU Jian, LU Hao. Cloud storage and heatmap generation method of trajectory big data [J]. Bulletin of Surveying and Mapping, 2021, 0(10): 146-149.
[8]	BAI Yang, KANG Huitao, ZHANG Wenchao, JIA Yuna, ZHENG Hao. Application of UAV in open-pit mine monitoring [J]. Bulletin of Surveying and Mapping, 2020, 0(9): 85-88.
[9]	DU Xiao, ZHOU Yuxiu, ZHOU Qi, ZHENG Yi, ZHANG Junhui, LIN Shangwei, CHEN Lijun. Design and experiment of heat map rules based on road network constraints [J]. Bulletin of Surveying and Mapping, 2020, 0(7): 18-21.
[10]	LUO Yong, FU Hongyun. Design of space and time dimension seismic catalog display system [J]. Bulletin of Surveying and Mapping, 2020, 0(6): 141-144.
[11]	GAO Zhiwen, ZHANG Xuedong, XU Zhijie, ZHANG Jianqin, SHEN Zhaomu, GUO Xiaogang, YANG Ming. A space-time visualization analysis method for urban public bicycles connecting to the subway [J]. Bulletin of Surveying and Mapping, 2020, 0(3): 48-55.
[12]	LIU Tao, ZHANG Xiaohui, HU Tian, LIU Yongqiang. Life convenience analysis of Lanzhou residential area based on the colorimetric co-location rule excavation [J]. Bulletin of Surveying and Mapping, 2020, 0(2): 137-141,146.
[13]	REN Dongyu, DONG Xianmin, ZHOU Qi, LUO Hao, YANG Jun. A method of river networks relationship establishing for river chief system application [J]. Bulletin of Surveying and Mapping, 2020, 0(12): 122-127.
[14]	XU Jiayuan, WEN Xinjian, WANG Simin, PENG Huier. Construction of one-stop geographic big data intelligent platform [J]. Bulletin of Surveying and Mapping, 2020, 0(12): 132-137.
[15]	HAO Huaixu, WAN Taili, LUO Nianxue. High-resolution remote sensing image classification using OpenStreetMap data [J]. 测绘通报, 2019, 0(7): 69-72,126.