基于室内定位数据的群体时空行为可视化分析
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摘要
室内定位数据记录了用户在室内空间活动的时空轨迹,是研究人群室内行为的重要信息源。室内数据时空耦合、分布复杂,可视化分析可以更好地揭示其规律。然而,与室外数据不同,室内数据具有时空粒度细、定位精度高等特点,与POI之间的空间关系更为明确,其轨迹受到室内设施和空间的制约,出现高维和不规则的特征,而这给室内行为研究提供依据的同时,又给可视化分析带来一定的挑战。现有的可视化方法主要应用于室外定位数据,关注轨迹自身的活动轨迹分析,往往忽略了所经过POI语义信息表达。针对这一问题,首先分析室内空间结构与定位数据的特征,阐述室内空间可视化分析的特殊性;在此基础上,面向室内人群的时空分布、移动模式及相关POI之间的对比、关联分析的需求,细化可视化分析的内容,明确可视化分析与展示的对象,并设计数据结构;从数据结构、可视化方法、展示图件及用户交互4个层次构建时空行为可视化分析模型;基于上述方法,采用WebGIS和WebGL技术综合设计和实现了面向商场定位的商场客流分析系统;最后,通过某一大型商场的用户定位数据进行可视化分析,从而验证了研究成果的正确性和有效性。
Indoor position data records the Spatiotemporal trajectory of users' activities in indoor space and is an important source of information for studying individual behavior.The similarity with the outdoor positioning data is that the space and time of the data is coupled and distributed,and the visual analysis can better reveal its regularity.However,unlike outdoor positioning data,indoor data has characteristics such as fine granularity in space and time,high positioning accuracy,and a clearer spatial relationship with POI(Point of Interest).Its trajectory is constrained by indoor facilities and space,resulting in high dimensional and irregular characteristics.The visual analysis of these data provides a basis for indoor behavior research,but also brings certain challenges.The existing visualization methods are mainly applied to outdoor positioning data,focusing on the trajectory analysis of spatiotemporal behavior itself,and often neglecting the expression of the POI semantic information with trajectory.To solve this problem,this paper first analyzed the characteristics of indoor location data,in comparison with the particularity of outdoor spatial visualization analysis.On this basis,facing spatial-temporal behavior analysis requirements for the indoor population spatial and temporal distribution,the movement mode and the correlation between related POIs of indoor population,detailed visual analysis contents,cleared the objects for visualize analysis and presentation,and design data structures.And then,this paper constructs a spatiotemporal behavior visualization analysis model from data structure,visualization method,display map and user interaction.Based on the above methods,a passenger flow visualization analysis system was designed for shopping mall with users' Wifi positioning data and implemented by use of the technology of WebGIS(Web based Geographic Information System) and WebGL(Web Graphics Library).The system realized passenger flow analysis and display in different shops,floors and entire shopping malls in the form of two-dimensional and three-dimensional integration.Finally,correctness and effectiveness of the research results were verified through a practical example.
Indoor position data records the Spatiotemporal trajectory of users' activities in indoor space and is an important source of information for studying individual behavior.The similarity with the outdoor positioning data is that the space and time of the data is coupled and distributed,and the visual analysis can better reveal its regularity.However,unlike outdoor positioning data,indoor data has characteristics such as fine granularity in space and time,high positioning accuracy,and a clearer spatial relationship with POI(Point of Interest).Its trajectory is constrained by indoor facilities and space,resulting in high dimensional and irregular characteristics.The visual analysis of these data provides a basis for indoor behavior research,but also brings certain challenges.The existing visualization methods are mainly applied to outdoor positioning data,focusing on the trajectory analysis of spatiotemporal behavior itself,and often neglecting the expression of the POI semantic information with trajectory.To solve this problem,this paper first analyzed the characteristics of indoor location data,in comparison with the particularity of outdoor spatial visualization analysis.On this basis,facing spatial-temporal behavior analysis requirements for the indoor population spatial and temporal distribution,the movement mode and the correlation between related POIs of indoor population,detailed visual analysis contents,cleared the objects for visualize analysis and presentation,and design data structures.And then,this paper constructs a spatiotemporal behavior visualization analysis model from data structure,visualization method,display map and user interaction.Based on the above methods,a passenger flow visualization analysis system was designed for shopping mall with users' Wifi positioning data and implemented by use of the technology of WebGIS(Web based Geographic Information System) and WebGL(Web Graphics Library).The system realized passenger flow analysis and display in different shops,floors and entire shopping malls in the form of two-dimensional and three-dimensional integration.Finally,correctness and effectiveness of the research results were verified through a practical example.
引文
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[2]周成虎.全空间地理信息系统展望[J].地理科学进展2015,34(2):129-131.[Zhou C H.Prospects on pan-spatial information system[J].Progress in Geography,201534(2):129-131.]
[3]邓中亮,张森杰,焦继超,等.基于高精度室内位置感知的大数据研究与应用[J].计算机应用,2016,36(2):295-300.[Deng Z L,Zhang S J,Jiao J C,et al.Research and application of high-precision indoor location-aware big data[J].Journal of Computer Applications,2016,36(2)295-300.]
[4]Delafontaine M,Versichele M,Neutens T,et al.Analysing spatiotemporal sequences in Bluetooth tracking data[J].Applied Geography,2012,34:659-668.
[5]刘瑜,康朝贵,王法辉.大数据驱动的人类移动模式和模型研究[J].武汉大学学报·信息科学版,2014,39(6):660-666.[Liu Y,Kang C G,Wang F H.Towards big datadriven human mobility patterns and models[J].Geomatics and Information Science of Wuhan University,201439(6):660-666.]
[6]王祖超,袁晓如.轨迹数据可视分析研究[J].计算机辅助设计与图形学学报,2015,27(1):9-25.[Wang Z CYuan X R.Visual analysis of trajectory data[J].Journa of Computer-Aided Design&Computer Graphics,201527(1):9-25.]
[7]蒲剑苏,屈华民,倪明选.移动轨迹数据的可视化[J].计算机辅助设计与图形学学报,2012,24(10):1273-1282[Pu J S,Qu H M,Ni M X.Survey on visualization of trajectory data[J].Journal of Computer-Aided Design&Computer Graphics,2012,24(10):1273-1282.]
[8]Petrenko A,Sizo A,Qian W,et al.Exploring mobility indoors:An application of sensor-based and GIS systems[J].Transactions in GIS,2014,18(3):351-369.
[9]Yoshimura Y,Sobolevsky S Y,Ratti C,et al.An analysis of visitors'behavior in the Louvre Museum:A study using Bluetooth data[J].Environment and Planning B:Planning and Design,2014,41(6):1113-1131.
[10]Yoshimura Y,Sobolevsky S Y,Girardin F,et al.New tools for studying visitor behaviours in museums:A case study at the Louvre[C].Proceedings of the international conference on information and communication technologies in tourism,2012.
[11]陈为,沈则潜,陶煜波.数据可视化[M].北京:电子工业出版社,2013:11-23.[Chen W,Shen Z Q,Tao Y B.Data visualization[M].Beijing:Publishing House of Electronics Industry,2013:11-13.]
[12]金培权,汪娜,张晓翔,等.面向室内空间的移动对象数据管理[J].计算机学报,2015,38(9):1777-1795.[Jin P Q,Wang N,Zhang X X,et al.Moving object data management for indoor spaces[J].Chinese Journal of Computer,2015,38(9):1777-1795.]
[13]汪娜.面向室内空间的时空数据管理[D].合肥:中国科学技术大学,2014:11-13.[Wang N.Research on key techniques of spatio-temportal data management for indoor space[D].Hefei:University of Science and Technology of China,2014:11-13.]
[14]应申,朱利平,李霖,等.基于室内空间特征的室内地图表达[J].导航定位学报,2015,3(4):74-78,91.[Ying S,Zhu LP,Li L,et al.Representation of indoor maps with the analysis of indoor space[J].Journal of Navigation and Positioning,2015,3(4):74-78,91.]
[15]Kim J,Kang H,Lee T,et al.Topology of the Prism Model for 3D indoor spatial objects[C].Taipei:Tenth international conference on mobile data management:Systems,Services and Middleware,2009:698-703.
[16]舒华,宋辞,裴韬.室内定位数据分析与应用研究进展[J].地理科学进展,2016,35(5):580-588.[Shu H,Song C,Pei T.Progress of studies on indoor positioning data analysis and application[J].Progress in Geography,2016,35(5):580-588.]
[17]王倩.室内移动对象轨迹相似性度量与应用[D].合肥:中国科学技术大学,2015:15-17.[Wang Q.Similarity measurement and application of indoor moving-object trajectories[D].Heifei:University of Science and Technology of China,2015:15-17.]
[18]Castells M.An introduction to the information age[J].City,1997,2(7):6-16.
[19]王行风,汪云甲.一种顾及拓扑关系的室内三维模型组织和调度方法[J].武汉大学学报·信息科学版,2017,42(1):35-42.[Wang X F,Wang Y J.Organization and scheduling of indoor three-dimensional geometric model based on spatial topological relation[J].Geomatics and Information Science of Wuhan University,2017,42(1):35-42.]
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