室内定位数据分析与应用研究进展

doi:10.18306/dlkxjz.2016.05.005

摘要/Abstract

摘要：

现代人文地理学的研究越来越多地关注人的时空行为,而获取个体在出行活动中的时空位置数据是研究人类时空行为的前提。受数据获取技术的限制,之前对时空行为的研究主要集中在室外空间。随着室内定位技术的出现和应用,这类研究由室外空间扩展至室内空间。室内定位技术和方法较多,但从数据的角度来看,根据数据获取中使用定位方法的不同,可将室内定位数据分为几何位置数据、指纹位置数据和符号位置数据3类。目前,基于室内定位数据的研究可以归结为以下4个方面,即：人在室内的时空分布、人在室内的移动模式、人在室内的行为习惯及属性推断、人与室内环境的交互作用。然而,总体上目前的研究还处于探索阶段,理论和方法体系尚未成熟。本文认为后续的研究中需要关注以下问题：①数据获取方面。相对于蓝牙、射频识别、红外等定位技术,“智能手机+WiFi”模式的定位系统具有覆盖范围广、成本低廉、无需专门设备支持、易与用户交互等优势,是一种最具应用前景的室内定位技术;②研究内容方面。时空行为特征的研究是基础,个体属性推断及个体与环境的相互作用形式和机理研究将是重点,多时空尺度数据融合分析是一种趋势;③科学伦理方面。室内定位涉及微观尺度人类活动的记录,隐私保护问题需要高度关注。

关键词: 定位数据, 室内定位, 数据分析, 时空行为, 大数据, 综述

Abstract:

Human spatiotemporal behavior increasingly draws attention in modern human geography, and obtaining individuals’ spatiotemporal location data in travel activities is the precondition to study the spatiotemporal behavior of people. Limited by data acquisition technology, previous studies about behavior in space and time mostly focused on behavior in the outdoor space of a city. With the occurrence of technology for indoor positioning, the spatial scale of this type of study has been extended to the indoor space. Although there is a wide range of technologies and methods for indoor positioning, according to the manner that these data are acquired, indoor positioning data can be divided into three categories, that is, geometric location data, fingerprinting location data, and symbolic location data. Currently, studies based on indoor positioning data can be divided into the following categories: (1) spatiotemporal distribution of people in indoor space; (2) movement pattern of people in indoor space; (3) behavioral habit and attribute inference of people in indoor space; and (4) interaction between people and indoor surroundings. However, most of these studies are still at a primary stage and there are no commonly accepted theories and methodologies at present. We believe that studies in the future may pay attention to the following issues: (1) with regard to data acquisition, the positioning system accomplished through “smart phone plus WiFi” that has wider coverage and lower cost, requires no special equipment, and interacts easily compared with Bluetooth, Radio Frequency Identification (RFID), and Infrared Ray (IR),and so on, is the most promising indoor positioning technology; (2) with regard to research contents, behavioral characteristics in space and time will be the basis, individuals’ attribute inference and interaction between individuals and indoor surroundings will be the focus, and analysis of multiscale fused spatiotemporal location data will be the trend of future development; (3) in terms of scientific ethics, privacy issues must be highlighted concerning the recording of individuals’ travel activity data at the micro scale.

Key words: positioning data, indoor positioning, data analysis, spatiotemporal behavior, big data, review

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.

Hua SHU, Ci SONG, Tao PEI. Progress of studies on indoor positioning data analysis and application[J]. PROGRESS IN GEOGRAPHY, 2016, 35(5): 580-588.

图/表 2

图1

表1

参考文献 52

[1]	柴彦威, 等. 2014. 空间行为与行为空间[M]. 南京: 东南大学出版社.
	[Chai Y W, et al.2014. Spatial behavior and behavioral space[M]. Nanjing, China: Southeast University Press.]
[2]	柴彦威, 颜亚宁, 冈本耕平. 2008. 西方行为地理学的研究历程及最新进展[J]. 人文地理, 23(6): 1-6, 59.
	[Chai Y W, Yan Y N, Okamoto K.2008. Development of behavioral geographic research in western countries and its recent progress[J]. Human Geography, 23(6): 1-6, 59.]
[3]	李清泉, 周宝定. 2015. 基于智能手机的个体室内时空行为分析[J]. 地理科学进展, 34(4): 457-465.
	[Li Q Q, Zhou B D.2015. Smartphone-based individual indoor spatiotemporal behavior analysis[J]. Progress in Geography, 34(4): 457-465.]
[4]	刘瑜, 康朝贵. 2014. 人类移动模式的多尺度分析方法探索[M]//柴彦威. 时空间行为研究前沿. 南京: 东南大学出版社: 110-121.
	[Liu Y, Kang C G.2014. Renlei yidong moshi de duochidu fenxi fangfa tansuo[M]//Chai Y W. The frontiers of space-time behavior research. Nanjing, China: Southeast University Press: 110-121.]
[5]	刘瑜, 康朝贵, 王法辉. 2014. 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, 39(6): 660-666.
	[Liu Y, Kang C G, Wang F H.2014. Towards big data-driven human mobility patterns and models[J]. Geomatics and Information Science of Wuhan University, 39(6): 660-666.]
[6]	周成虎. 2015. 全空间地理信息系统展望[J]. 地理科学进展, 34(2): 129-131.
	[Zhou C H.2015. Prospects on pan-spatial information system[J]. Progress in Geography, 34(2): 129-131.]
[7]	Aitenbichler E, Mühlhäuser M.2003. An IR local positioning system for smart items and devices[C]//Proceedings of the 23rd international conference on distributed computing systems workshops. Providence, RI: IEEE: 334-339.
[8]	Anastasi G, Bandelloni R, Conti M, et al.2003. Experimenting an indoor bluetooth-based positioning service[C]//Proceedings of the 23rd international conference on distributed computing systems workshops. Providence, RI: IEEE: 480-483.
[9]	Bahl P, Padmanabhan V N.2000. RADAR: An in-building RF-based user location and tracking system[C]//Proceedings of the 19th annual joint conference of the IEEE computer and communications societies. Tel Aviv, Israel: IEEE: 2: 775-784.
[10]	Bekkali A, Sanson H, Matsumoto M.2007. RFID indoor positioning based on probabilistic RFID map and Kalman filtering[C]//Proceedings of the 3rd IEEE international conference on wireless and mobile computing, networking and communications. White Plains, NY: IEEE: 21.
[11]	Bell S, Jung W R, Krishnakumar V.2010. WiFi-based enhanced positioning systems: Accuracy through mapping, calibration, and classification[C]//Proceedings of the 2nd ACM SIGSPATIAL international workshop on indoor spatial awareness. New York: ACM: 3-9.
[12]	Bian L.2004. A conceptual framework for an individual-based spatially explicit epidemiological model[J]. Environment and Planning B: Planning and Design, 31(3): 381-395.
[13]	Delafontaine M, Versichele M, Neutens T, et al.2012. Analysing spatiotemporal sequences in Bluetooth tracking data[J]. Applied Geography, 34: 659-668.
[14]	Estrin D, Govindan R, Heidemann J, et al.1999. Next century challenges: Scalable coordination in sensor networks[C]//Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking. New York: ACM: 263-270.
[15]	Feldmann S, Kyamakya K, Zapater A, et al.2003. An indoor bluetooth-based positioning system: Concept, implementation and experimental evaluation[C]//Proceedings of international conference on wireless networks. Las Vegas, NE: DBLP: 109-113.
[16]	Fukuhara T, Tenmoku R, Okuma T, et al.2013. Measuring and evaluating real service operations with human-behavior sensing: A case study in a Japanese cuisine restaurant[C]//Proceedings of the 19th Korea-Japan joint workshop on frontiers of computer vision. Incheon, Korea: IEEE: 113-116.
[17]	Gigl T, Janssen G J M, Dizdarević V, et al.2007. Analysis of a UWB indoor positioning system based on received signal strength[C]//Proceedings of the 4th workshop on positioning, navigation and communication. Hannover, Germany: IEEE: 97-101.
[18]	Hann S, Kim J H, Jung S Y, et al.2010. White LED ceiling lights positioning systems for optical wireless indoor applications[C]//Proceedings of the 36th European conference and exhibition on optical communication. Torino, Italy: IEEE: 1-3.
[19]	Harter A, Hopper A, Steggles P, et al.2002. The anatomy of a context-aware application[J]. Wireless Networks, 8(2-3): 187-197.
[20]	Hashemian M S, Stanley K G, Knowles D L, et al.2012. Human network data collection in the wild: the epidemiological utility of micro-contact and location data[C]//Proceedings of the 2nd ACM SIGHIT international health informatics symposium. New York: ACM: 255-264.
[21]	Hazas M, Hopper A.2006. Broadband ultrasonic location systems for improved indoor positioning[J]. IEEE Transactions on Mobile Computing, 5(5): 536-547.
[22]	Hightower J, Borriello G.2001. A survey and taxonomy of location systems for ubiquitous computing[J]. Computer, 34(8): 57-66.
[23]	Huang M L, Park S C.2009. A WLAN and ZigBee coexistence mechanism for wearable health monitoring system[C]//Proceedings of the 9th international symposium on communications and information technology. Icheon, Korea: IEEE: 555-559.
[24]	Hui S K, Bradlow E T, Fader P S.2009. Testing behavioral hypotheses using an integrated model of grocery store shopping path and purchase behavior[J]. Journal of Consumer Research, 36(3): 478-493.
[25]	Kanda T, Shiomi M, Perrin L, et al.2007. Analysis of people trajectories with ubiquitous sensors in a science museum[C]//Proceedings of 2007 IEEE international conference on robotics and automation. Roma, Italy: IEEE, 4846-4853.
[26]	Kholod M, Nakahara T, Azuma H, et al.2010. The influence of shopping path length on purchase behavior in grocery store[M]//Setchi R, Jordanov I, Howlett R J, et al. Knowledge-based and intelligent information and engineering systems. Berlin & Heidelberg, Germany: Springer: 273-280.
[27]	Kim S E, Kim Y, Yoon J, et al.2012. Indoor positioning system using geomagnetic anomalies for smartphones[C]//Proceedings of international conference on indoor positioning and indoor navigation. Sydney, Australia: IEEE: 1-5.
[28]	Larranaga J, Muguira L, Lopez-Garde J M, et al.2010. An environment adaptive ZigBee-based indoor positioning algorithm[C]//Proceedings of 2010 international conference on indoor positioning and indoor navigation. Zurich, Switzerland: IEEE: 1-8.
[29]	Larson J S, Bradlow E T, Fader P S.2005. An exploratory look at supermarket shopping paths[J]. International Journal of Research in Marketing, 22(4): 395-414.
[30]	Li B H, Salter J, Dempster A G, et al.2006. Indoor positioning techniques based on wireless LAN[C]//Proceedings of the 1st IEEE international conference on wireless broadband and ultra wideband communications. Sydney, Australia: IEEE: 13-16.
[31]	Liebig T, Andrienko G, Andrienko N.2014. Methods for analysis of spatio-temporal bluetooth tracking data[J]. Journal of Urban Technology, 21(2): 27-37.
[32]	Liu H, Darabi H, Banerjee P, et al.2007. Survey of wireless indoor positioning techniques and systems[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 37(6): 1067-1080.
[33]	Liu Y, Wang F H, Xiao Y, et al.2012. Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai[J]. Landscape and Urban Planning, 106(1): 73-87.
[34]	Ni L M, Liu Y H, Lau Y C, et al.2003. LANDMARC: Indoor location sensing using active RFID[C]//Proceedings of the 1st IEEE international conference on pervasive computing and communications. Fort Worth, TX: IEEE: 407-418.
[35]	Noh H Y, Lee J H, Oh S W, et al.2012. Exploiting indoor location and mobile information for context-awareness service[J]. Information Processing & Management, 48(1): 1-12.
[36]	Pahlavan K, Krishnamurthy P, Beneat J.1998. Wideband radio propagation modeling for indoor geolocation applications[J]. IEEE Communications Magazine, 36(4): 60-65.
[37]	Pei T, Sobolevsky S, Ratti C, et al.2014. A new insight into land use classification based on aggregated mobile phone data[J]. International Journal of Geographical Information Science, 28(9): 1988-2007.
[38]	Petrenko A, Sizo A, Qian W, et al.2014. Exploring mobility indoors: An application of sensor-based and GIS systems[J]. Transactions in GIS, 18(3): 351-369.
[39]	Poulin F, Amiot L P.2002. Interference during the use of an electromagnetic tracking system under OR conditions[J]. Journal of Biomechanics, 35(6): 733-737.
[40]	Rekimoto J, Miyaki T, Ishizawa T.2007. LifeTag: WiFi-based continuous location logging for life pattern analysis[C]//Proceedings of the 3rd international symposium conference on location-and context-awareness. Oberpfaffenhofen, Germany: Springer: 35-49.
[41]	Sorensen H.2003. The science of shopping[J]. Marketing Research, 15(3): 30-35.
[42]	Steggles P, Gschwind S.2005. The Ubisense smart space platform[M]//Adjunct proceedings of the 3rd international conference on pervasive computing. Berlin, Germany: Springer.
[43]	Tröndle M, Greenwood S, Kirchberg V, et al.2012. An integrative and comprehensive methodology for studying aesthetic experience in the field: Merging movement tracking, physiology, and psychological data[J]. Environment and Behavior, 46(1): 102-135.
[44]	Tröndle M, Tschacher W.2012. The physiology of phenomenology: The effects of artworks[J]. Empirical Studies of the Arts, 30(1): 75-113.
[45]	Werb J, Lanzl C.1998. Designing a positioning system for finding things and people indoors[J]. IEEE Spectrum, 35(9): 71-78.
[46]	Wolf J, Guensler R, Bachman W.2001. Elimination of the travel diary: Experiment to derive trip purpose from global positioning system travel data[J]. Transportation Data and Information Technology: Planning and Administration, 1768(1): 125-134.
[47]	Yalowitz S S, Bronnenkant K.2009. Timing and tracking: Unlocking visitor behavior[J]. Visitor Studies, 12(1): 47-64.
[48]	Yoshimura Y, Girardin F, Carrascal J P, et al.2012. 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. Helsingborg, Sweden: Springer: 391-402.
[49]	Yoshimura Y, Sobolevsky S, Ratti C, et al.2014. An analysis of visitors’ behavior in the Louvre Museum: A study using Bluetooth data[J]. Environment and Planning B: Planning and Design, 41(6): 1113-1131.
[50]	Youssef M, Agrawala A.2004. Handling samples correlation in the Horus system[C]//Proceedings of the 23rd annual joint conference of the IEEE computer and communications societies. Hong Kong, China: IEEE: 1023-1031.
[51]	Yue Y, Zhuang Y, Li Q Q, et al.2009. Mining time-dependent attractive areas and movement patterns from taxi trajectory data[C]//Proceedings of the 17th international conference on geoinformatics. Fairfax, VA: IEEE: 689-694.
[52]	Zheng Y, Li Q N, Chen Y K, et al.2008. Understanding mobility based on GPS data[C]//Proceedings of the 10th international conference on ubiquitous computing. Seoul, Korea: ACM: 312-321.

定位方法	基本原理	系统示例(无线技术)	优缺点	应用场景	复杂度	代表性研究
三角测量法	根据待测点和坐标已知参考点之间连接形成三角形的几何特性计算待测点的坐标	RADAR(WLAN) Active Bat(Ultrasound) Ubisense(UWEB)	优点：定位精度高,理论上可实现对信号覆盖区域任意空间位置的定位; 缺点：对信号传播范围和稳定性要求较高;信号遮挡、反射和波动等会造成位置“漂移”	较空旷的室内场景(如机场、火车站大厅等)	适中	Bahl et al, 2000; Harter et al, 2002; Steggles et al, 2005
场景分析法	事先获取指定位置的信号特征参数,通过对比待测点接收到信号的特征参数与数据库中存储的各指定位置的信号特征参数,确定待测点位置	Horus(WLAN) MotionStar(Magnetic) LifeTag(WLAN)	优点：无需事先测量参考点位置坐标,定位精度高,稳定性好; 缺点：前期位置指纹数据采集工作量较大	应用场景较广泛,可用于环境复杂的室内场景	高	Youssef et al, 2004; Poulin et al, 2002; Li et al, 2006
邻近法	根据传感器信号作用范围有限的特点,确定待测点是否在参考点附近	BIPS(Bluetooth) LANDMARC(RFID) PAN(ZigBee)	优点：对传感器信号稳定性要求低,定位算法简单,实现方便; 缺点：一般需要用户主动参与,且无法记录用户空间活动轨迹的细节信息	廊道型室内场景或功能区划明确的室内场景(廊道型博物馆,商铺彼此独立的商场等)	低	Anastasi et al, 2003; Ni et al, 2003; Huang et al, 2009