- Email: [email protected]
Advances in Location-Based Services
C H A P T E R
7 Advances in Location-Based Services Georg Gartner Vienna University of Technology, Vienna, Austria
O U T L I N E 7.1 Introduction
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7.2 Development Phases of LBS
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7.3 S ome Emerging Research Topics99
7.3.1 Indoor Navigation 7.3.2 Integrating Social Media 7.3.3 Interface Design 7.4 Conclusion
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7.1 INTRODUCTION Information is available anytime and anywhere nowadays. In its provision and delivery it is increasingly tailored to the user's context and needs. Location and maps play a key role in what information is provided and how it is presented. Location-based Services (LBS) are widespread and are being used with increasing frequency in a truly ubiquitous manner. Individuals often feel spatially blind without access to their LBS, which enables them to see who or what is near them and receive supporting information. They can also conduct searches on their current location, and collect data on their site in an accurate and timely manner. Mobile technologies have demonstrated their huge potential and change how we work, how we live and how we interact. The paradigm of Cybercartography, introduced by Taylor (1997, 2005) and further developed in this volume, is a conceptual framework for these developments, as it clearly emphasizes the ‘holistic approach where both mapping as a process and the map as a product are expanded’ (Taylor, 1997). In this chapter, a number of major developments that are influencing the development of LBS, especially as they affect cartography, are discussed. Developments in the Theory and Practice of Cybercartography, Second Edition, ISSN 1363-0814 http://dx.doi.org/10.1016/B978-0-444-62713-1.00007-6
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© 2014 Elsevier B.V. All rights reserved.
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7.2 DEVELOPMENT PHASES OF LBS LBS are very much a development of the last decade but three phases in what is an increasingly rapid development can be determined. It should be noted that given the relatively short time-frame involved these phases are not distinct and developments have been more of a continuous process. The early stages of LBS developments were basically driven by technology (Gartner, 2005). Whenever innovative positioning, telecommunication, or device technologies were introduced, new ideas, applications, and products in the domain of LBS were triggered. That period was very much characterized by efforts to deal with research questions such as ‘How to locate or track a user?’ as well as data modeling and data representation on small display devices. Significant progress was made in determining the positions of devices and users in both outdoor and indoor scenarios as well as in mixed environments and in 3D. The need for real-time tracking was tackled as well as attempts to combine direct and indirect positioning methods by using smart environment sensors. The initial period of LBS development was also strongly influenced by the rapid availability of an increasing number of new handheld devices. By creating more and better availability and accessibility to sensors, processing performance and communication channels, these handheld devices became quickly both smart and mass-market acceptable. A number of applications were developed influenced simply by the availability of a new smart device, including those that took the idea of LBS into new domains such as the entertainment and gaming, business, disaster management, and urban and regional planning, among others. Most of those applications were constrained by the limitations of the technologies available, forcing many users and use scenarios to adapt to the technical constraints rather than having systems available that had been designed with particular uses and users in mind. The second phase was characterized by data-driven approaches. With the number of available technologies that have become available and the pace of technological innovations slowing, the high quality of mass-market technologies resulted in a shift of the focus to the availability of data. A major challenge was to overcome the obstacles between data acquisition and data consumption as well as to establish interoperability between the three main parallel worlds of authoritative, commercial and open data providers. The development of technical, legal, and economic standards and procedures was of great benefit to the LBSenvironment as these helped to define accessibility and availability of data in an acceptable way. Additional data acquisition and usage from ubiquitous sensory sources as well as from user behaviour and use patterns was now not only possible technically but also was embedded in a legal and economic framework. Dealing with volunteered data posed new challenges, such as the necessity to learn about the motivation, the structure, and the representativeness of the volunteers as well as developing mechanism to determine the reliability of the available data. These and other aspects of volunteered geographical information are discussed in Chapter 4 of this volume. The huge quantities of data being generated created a requirement for enhanced data management systems. The need to address this problem drove one of the main trends – an increasing use of and reliance on Big Data technologies – technologies that enable the analysis of vast quantities of information within useable and practical time frames. Massively scalable, distributed systems for processing unstructured and semistructured data emerged as a result of this need, and became widely accepted and
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relied upon in the management and interpretation of geospatial information. Given the vast amount of data being generated, particularly through use of the Web, and the need to make sense of this data, the ability to link information on the Web became increasingly important. The network built on increasing numbers of sensors and the resulting increase in data, produced a hyperconnected environment or ‘Internet of Things’. Location provides a vital link between the sensors that generate the Internet of Things and thus LBS making use of location are key elements to make use of these networks. Finally, the third major and current phase of LBS developments can be characterized as focusing on the individual user and thus fitting the conceptual framework of Cybercartography, which is increasingly user-centric (Taylor, Chapter 1). Smart LBS support decisionmaking either on a collaborative or an individual level, help to act in space, support spatial enabled societies, enhance spatial awareness, or simply support daily routines. Everything happens somewhere, and thus location is a key selector for what we do and how we do it. In this phase, context-awareness is as interesting research and development topic as use and user modeling or developing adaptive algorithms. Cartography and data presentation remain the language through which the data explosion is spatially interpreted. The fact that increasing amounts of geospatial information are consumed and interpreted through mobile devices also led to the development of new visualization techniques including 3D and especially 4D – representations. The successful development of LBS requires integrated interdisciplinary approaches from domains such as computer science, communication science, human–computer interaction, telecommunication sciences, cognitive sciences, law, economics, geospatial information management, and cartography to allow human-centred application developments by applying innovative engineering methods and tools in a highly volatile technological framework. In this respect, the interdisciplinary nature of LBS is very similar to that of cybercartography. The rapidly increasing use of geospatial information has led to a growing recognition amongst both governments and the private sector that an understanding of location and place is a vital component of effective decision-making. Citizens with no recognized expertise in geospatial information and who are unlikely to even be familiar with the term are also increasingly using and interacting with geospatial information and in some cases they are contributing to its collection as several chapters in this volume illustrate. A number of important technologydriven trends have had a major impact, creating previously unimaginable amounts of location-referenced information thus making LBS a central focus of research and development and an important component of cybercartography.
7.3 SOME EMERGING RESEARCH TOPICS There is a number of important emerging research topics. Three of these will be considered here – indoor navigation, integrating social media and interface design.
7.3.1 Indoor Navigation Outdoor navigation has been a research topic for LBS applications from the outset. However, indoor navigation is now emerging as a research and development focus.
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Research has shown that people tend to lose orientation a lot easier within buildings than outdoor (Radoczky, 2003; Hohenschuh, 2004). Indoor navigation systems are designed to meet this need. Navigation systems have three major components: positioning, route planning, and route communication/presentation (Huang and Gartner, 2010). In terms of positioning, outdoor navigation often employs global positioning system (GPS), which is not usually available for indoor navigation. Positioning in indoor navigation is often provided with the help of WiFi, Radio-Frequency Identification (RFID), Ultra-Wideband (UWB), Bluetooth, and dead reckoning. With the exception of dead reckoning, other positioning techniques require infrastructure that is not ubiquitously available. Due to the differences between outdoor and indoor data, route-planning techniques in outdoor navigation are not easily applied to an indoor navigation. Route planning in indoor environments is strongly influenced by the positioning of structures such as doors and corridors, which may change location frequently and need to be modeled in a real-time manner. In terms of route communication, different interface techniques such as mobile maps, verbal instructions, 3D, and augmented reality (AR) could all be useful. However, the contents or instructions must be adapted to the indoor environment, which is restricted, smaller, and has different landmarks. A similar comparison is given by Karimi (2011) that analyses the differences between outdoor and indoor navigations with respect to travel modes supported, map data, impacting factors, and routing criteria and concludes that these differences mainly stem from the differences in physical spaces in which navigation takes place. The early research on indoor navigation mainly focused on how to obtain reliable and accurate positioning by using technologies and techniques like WiFi, RFID, and read reckoning (Gartner et al., 2004; Retscher, 2007; Zhang et al., 2009). While indoor positioning is still a research focus, recent research has started to explore the aspects of indoor data modeling, indoor route planning, and route communication. In contrast to the outdoors, where data are often standardized and readily available from commercial data providers and mapping agencies, indoor data have not yet been standardized or widely collected. Afyouni et al. (2012) classify indoor spatial models into geometric and symbolic spatial models. They conclude that geometric models can efficiently integrate metric properties to provide highly accurate location and distance information, and that they require semantic annotations for different application needs. On the other hand, symbolic models semantically represent indoor space with human-recognizable objects (e.g. rooms) as well as their properties and relationships (e.g. connectedness), but lack geometric details on entities and places represented in space. Afyouni et al. (2012) propose that hybrid models of indoor space should be developed. Other similar classifications of indoor models and recommendations for the use of hybrid models can be found in Worboys (2011). Data about indoor spaces can be obtained from field surveying or extracted from Computer-Aided Design (CAD)s. Recently, following the success of mapping outdoors by using crowdsourcing approaches, the OpenStreetMap (OSM) community has begun to map indoor environment. Different data schema proposals have been developed, but these vary substantially from each other. One of the most popular schemes is IndoorOSM proposed by Goetz and Zipf (2011). However, it is important to note that there is currently no community-agreed standard schema (Goetz and Zipf, 2013) that hinders the mapping activities of indoor spaces. Another challenge of indoor data modeling and collecting is the consideration of real-time aspects, which has not been well addressed in literature.
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Route planning is a basic element in navigation, and aims at computing an optimal route between an origin and a destination. Vanclooster and Maeyer (2012) provide a state of the art review of common route planners, such as Bing and Google Maps, and conclude that due to a lack of available indoor data, most existing route planners do not incorporate indoor route calculation. In the research community, Yuan and Schneider (2010) propose a model to construct an indoor navigation network from architectural plans, and use the network to support length-dependent optimal routing. Accessibility of network elements is also considered. Liu and Zlatanova (2011) propose a ‘door-to-door’ route-planning algorithm based on 2D floor plan. The algorithm first determines a coarse route between rooms (doors), and then computes a detailed route for each room. Dijkstra's algorithm is used to compute shortest or fastest routes (in normal situations) or safest routes (in emergencies). However, the paper does not indicate how to measure or calculate a safety coefficient. Schougaard et al. (2012) extract navigation graphs and visibility graphs from building information (e.g. in Industry Foundation Classes IFC standard), and these two graphs are then employed to calculate a shortest route. In contrast to the approaches using floor plans or building information, Goetz and Zipf (2013) process IndoorOSM data to automatically derive a Weighted Indoor-Routing Graph, and this graph is then used to compute a shortest path between an origin and a destination. It is important to note that current route-planning approaches mainly aim for shortest routes. This is probably due to the fact that objective measures such as distance and time are easier to identify for routing graphs. Since people usually do not follow the shortest routes when navigating, more work should be done on providing routing with other characteristics, such as attractiveness, simplicity, and safety. In terms of route communication (i.e. conveying route instructions/guidance), Huang and Gartner (2010) provide a survey on the related issues, and find that compared to outdoor navigation, which mainly uses smartphones with mobile maps and verbal instructions, indoor navigation uses various client platforms with more presentation forms, e.g. digital signs on public displays, vibration on wrist devices, and AR with wearable computers. Butz et al. (2001) argue that route communication should deal with the inaccuracy of position and direction information. For example, when accurate positioning information is available, a simple navigation instruction such as an arrow can be given, but when position or direction information is uncertain, more information must be given in order for the user to understand the navigation instructions. The PerPosNav platform addresses this issue, and proposes four kinds of interfaces for route communication in indoor environments with various positioning accuracies: augmented signs (e.g. presenting route information on digital signs or public displays), mobile maps, verbal instructions, mobile AR (Schougaard et al., 2012). The PerPosNav platform also provides a tracking service to obtain information of whether the users are on route or not, and where they are on the route. The approach of augmented signs and mobile maps was evaluated in two real-world experiments, of which both received positive feedbacks from the users. It is important to note that the concept of landmark has not been well addressed in the existing research. However, as emphasized in the literature, landmarks play an essential role during navigation. In order to provide more effective navigation guidance, a landmark-based approach should be developed. This is still challenging as taxonomy for indoor landmarks and the way to derive them has not been well studied. Considerable progress has been made for indoor navigation, but more research is required on indoor data model, and indoor routing and communication.
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7.3.2 Integrating Social Media Recently, with increasing ubiquity of GPS-enabled devices, location-based social network and media-sharing services are quickly growing in popularity, such as FourSquare, Facebook Place, and Eventful. In the meantime, many traditional social media (such as Flick and Twitter) are also allowing users to geotag their information. As a result, more and more georeferenced data are becoming available. On the one hand, LBS can be viewed as a tool for contributing georeferenced social media data. On the other hand, such data also enable a variety of novel LBS applications such as in the ‘emotional mapping’ project (Klettner and Schmidt, 2012), where subjective relations to places can be expressed in situ by using mobile devices. This allows the production of a rich database of subjective perceptions of places in an urban environment. This demonstrates the role of LBS maps and holistic instruments as conceived in the Cybercartography framework (Figures 7.1 and 7.2).
FIGURE 7.1 Emotional Mapping project of TU Vienna: Harvesting subjective perceptions of space by mobile phone apps and allowing adapted routing due to optimization based on emotional, subjective perceptions. Source: Map data: OpenStreetMap and Contributors, CC-BY-SA.
FIGURE 7.2 A screenshot of the AR-based interface, with a real-world camera view, route overlay, street names, and relevant landmarks. Source: © Salzburg Research.
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Much research focuses on studying people's travel behaviours (Jankowski et al., 2010; Zheng et al., 2011). These approaches often start with building a database of travel trajectories, and apply some data mining techniques for identifying locations of attraction and frequent travel sequences that are very important for LBS applications. There is also research focusing on constructing travel itineraries based on geotagged photos on the Web (De Choudhury et al., 2010). Metadata (e.g. timestamp, tags, and GPS) of the photos are used to generate past tourists' travel trails, which are then combined to generate travel itineraries for future tourists. Research attention is also being given to modeling users' preferences from their information on social media, and directly developing smart LBS based on the information extracted. For example, McKenzie and Raubal (2011) argue that the data contributed to online social networks not only offer insight to the reasons why people travel from location to location, but also offer a means of predicting future activities and travel. They study these data to extract social constraints for LBS, and thus provide socially relevant information to LBS users. Ye et al. (2011) provide Point of Interest (POI) recommendations by considering user preference, social influence and geographical influence, which are all derived from social media, such as FourSquare. With real datasets, they show that the proposed solution significantly outperforms a wide spectrum of alternative recommendation approaches. Levandoski et al. (2012) use location-aware ratings to recommend nearby features. The datasets used are social data from Foursquare and a part of MovieLens movie recommendation data. Waga et al. (2012) propose a context-aware personalized recommendation system on web and mobile devices to recommend services, photos, and GPS routes in a user's surrounding based on user-generated data collection. The relevance of items is determined by location, content, time, and network. Savage et al. (2012) learn user preferences by mining a person's social network profile (e.g. FourSquare). This information is then combined with physical constraints such as current location and transportation mode to provide LBS users with location recommendations. The research discussed above mainly focuses on using information from a single social media source, which might only reflect parts of users' preferences and activities. Combining data from different social media will contribute to a better understanding of users, and based on this, more relevant services can be provided for LBS users. However, this issue is still very challenging due to the fact that social media data are often very noisy, unstructured, and contain heterogeneous and multilingual content.
7.3.3 Interface Design While the early LBS tend to use mobile map and verbal/written descriptions for communicating relevant information to users, recent research attention has been also paid to explore the suitability of other interface technologies such as AR and haptic for LBS. Tactile Wayfinder (Pielot and Boll, 2010) uses a belt with tactile output to convey navigation information. The direction of the next waypoint and the waypoint after that is given by vibration with different rhythm and intensity on the belt. Similarly, Jacob et al. (2012) provide the user with location, orientation, and distance information using varying vibration patterns. Evaluation shows that with the haptic solution, the user can point and query the availability of POI along the street, and follow the route without increased cognitive burden of interaction with the mobile interface. The authors conclude that Haptics is powerful for
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conveying quantitative information. AR has attracted much interest in the last few years. Wikitute's World Browser (http://www.wikitude.com/) is one of the most popular ARs, and it supports users' exploration of the real-world environment by adding virtual information to the camera views. Walther-Franks (2007) has developed two image-based AR prototypes for navigation purposes, implementing implicit instructions (‘follow me’) and explicit instructions (‘go there’). The evaluation shows that test persons using the implicit route visualization of a virtual path diverged less from the route as turning instructions were grasped more clearly. However, in a field experiment, Rehrl et al. (2012) compare AR with voice and mobile maps, and find that compared to voice and mobile maps, AR leads to a higher cognition load and lower usability. This is probable due to the fact that AR is relatively new and still suffers from poor and unstable positioning and direction information. Research has also addressed the issues of multimodal interaction in LBS. For example, the EU project HaptiMap (http://www.haptimap.org/) investigates how, and in what ways, multimodal feedback can both augment and replace visual feedback for diverse users in diverse situations. The project has developed tools that make it easier for developers to add adaptable multimodal components in LBS applications. Different applications have been developed in this project to illustrate multimodal interaction. It is important to note that existing interface techniques are mainly designed to help LBS users solve their tasks (e.g. wayfinding) with less cognitive load. Other important aspects such as the side effects of these techniques, such as poor spatial knowledge acquisition as shown in Ishikawa and Montello (2006), Münzer et al. (2006) and Huang et al. (2012), have seldom been addressed. As people rely more and more on LBS, developing solutions to alleviate these kinds of side effects has become a challenge.
7.4 CONCLUSION In this chapter, major aspects of the ongoing development of LBS are discussed. Mapbased LBS include aspects of integrative positioning, context-adapted data modeling, and trends like indoor positioning, data mining from social media, and innovative presentation interfaces. The importance of LBS within the context of Cybercartography is clear as LBS address many of the same issues in both theory and practice.
References Afyouni, I., Ray, C., Claramunt, C., 2012. Spatial models for context-aware indoor navigation systems: a survey. Journal of Spatial Information Science 4. Butz, A., Baus, J., Krüger, A., Lohse, M., 2001. A hybrid indoor navigation system. In: Proceedings of the 6th International Conference on Intelligent User Interfaces, ACM, New York, NY, USA, pp. 25–32. De Choudhury, M., Feldman, M., Amer-Yahia, S., Golbandi, N., Lempel, R., Yu, C., 2010. Automatic construction of travel itineraries using social breadcrumbs. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, ACM, New York, NY, USA, pp. 35–44. Gartner, G., Frank, A., Retscher, G., 2004. Pedestrian navigation system - in mixed indoor/outdoor environment – the NAVIO project. In: Proceedings of the CORP 2004 and Geomultimedia 2004 Symposium, Vienna, pp. 165–171. Gartner, G., 2005. TeleCartography: a new means of GeoCommunication. In: Taylor, D.R.F. (Ed.), Cybercartogaphy – Theory and Practice, Elsevier, pp. 373–387. Goetz, M., Zipf, A., 2011. Extending OpenStreetMap to indoor environments: bringing volunteered geographic information to the next level. In: Urban and Regional Data Management: UDMS Annual, pp. 47–58.
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Goetz, M., Zipf, A., 2013. Indoor route planning with volunteered geographic information on a (mobile) web-based platform. In: Krisp, J.M. (Ed.), Progress in Location-Based Services, Springer, Berlin Heidelberg, pp. 211–231. Hohenschuh, F., 2004. Prototyp eines mobilen Navigationssystems fuer die Stadt Hamburg. Master Thesis. Hamburg University. Huang, H., Gartner, G., 2010. A survey of mobile indoor navigation systems. In: Gartner, G., Ortag, F. (Eds.), Cartography in Central and Eastern Europe, Springer, Berlin/Heidelberg, pp. 305–319. Huang, H., Schmidt, M., Gartner, G., 2012. Spatial knowledge acquisition with mobile maps, augmented reality and voice in the context of GPS-based pedestrian navigation: results from a field test. Cartography and Geographic Information Science 39 (2), 107–116. Ishikawa, T., Montello, D.R., 2006. Spatial knowledge acquisition from direct experience in the environment: individual differences in the development of metric knowledge and the integration of separately learned places. Cognitive Psychology 52 (2), 93–129. Jacob, R., Mooney, P., Winstanley, A., 2012. What's up that street? Exploring streets using a haptic GeoWand. In: Gartner, G., Ortag, F. (Eds.), Advances in Location-Based Services, Springer, Berlin Heidelberg, pp. 91–105. Jankowski, P., Andrienko, N., Andrienko, G., Kisilevich, S., 2010. Discovering landmark preferences and movement patterns from photo postings. Transactions in GIS 14 (6), 833–852. Karimi, H.A., 2011. Universal Navigation on Smart Phones. Springer. Klettner, S., Schmidt, M., 2012. Visualisierung von Emotionen im Raum. In: Reder, C. (Ed.), Kartographisches Denken, Springer, Wien, pp. 398–399. Levandoski, J.J., Sarwat, M., Eldawy, A., Mokbel, M.F., 2012. LARS: a location-aware recommender system. In: Proceedings of the 2012 IEEE 28th International Conference on Data Engineering, IEEE Computer Society, Washington, DC, USA, pp. 450–461. Liu, L., Zlatanova, S., 2011. A “door-to-door” path-finding approach for indoor navigation. In: Proceedings Gi4DM 2011: GeoInformation for Disaster Management. McKenzie, G., Raubal, M., 2011. Adding social constraints to location based services. In: Proceedings of the 8th International Symposium on Location-Based Services. Münzer, S., Zimmer, H.D., Schwalm, M., Baus, J., Aslan, I., 2006. Computer-assisted navigation and the acquisition of route and survey knowledge. Journal of Environmental Psychology 26 (4), 300–308. Pielot, M., Boll, S., 2010. Tactile wayfinder: comparison of tactile waypoint navigation with commercial pedestrian navigation systems. In: Floréen, P., Krüger, A., Spasojevic, M. (Eds.), Pervasive Computing, Springer, Berlin Heidelberg, pp. 76–93. Radoczky, V., 2003. Kartographische Unterstuetzungsmoeglichkeiten zur Routenbeschreibung in Fussgaenger Navigationssystemen im In- und Outdoorbereich. Master Thesis. Vienna University of Technology. Rehrl, K., Häusler, E., Steinmann, R., Leitinger, S., Bell, D., Weber, M., 2012. Pedestrian navigation with augmented reality, voice and digital map: results from a field study assessing performance and user experience. In: Gartner, G., Ortag, F. (Eds.), Advances in Location-Based Services, Springer, Berlin Heidelberg, pp. 3–20. Retscher, G., 2007. Augmentation of indoor positioning systems with a barometric pressure sensor for direct altitude determination in a multi-storey building. Cartography and Geographic Information Science 34 (4), 305–310. Savage, S., Baranski, M., Elva Chavez, N., Höllerer, T., 2012. I'm feeling LoCo: a location based context aware recommendation system. In: Gartner, G., Ortag, F. (Eds.), Advances in Location-Based Services, Springer, Berlin Heidelberg, pp. 37–54. Schougaard, K.R., Grønbæk, K., Scharling, T., 2012. Indoor pedestrian navigation based on hybrid route planning and location modeling. In: Kay, J., Lukowicz, P., Tokuda, H., Olivier, P., Krüger, A. (Eds.), Pervasive Computing, Springer, Berlin Heidelberg, pp. 289–306. Taylor, D.R.F., 1997. Maps and mapping in the information era. Keynote address to the 18th ICC, Stockholm, Sweden In: Ottoson, L. (Ed.), Proceedings, vol. 1. Swedish Cartographic Society, Gävle, pp. 1–10. Taylor, D.R.F., 2005. Cybercartography: Theory and Practice. Elsevier, Amsterdam 574 pp. Vanclooster, A., Maeyer, P.D., 2012. Combining indoor and outdoor navigation: the current approach of route planners. In: Gartner, G., Ortag, F. (Eds.), Advances in Location-Based Services, Springer, Berlin Heidelberg, pp. 283–303. Waga, K., Tabarcea, A., Fränti, P., 2012. Recommendation of points of interest from user generated data collection. In: IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing. Walther-Franks, B., 2007. Augmented Reality on Handhelds for Pedestrian Navigation. Master Thesis. University of Bremen. Worboys, M., 2011. Modeling indoor space. In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness, ACM, New York, NY, USA, pp. 1–6.
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Ye, M., Yin, P., Lee, W.-C., Lee, D.-L., 2011. Exploiting geographical influence for collaborative point-of-interest recommendation. Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, New York, NY, USA, pp. 325–334. Yuan, W., Schneider, M., 2010. iNav: an indoor navigation model supporting length-dependent optimal routing. In: Painho, M., Santos, M.Y., Pundt, H. (Eds.), Geospatial Thinking, Springer, Berlin Heidelberg, pp. 299–313. Zhang, K., Zhu, M., Retscher, G., Wu, F., Cartwright, W., 2009. Three-dimension indoor positioning algorithms using an integrated RFID/INS system in multi-storey buildings. In: Gartner, G., Rehrl, K. (Eds.), Location Based Services and TeleCartography II, Springer, Berlin Heidelberg, pp. 373–386. Zheng, Y.-T., Li, Y., Zha, Z.-J., Chua, T.-S., 2011. Mining travel patterns from GPS-tagged photos. In: Lee, K.-T., Tsai, W.-H., Liao, H.-Y., Chen, T., Hsieh, J.-W., Tseng, C.-C. (Eds.), Advances in Multimedia Modeling, Springer, Berlin/ Heidelberg, pp. 262–272.
7 Advances in Location-Based Services Georg Gartner Vienna University of Technology, Vienna, Austria
O U T L I N E 7.1 Introduction
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7.2 Development Phases of LBS
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7.3.1 Indoor Navigation 7.3.2 Integrating Social Media 7.3.3 Interface Design 7.4 Conclusion
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7.1 INTRODUCTION Information is available anytime and anywhere nowadays. In its provision and delivery it is increasingly tailored to the user's context and needs. Location and maps play a key role in what information is provided and how it is presented. Location-based Services (LBS) are widespread and are being used with increasing frequency in a truly ubiquitous manner. Individuals often feel spatially blind without access to their LBS, which enables them to see who or what is near them and receive supporting information. They can also conduct searches on their current location, and collect data on their site in an accurate and timely manner. Mobile technologies have demonstrated their huge potential and change how we work, how we live and how we interact. The paradigm of Cybercartography, introduced by Taylor (1997, 2005) and further developed in this volume, is a conceptual framework for these developments, as it clearly emphasizes the ‘holistic approach where both mapping as a process and the map as a product are expanded’ (Taylor, 1997). In this chapter, a number of major developments that are influencing the development of LBS, especially as they affect cartography, are discussed. Developments in the Theory and Practice of Cybercartography, Second Edition, ISSN 1363-0814 http://dx.doi.org/10.1016/B978-0-444-62713-1.00007-6
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7.2 DEVELOPMENT PHASES OF LBS LBS are very much a development of the last decade but three phases in what is an increasingly rapid development can be determined. It should be noted that given the relatively short time-frame involved these phases are not distinct and developments have been more of a continuous process. The early stages of LBS developments were basically driven by technology (Gartner, 2005). Whenever innovative positioning, telecommunication, or device technologies were introduced, new ideas, applications, and products in the domain of LBS were triggered. That period was very much characterized by efforts to deal with research questions such as ‘How to locate or track a user?’ as well as data modeling and data representation on small display devices. Significant progress was made in determining the positions of devices and users in both outdoor and indoor scenarios as well as in mixed environments and in 3D. The need for real-time tracking was tackled as well as attempts to combine direct and indirect positioning methods by using smart environment sensors. The initial period of LBS development was also strongly influenced by the rapid availability of an increasing number of new handheld devices. By creating more and better availability and accessibility to sensors, processing performance and communication channels, these handheld devices became quickly both smart and mass-market acceptable. A number of applications were developed influenced simply by the availability of a new smart device, including those that took the idea of LBS into new domains such as the entertainment and gaming, business, disaster management, and urban and regional planning, among others. Most of those applications were constrained by the limitations of the technologies available, forcing many users and use scenarios to adapt to the technical constraints rather than having systems available that had been designed with particular uses and users in mind. The second phase was characterized by data-driven approaches. With the number of available technologies that have become available and the pace of technological innovations slowing, the high quality of mass-market technologies resulted in a shift of the focus to the availability of data. A major challenge was to overcome the obstacles between data acquisition and data consumption as well as to establish interoperability between the three main parallel worlds of authoritative, commercial and open data providers. The development of technical, legal, and economic standards and procedures was of great benefit to the LBSenvironment as these helped to define accessibility and availability of data in an acceptable way. Additional data acquisition and usage from ubiquitous sensory sources as well as from user behaviour and use patterns was now not only possible technically but also was embedded in a legal and economic framework. Dealing with volunteered data posed new challenges, such as the necessity to learn about the motivation, the structure, and the representativeness of the volunteers as well as developing mechanism to determine the reliability of the available data. These and other aspects of volunteered geographical information are discussed in Chapter 4 of this volume. The huge quantities of data being generated created a requirement for enhanced data management systems. The need to address this problem drove one of the main trends – an increasing use of and reliance on Big Data technologies – technologies that enable the analysis of vast quantities of information within useable and practical time frames. Massively scalable, distributed systems for processing unstructured and semistructured data emerged as a result of this need, and became widely accepted and
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relied upon in the management and interpretation of geospatial information. Given the vast amount of data being generated, particularly through use of the Web, and the need to make sense of this data, the ability to link information on the Web became increasingly important. The network built on increasing numbers of sensors and the resulting increase in data, produced a hyperconnected environment or ‘Internet of Things’. Location provides a vital link between the sensors that generate the Internet of Things and thus LBS making use of location are key elements to make use of these networks. Finally, the third major and current phase of LBS developments can be characterized as focusing on the individual user and thus fitting the conceptual framework of Cybercartography, which is increasingly user-centric (Taylor, Chapter 1). Smart LBS support decisionmaking either on a collaborative or an individual level, help to act in space, support spatial enabled societies, enhance spatial awareness, or simply support daily routines. Everything happens somewhere, and thus location is a key selector for what we do and how we do it. In this phase, context-awareness is as interesting research and development topic as use and user modeling or developing adaptive algorithms. Cartography and data presentation remain the language through which the data explosion is spatially interpreted. The fact that increasing amounts of geospatial information are consumed and interpreted through mobile devices also led to the development of new visualization techniques including 3D and especially 4D – representations. The successful development of LBS requires integrated interdisciplinary approaches from domains such as computer science, communication science, human–computer interaction, telecommunication sciences, cognitive sciences, law, economics, geospatial information management, and cartography to allow human-centred application developments by applying innovative engineering methods and tools in a highly volatile technological framework. In this respect, the interdisciplinary nature of LBS is very similar to that of cybercartography. The rapidly increasing use of geospatial information has led to a growing recognition amongst both governments and the private sector that an understanding of location and place is a vital component of effective decision-making. Citizens with no recognized expertise in geospatial information and who are unlikely to even be familiar with the term are also increasingly using and interacting with geospatial information and in some cases they are contributing to its collection as several chapters in this volume illustrate. A number of important technologydriven trends have had a major impact, creating previously unimaginable amounts of location-referenced information thus making LBS a central focus of research and development and an important component of cybercartography.
7.3 SOME EMERGING RESEARCH TOPICS There is a number of important emerging research topics. Three of these will be considered here – indoor navigation, integrating social media and interface design.
7.3.1 Indoor Navigation Outdoor navigation has been a research topic for LBS applications from the outset. However, indoor navigation is now emerging as a research and development focus.
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Research has shown that people tend to lose orientation a lot easier within buildings than outdoor (Radoczky, 2003; Hohenschuh, 2004). Indoor navigation systems are designed to meet this need. Navigation systems have three major components: positioning, route planning, and route communication/presentation (Huang and Gartner, 2010). In terms of positioning, outdoor navigation often employs global positioning system (GPS), which is not usually available for indoor navigation. Positioning in indoor navigation is often provided with the help of WiFi, Radio-Frequency Identification (RFID), Ultra-Wideband (UWB), Bluetooth, and dead reckoning. With the exception of dead reckoning, other positioning techniques require infrastructure that is not ubiquitously available. Due to the differences between outdoor and indoor data, route-planning techniques in outdoor navigation are not easily applied to an indoor navigation. Route planning in indoor environments is strongly influenced by the positioning of structures such as doors and corridors, which may change location frequently and need to be modeled in a real-time manner. In terms of route communication, different interface techniques such as mobile maps, verbal instructions, 3D, and augmented reality (AR) could all be useful. However, the contents or instructions must be adapted to the indoor environment, which is restricted, smaller, and has different landmarks. A similar comparison is given by Karimi (2011) that analyses the differences between outdoor and indoor navigations with respect to travel modes supported, map data, impacting factors, and routing criteria and concludes that these differences mainly stem from the differences in physical spaces in which navigation takes place. The early research on indoor navigation mainly focused on how to obtain reliable and accurate positioning by using technologies and techniques like WiFi, RFID, and read reckoning (Gartner et al., 2004; Retscher, 2007; Zhang et al., 2009). While indoor positioning is still a research focus, recent research has started to explore the aspects of indoor data modeling, indoor route planning, and route communication. In contrast to the outdoors, where data are often standardized and readily available from commercial data providers and mapping agencies, indoor data have not yet been standardized or widely collected. Afyouni et al. (2012) classify indoor spatial models into geometric and symbolic spatial models. They conclude that geometric models can efficiently integrate metric properties to provide highly accurate location and distance information, and that they require semantic annotations for different application needs. On the other hand, symbolic models semantically represent indoor space with human-recognizable objects (e.g. rooms) as well as their properties and relationships (e.g. connectedness), but lack geometric details on entities and places represented in space. Afyouni et al. (2012) propose that hybrid models of indoor space should be developed. Other similar classifications of indoor models and recommendations for the use of hybrid models can be found in Worboys (2011). Data about indoor spaces can be obtained from field surveying or extracted from Computer-Aided Design (CAD)s. Recently, following the success of mapping outdoors by using crowdsourcing approaches, the OpenStreetMap (OSM) community has begun to map indoor environment. Different data schema proposals have been developed, but these vary substantially from each other. One of the most popular schemes is IndoorOSM proposed by Goetz and Zipf (2011). However, it is important to note that there is currently no community-agreed standard schema (Goetz and Zipf, 2013) that hinders the mapping activities of indoor spaces. Another challenge of indoor data modeling and collecting is the consideration of real-time aspects, which has not been well addressed in literature.
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Route planning is a basic element in navigation, and aims at computing an optimal route between an origin and a destination. Vanclooster and Maeyer (2012) provide a state of the art review of common route planners, such as Bing and Google Maps, and conclude that due to a lack of available indoor data, most existing route planners do not incorporate indoor route calculation. In the research community, Yuan and Schneider (2010) propose a model to construct an indoor navigation network from architectural plans, and use the network to support length-dependent optimal routing. Accessibility of network elements is also considered. Liu and Zlatanova (2011) propose a ‘door-to-door’ route-planning algorithm based on 2D floor plan. The algorithm first determines a coarse route between rooms (doors), and then computes a detailed route for each room. Dijkstra's algorithm is used to compute shortest or fastest routes (in normal situations) or safest routes (in emergencies). However, the paper does not indicate how to measure or calculate a safety coefficient. Schougaard et al. (2012) extract navigation graphs and visibility graphs from building information (e.g. in Industry Foundation Classes IFC standard), and these two graphs are then employed to calculate a shortest route. In contrast to the approaches using floor plans or building information, Goetz and Zipf (2013) process IndoorOSM data to automatically derive a Weighted Indoor-Routing Graph, and this graph is then used to compute a shortest path between an origin and a destination. It is important to note that current route-planning approaches mainly aim for shortest routes. This is probably due to the fact that objective measures such as distance and time are easier to identify for routing graphs. Since people usually do not follow the shortest routes when navigating, more work should be done on providing routing with other characteristics, such as attractiveness, simplicity, and safety. In terms of route communication (i.e. conveying route instructions/guidance), Huang and Gartner (2010) provide a survey on the related issues, and find that compared to outdoor navigation, which mainly uses smartphones with mobile maps and verbal instructions, indoor navigation uses various client platforms with more presentation forms, e.g. digital signs on public displays, vibration on wrist devices, and AR with wearable computers. Butz et al. (2001) argue that route communication should deal with the inaccuracy of position and direction information. For example, when accurate positioning information is available, a simple navigation instruction such as an arrow can be given, but when position or direction information is uncertain, more information must be given in order for the user to understand the navigation instructions. The PerPosNav platform addresses this issue, and proposes four kinds of interfaces for route communication in indoor environments with various positioning accuracies: augmented signs (e.g. presenting route information on digital signs or public displays), mobile maps, verbal instructions, mobile AR (Schougaard et al., 2012). The PerPosNav platform also provides a tracking service to obtain information of whether the users are on route or not, and where they are on the route. The approach of augmented signs and mobile maps was evaluated in two real-world experiments, of which both received positive feedbacks from the users. It is important to note that the concept of landmark has not been well addressed in the existing research. However, as emphasized in the literature, landmarks play an essential role during navigation. In order to provide more effective navigation guidance, a landmark-based approach should be developed. This is still challenging as taxonomy for indoor landmarks and the way to derive them has not been well studied. Considerable progress has been made for indoor navigation, but more research is required on indoor data model, and indoor routing and communication.
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7.3.2 Integrating Social Media Recently, with increasing ubiquity of GPS-enabled devices, location-based social network and media-sharing services are quickly growing in popularity, such as FourSquare, Facebook Place, and Eventful. In the meantime, many traditional social media (such as Flick and Twitter) are also allowing users to geotag their information. As a result, more and more georeferenced data are becoming available. On the one hand, LBS can be viewed as a tool for contributing georeferenced social media data. On the other hand, such data also enable a variety of novel LBS applications such as in the ‘emotional mapping’ project (Klettner and Schmidt, 2012), where subjective relations to places can be expressed in situ by using mobile devices. This allows the production of a rich database of subjective perceptions of places in an urban environment. This demonstrates the role of LBS maps and holistic instruments as conceived in the Cybercartography framework (Figures 7.1 and 7.2).
FIGURE 7.1 Emotional Mapping project of TU Vienna: Harvesting subjective perceptions of space by mobile phone apps and allowing adapted routing due to optimization based on emotional, subjective perceptions. Source: Map data: OpenStreetMap and Contributors, CC-BY-SA.
FIGURE 7.2 A screenshot of the AR-based interface, with a real-world camera view, route overlay, street names, and relevant landmarks. Source: © Salzburg Research.
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Much research focuses on studying people's travel behaviours (Jankowski et al., 2010; Zheng et al., 2011). These approaches often start with building a database of travel trajectories, and apply some data mining techniques for identifying locations of attraction and frequent travel sequences that are very important for LBS applications. There is also research focusing on constructing travel itineraries based on geotagged photos on the Web (De Choudhury et al., 2010). Metadata (e.g. timestamp, tags, and GPS) of the photos are used to generate past tourists' travel trails, which are then combined to generate travel itineraries for future tourists. Research attention is also being given to modeling users' preferences from their information on social media, and directly developing smart LBS based on the information extracted. For example, McKenzie and Raubal (2011) argue that the data contributed to online social networks not only offer insight to the reasons why people travel from location to location, but also offer a means of predicting future activities and travel. They study these data to extract social constraints for LBS, and thus provide socially relevant information to LBS users. Ye et al. (2011) provide Point of Interest (POI) recommendations by considering user preference, social influence and geographical influence, which are all derived from social media, such as FourSquare. With real datasets, they show that the proposed solution significantly outperforms a wide spectrum of alternative recommendation approaches. Levandoski et al. (2012) use location-aware ratings to recommend nearby features. The datasets used are social data from Foursquare and a part of MovieLens movie recommendation data. Waga et al. (2012) propose a context-aware personalized recommendation system on web and mobile devices to recommend services, photos, and GPS routes in a user's surrounding based on user-generated data collection. The relevance of items is determined by location, content, time, and network. Savage et al. (2012) learn user preferences by mining a person's social network profile (e.g. FourSquare). This information is then combined with physical constraints such as current location and transportation mode to provide LBS users with location recommendations. The research discussed above mainly focuses on using information from a single social media source, which might only reflect parts of users' preferences and activities. Combining data from different social media will contribute to a better understanding of users, and based on this, more relevant services can be provided for LBS users. However, this issue is still very challenging due to the fact that social media data are often very noisy, unstructured, and contain heterogeneous and multilingual content.
7.3.3 Interface Design While the early LBS tend to use mobile map and verbal/written descriptions for communicating relevant information to users, recent research attention has been also paid to explore the suitability of other interface technologies such as AR and haptic for LBS. Tactile Wayfinder (Pielot and Boll, 2010) uses a belt with tactile output to convey navigation information. The direction of the next waypoint and the waypoint after that is given by vibration with different rhythm and intensity on the belt. Similarly, Jacob et al. (2012) provide the user with location, orientation, and distance information using varying vibration patterns. Evaluation shows that with the haptic solution, the user can point and query the availability of POI along the street, and follow the route without increased cognitive burden of interaction with the mobile interface. The authors conclude that Haptics is powerful for
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conveying quantitative information. AR has attracted much interest in the last few years. Wikitute's World Browser (http://www.wikitude.com/) is one of the most popular ARs, and it supports users' exploration of the real-world environment by adding virtual information to the camera views. Walther-Franks (2007) has developed two image-based AR prototypes for navigation purposes, implementing implicit instructions (‘follow me’) and explicit instructions (‘go there’). The evaluation shows that test persons using the implicit route visualization of a virtual path diverged less from the route as turning instructions were grasped more clearly. However, in a field experiment, Rehrl et al. (2012) compare AR with voice and mobile maps, and find that compared to voice and mobile maps, AR leads to a higher cognition load and lower usability. This is probable due to the fact that AR is relatively new and still suffers from poor and unstable positioning and direction information. Research has also addressed the issues of multimodal interaction in LBS. For example, the EU project HaptiMap (http://www.haptimap.org/) investigates how, and in what ways, multimodal feedback can both augment and replace visual feedback for diverse users in diverse situations. The project has developed tools that make it easier for developers to add adaptable multimodal components in LBS applications. Different applications have been developed in this project to illustrate multimodal interaction. It is important to note that existing interface techniques are mainly designed to help LBS users solve their tasks (e.g. wayfinding) with less cognitive load. Other important aspects such as the side effects of these techniques, such as poor spatial knowledge acquisition as shown in Ishikawa and Montello (2006), Münzer et al. (2006) and Huang et al. (2012), have seldom been addressed. As people rely more and more on LBS, developing solutions to alleviate these kinds of side effects has become a challenge.
7.4 CONCLUSION In this chapter, major aspects of the ongoing development of LBS are discussed. Mapbased LBS include aspects of integrative positioning, context-adapted data modeling, and trends like indoor positioning, data mining from social media, and innovative presentation interfaces. The importance of LBS within the context of Cybercartography is clear as LBS address many of the same issues in both theory and practice.
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