Progress in Location-Based Services

The adaptation of location-based services considers mainly objective information and collections of facts. Subjective components such as emotions and opinions can provide alternative views, e.g. for supporting decision making. Therefore, research on affect analysis is carried out by capturing and analyzing location-based emotions from user generated content. The chapter presents the approach of extracting emotions from photo titles, descriptions and tags of Flickr and Panoramio pictures. The obtained emotions are documented in the valence-arousal-space as well as in emotional maps of geospace. The distribution of emotions within the valence-arousal-space represents the kinds of emotions occurring in the study area of Dresden whereas the emotional map shows the geospatial distribution. The investigation results offer further potential for an analysis regarding influencing demographic factors and their effect on spatial applications in the field of tourism.

This chapter presents an automatic methodology for the extraction of spatial road features and networks from floating car data (FCD) that was integrated with multispectral remote sensing images in metropolitan areas. This methodology is divided into two basic steps. Firstly, a spatial local statistical examination is carried out to extract the nodes of each road segment. Based on the local Moran’s I statistics, a new statistic method is developed to detect local clusters. Significance is assessed using a Monte Carlo approach to determine the probability through observing large samples under the null hypothesis of no pattern. When all the necessary nodes are detected, spatial road segments can then be organized by linking pairs of nodes, which are used as the candidate road segments for the next step. Secondly, pre-processed multispectral remote sensing images are utilised for testing those initial road segments. To prove the concept, a Metropolitan area is employed as a case study. Road segments with high significance values in the tests are selected to construct the spatial road network. The developed methodology could be adopted for the provision of high quality navigational road maps in a cost-effective manner and the experimental results are presented.

In conventional thematic cartography the visualisation techniques to symbolise spatio-temporal phenomena are limited. On a two dimensional map temporal changes can only be visualised adequately as time series or by animation. To simultaneously visualise thematic data in space and time a third dimension must be added. In this work conventional cartographic symbolization meets the space-time cube to create a holistic 3D spatio-temporal visualisation model. The two dimensional proportional symbol mapping technique is adopted and extruded into the third dimension to model the temporal factor. Kernel density estimation is performed on the time line to create a temporal continuous model from discrete points in time. The resulting visualisation model is implemented into an earth viewer to enable the user to freely navigate the phenomenon and visually detect anomalies without losing the overall view. This tool is evaluated by visualizing the events of a mobile phone location dataset over space and time in one single model.

Volunteered Geographic Information (VGI) provides valuable information to analyze human activities in space and time. In this chapter, we use Flickr photos as an example to explore the possibilities of VGI to analyze spatiotemporal patterns of tourists’ accommodation in Vienna, Austria as study site. Kernel density estimations and spatial scan statistics are used to explore the distribution of photos, while seasonality is considered additionally. The results show seasonal tendency of tourists for accommodation. It has been discovered that Flickr photos have, in general, the capability to improve tourism-related researches. In particular, they are useful to investigate spatiotemporal human activities, which open new possibilities for further location and event based analysis.

Community Mapping and Citizen Science involve members of the public in projects to address real-world problems such as noise pollution, air pollution or large-scale development in their neighbourhood. Many of these are inherently location-based and maps provide a powerful tool for engagement. Most importantly, they can be tailor-made to display information required by the drivers of these projects—different groups of people with different interests. Previous Community Mapping and Citizen Science projects allowed the public to capture data for use on such maps via web based systems. However, mobile devices offer additional means of data capture and their in-built sensing devices (microphone, accelerometer, GPS) allow participants to work with additional types of information not available on web-based systems. Although many such Applications (Apps) exist, our experience with community groups shows that flexibility is key—the groups themselves must be able to decide what information they are interested in. While it is possible to meet this need by developing a bespoke App for each group, many of the group members involved in such projects are not programmers and do not have funding for bespoke development. This paper describes the development of a location based services App platform for Community Mapping and Citizen Science. The platform allows an unlimited number of bespoke Apps to be created by a non-technical administrator, without the need for programming skills.

This chapter describes a lightweight approach for custom definition and detection of group patterns in a real-time analysis scenario, using a simple, yet flexible notion for groups of moving point objects (MPOs) travelling together. Groups are defined as sets of MPOs that are directly or transitively related to each other via freely definable binary relations. Group candidates are identified within a snapshot view of the MPOs at discrete time instances. By backtracking over previous snapshots, stable group compositions over previous time instances are identified and reported. We give insight about the used data structures and algorithms for the group candidate calculation and backtracking steps and illustrate the approach’s functionality with examples from a real data set.

With an explosive growth in the number of contributors for creating and assembling of spatial data, freely available databases and open source products have drawn the attention among decision makers for facility management and service planning. Many location-based services are using Volunteered Geographic Information (VGI) as spatial data sources. The key motivation of this work is to mine hidden patterns of social activities and the interests of contributors to share event-related knowledge within OSM community as one of the most prominent examples of user generated spatial data. In this study, the term event referred to anomalous user activities, number of contributors plus number of contributions, which happened at a time point or within a specific period of time. We focused on events which have happened and the events for which we had prior knowledge. For the purpose of retrospective event detection, it is necessary to analyse the history of OSM for the area of the event. In our case, the entire OSM history of Vechta, Munich, Los Angeles, and Sendai, around the area where the events happened was extracted to examine the potential of OSM database for event detection. Our experimental analysis reveals that while changes to OSM can be effectively rendered on the globally visible OSM maps in a few hours, citizens would not naturally use OSM as a tool to mark an event. In fact, the contributors do not treat this community in the same way as they do with other user-friendly electronic exchange platforms such as Twitter, Face book, or Flickr. The obtained results also show that for big events such as tsunami in Sendai during which the geometry of objects is affected, post-disaster, structural and environmental damages (demolished buildings, road infrastructure changes, etc.) are detectable through OSM.

Current indoor localisation systems make use of common wireless signals such as Bluetooth, WiFi to track the users inside a building. Amongst those, Bluetooth has been widely known for its low-power consumption, small maintenance cost, as well as its wide-spread amongst the commodity devices. Understanding the properties of such wireless signal definitely aids the tracking system design. However, little research has been done to understand the properties of Bluetooth wireless signal amongst the current Bluetooth-based tracking systems. In this chapter, the most important Bluetooth properties related to indoor localisation are experimentally investigated from a statistical perspective. A Bluetooth-based tracking system is proposed and evaluated with the location fingerprinting technique to incorporate the Bluetooth properties described in the chapter.

A system for pedestrian indoor localization is presented, which uses the data of an inertial sensor unit mounted on the foot of a person walking through an indoor or outdoor environment. The inertial sensor data are integrated to a position/orientation information using a classical strapdown navigation approach, while several additional sensor data and constraints, such as Zero Velocity Updates, magnetometer and barometer readings and the detection of spatially distributed RFID tags, are incorporated to the solution using an Unscented Kalman Filter. The work presents a custom sensor system development, describes the developed algorithms and evaluates several methods to reduce the drift, which usually comes with the integration of low cost inertial sensors.

Indoor localization, especially in wireless networks (WN) has become an important research focus in computer science during the past ten years. Several approaches exist to estimate a node’s position relative to other devices. Most approaches are based on distance measurements and localization algorithms. In this chapter we provide an overview of common and new localization algorithms. A detailed investigation on the error distribution and the real world behaviour of these algorithms is presented. We also provide a discussion of the evaluation results that leads to open questions and future research approaches.

Many people have difficulties getting their bearings when entering an unknown building. However, this problem can be solved by localisation and navigation on a mobile phone. This chapter presents a locating system which is based on recognising geomagnetic field disturbances and ambient light. A particle filter is applied to the locating problem. It is used to fuse together the data of both sensors and track the mobile phone. The prototypic implementation of locating takes place on an Android tablet. Different aspects of the particle filter are evaluated regarding their influence on the accuracy of locating. The tests took place in an office building. In the course of these tests an arithmetic mean locating error of 4 m was achieved.

Route planning services for a priori route planning on computers or on-demand planning on mobile devices are omnipresent, not only for vehicles but also for bicyclists or pedestrians. Furthermore, public or commercial buildings such as hospitals, hotels or shopping malls are getting bigger and their inner complexity increases. Additionally, most of the time of our lives is spent indoors, apparently quite often in unknown and foreign buildings. Consequently, the need for mature indoor route planning applications emerged and both academia and economy are now trying to adapt well known outdoor routing services to complex indoor spaces. Contrary to the outdoors, where typically commercial data providers or professional surveyors capture spatial data, it is unlikely that commercial institutes are able to capture indoor information on a large-scale. In the last couple of years, Volunteered Geographic Information (VGI) or crowdsourced geodata has increasingly gained attractiveness and the manifoldness and quality of such data has already been demonstrated in different (outdoor) applications. Trying to gain traction in the emerging field of indoor applications, OpenStreetMap (OSM) as one of the most popular VGI communities aims at taking the lead in capturing information about indoor spaces. Trying to satisfy the demand for indoor services, this chapter presents an extensive application for indoor environments. By providing indoor maps and route planning services with indoor OSM data, the here conducted work on the one hand demonstrates the possibilities arising from VGI and on the other hand provides a mature indoor application. In particular, the developed application can be used for a priori route planning at home on a personal computer as well as for on-demand route planning on a mobile device. A prototypical implementation for BlackBerry smartphones is also presented, whereas the application, due to its design and technology, can be easily ported to other mobile platforms such as Android smartphones, iPhones or iPads.

Positioning and map technology integrated to smart mobile devices allows the users to locate themselves and find routes between locations. Such route finding typically works only outdoors due to reliance on the GPS system and lack of indoor map data. This work introduces a prototype for combined indoor and outdoor mobile navigation system for a university campus. An important part of the prototype implementation is the conversion of CAD floor plans to GIS data that can be used together with existing outdoor maps for locating and for finding shortest routes between locations. This work describes a semi-automatic conversion process that produces indoor map data, which is combined with OpenStreetMap and Bing map data for route finding and displaying a hybrid map. The prototype application, which uses this data, has been implemented on the iPad. The prototype uses GPS for outdoor positioning and QR codes for indoor positioning. The work is currently in process, and future prospects of the prototype are discussed.

This article proposes a novel representation for route descriptions called an Augmented Route Sketch Map (ARSM). In this representation a route is composed of a sketch map drawn over a detailed base-map. The base-map has the effect of augmenting ones knowledge and in turn reduces the complexity of accurately representing and interpreting a route. This is demonstrated through a set of user trials. The proposed ARSM representation also facilitates the automatic parsing of route descriptions and in turn the transformation to other representations such as a turn-by-turn instructions.

Indoor navigation is becoming a most wanted application especially on the background of the wide availability of powerful personal mobile devices and new methods for indoor positioning. Existing approaches do seldom incorporate that people can move freely through e.g. big halls and are not constrained to specific lanes as vehicles are on road networks. Thereby these approaches can only approximate shortest paths and cannot benefit from possible highly accurate indoor positioning methods. In this chapter we show how the concept of visibility graphs can be applied to indoor routing and how it results in highly accurate shortest paths. We demonstrate how any accurate position can be incorporated in the automatically constructed graph. Furthermore we show how the knowledge that different levels of a building are usually sparsely interconnected can be used to speed up the well-known shortest path algorithm A* by introducing a new heuristic. In experiments we show that our approach needs 29 % less run-time than a standard A*-algorithm.

Navigating and travelling between destinations with the help of Geographic Information Systems route planning is a very common task carried out by millions of commuters daily. The route is mostly based on geocoding of the addresses given by the traveller based on static road network into digital-map positions, and thus the creation of path and directions needed to be taken. Today’s navigation data sets rarely contain information about parking lots, related to building entrances, and walking paths. This is especially relevant for large building complexes (hospitals, industrial buildings, city halls, universities). A fine-tuned route tailored for the driver requirement, e.g., park the car close-by to destination, is required in such cases to save time and frustration. The idea of this chapter is to extract this information from the navigational behaviour of users, which is accessible via an analysis of GPS traces; analysis of car commuters in relation to their point of departure and destination by analysing the walking path they took from—and to—their parked car in relation to a specific address. A classification scheme of GPS-traces is suggested, which enables to classify robustly different travel modes that compose a single GPS trace. By ascribing the classified vehicular car trace, which is accompanied by a walking path to/from the car, to a specific address, it is made feasible to extract the required ascribed data: parking places corresponding to that address. This additional data can later be added to the road network navigation maps used by the route planning scheme to enable the construction of a more fine-tuned optimal and reliable route that will prevent subsequent detours.

Navigation services are gaining much importance for all kind of human activities ranging from tourist navigation to support of rescue teams in disaster management. With the frequent natural disasters occurring in recent years, emergency navigation for first responders poses a set of serious challenges for researchers in the navigation field. The chapter introduces a taxonomy of navigation among obstacles, categorizes navigation cases on basis of type and multiplicity of first responders, destinations, and obstacles, and reviews related research. This review reveals limitations in current navigation research and challenges that have not been explored yet. We also briefly present our approach using agent-based technology, real measurements and web technologies for the development and implementation of navigation systems that aim at navigating first responders among both static and moving obstacles. Finally, we conclude by providing views on further investigations and developments.

Nowadays, mobile devices are widely used as navigation aids, e.g., for car navigation. Their greatest advantage is the ability of automatic position tracking. In indoor environments, this feature is often not available, since indoor localization techniques are not ready for the mass-market yet. What remains is a small display with limited space for route visualizations. In contrast, the variable size of paper allows for the representation of additional context information as a means for spatial understanding and orientation in space, rendering it a valuable alternative presentation medium for indoor navigation aids. Independent of the medium used, provided visualizations must meet specific cartographic requirements like clarity, comprehensibility, and expedience. Within a co-operation between geoinformation science and sociology, we develop and investigate cartographic methods for effective route guidance in indoor environments. Our evaluation base comes from user studies conducted with more than 3,000 visitors, of both genders and aged between 4 and 78 years. These user studies were collected during the “Long Nights of Science” in Berlin in 2009, 2010, 2011, and 2012. We used paper as the presentation medium for our experiments, not only for practical reasons but also because we want to confront our participants with a solution which does not align to the current trend. Within this article we put special focus on media characteristics and users’ media preferences. Therefore, we asked our participants about their opinion on the provided paper maps in contrast to mobile solutions. Based on their answers, we could derive media characteristics relevant from a user’s perspective, as well as the affinities of different user groups. One astonishing outcome was that 11–15 year-old teenagers indicate a much higher tendency towards paper maps than towards smartphone apps.

Hierarchical place descriptions are a common means for people to communicate about place. Within them hierarchically ordered elements are linked by explicit or implicit relationships. This study analyses place descriptions collected in a mobile game, investigating hierarchies based on a classification of spatial granularity. The main findings show a dominance of hierarchical structures in place descriptions, but also a considerable number of deviations. Deviations are explained by principles other than spatial granularity, such as the presence of salient features and other construction principles. We conclude the need for and significance of more flexible models of hierarchies in the interaction with users of location-based services.

As a society we are now more connected than ever before. Citizens interact with each other and form virtual communities based on a common interest or being involved in certain cultural, political, intellectual, or other issues. Volunteered Geographic Information (VGI) is generated when citizens annotate content from social media and smart devices. OpenStreetMap is a famous example of a VGI project with a very large community of contributors. This chapter examines this community and investigates the types of contributors and interactions amongst members of OSM. Our results show that there are very small groups of individuals creating and editing over 85 % of all OSM objects in three case-study cities. Editing and contribution behaviour is mostly steady and consistent over time except during months where OSM ‘mapping parties’ occur or when freely available spatial data is bulk imported into OSM. The paper also provides results of analysis into the social interaction between contributors to OSM where we show that a very small number of contributors are actively editing and maintaining the data submitted by other contributors. This indicates that most contributors work exclusively on their own data and rarely edit the work of others. Overall this paper will be of interest to LBS practitioners considering using OSM as a source of spatial data for LBS applications.

The chapter proposes a conceptual model as foundation for analyzing user contributions in the context of VGI. The conceptual model is based on a set of action and domain concepts, which are combined to a task-model describing typical tasks of volunteered geographic information contribution. As a proof-of-concept, the model is applied to two sample data sets that are extracted from the OpenStreetMap (OSM) change history. OSM data samples provide a proof-of-concept concerning the applicability of the model for crowd activity analysis. The resulting “contribution graph”, which is a graph-like structure of linked editing actions, can be used as foundation for analyzing complex contribution patterns.

Ubiquitous sensor networks and Location-based Services can potentially assist in taking decisions in near real time in a variety of application areas such as public safety, traffic management, environmental monitoring or in public health. Yet, analysing our surroundings in real time is still a major challenge due to sparsely available data sources for real-time monitoring. The innovative concept of People as Sensors defines a measurement model, in which measurements are not only taken by calibrated hardware sensors, but in which also humans can contribute their individual ‘measurements’ such as their subjective sensations, current perceptions or personal observations. This chapter contains a disambiguation between the terms People as Sensors (people contributing subjective observations), Collective Sensing (analysing aggregated anonymised data coming from collective networks) and Citizen Science (exploiting and elevating expertise of citizens and their personal, local experiences). Then, the particular significance of integrating the People as Sensors concept with established LBS, data analysis and visualisation systems is elaborated. Finally, the paper discusses current challenges, points out possible solutions, and pin-points directions for future research areas.

How to effectively schedule individual’s daily activities in actual temporal–spatial environments is a challenging task especially when considering various dynamic conditions and constraints. In this chapter, we present a prototype of a personal daily activity scheduler based on our previously developed travel information system, where point of interest (POI) information and travel information have been integrated into an individual’s agenda service. The scheduler provides all operations based on constraints checking, agenda operations (e.g., inserting, updating and deleting activities), recommending locations, detecting deviations from schedule, detecting real-time event consequences and detecting relevant POIs. Initial tests for the basic operations indicate that the approach works well and more comprehensive tests will be conducted in the future.

Paper route maps are common for tools assisting walking tours, but they are not so kind to readers. Some commercial mobile applications for walking tours are available; it is difficult in manual operation for maps. There are fewer frameworks to integrate multiple hand-drawing well-designed maps with GPS and other digital content such as photographs and audios. In this paper, we proposed a new framework for supporting walking tours with stories and maps implemented on a smartphone. The main purpose of the tool is easiness for operating the device and for finding and acquiring necessary information. Design method of map-based storytelling allows users to smoothly interact with mobile devices with location-based pull style information service. We discuss the feasibility and usefulness of our proposed framework by implementing real mobile software and testing real applications in the real world.

In this chapter the draft for a novel conceptual model for indoor navigation, the multi layered space-event model (MLSM), is combined with existing standards for Geo Web Services in order to define a framework for a 3D indoor routing service with rule-based 2D visualization. We prove that the MLSM in the test scenarios we defined can be used as a conceptual model for an indoor routing service. With the help of a prototype we also show that the MLSM needs some extensions if a 3D MLSM graph shall be visualized in 2D. Our case scenarios and visualization examples are based on an existing Building Information Model (BIM) of a specific building.

Location-Based-Services (LBS), such as route planning or Point-of-Interest (POI) search are well-known and their consumption is quite common on personal computers. Simultaneously, mobile devices, such as tablets or smart phones, penetrate the market and offer great potentials for sophisticated and advanced LBS. Based on the open and free OSM data, the current project OSM-3D has already demonstrated that such data can be perfectly used for the generation of a 3D virtual globe and for the provision of various 3D-LBS. Trying to merge the phenomenon of 3D-VGI with the increasingly available mobile devices, the here conducted work discusses potentials and challenges arising from a mobile OSM-3D application, and divides the challenges into platform adaptation, physical adaptation and scenario adaptation. For platform adaptation, this chapter selects one very promising technology for adapting OSM-3D to mobile devices by critically evaluating different technologies for the aimed work. For physical adaptation, the here conducted work discusses ideas for fast and apprehensible rendering of 3D objects on mobile devices. Since a good knowledge of context is essential for both physical adaptation and scenario adaptation, the presented framework also incorporates a tailored analysis of the mobile using context of OSM-3D. Thereby, this chapter provides a comprehensive framework and proposal towards a context-aware OSM-3D mobile application.