We are now living in a mobile information era, which is fundamentally changing science and society. Location Based Services (LBS), which deliver information depending on the location of the (mobile) device and user, play a key role in this mobile information era. This article first reviews the ongoing evolution and research trends of the scientific field of LBS in the past years. To motivate further LBS research and stimulate collective efforts, this article then presents a series of key research challenges that are essential to advance the development of LBS, setting a research agenda for LBS to ‘positively’ shape the future of our mobile information society. These research challenges cover issues related to the core of LBS development (e.g. positioning, modelling, and communication), evaluation, and analysis of LBS-generated data, as well as social, ethical, and behavioural issues that rise as LBS enter into people’s daily lives.

This Editorial lead article for the Journal of Location Based Services surveys this complex and multi-disciplinary field and identifies the key research issues. Although this field has produced early commercial disappointments, the inevitability that pervasive location-aware services on mobile devices will emerge means that much research is needed to inform these developments. The article reviews firstly: the science and technology of positioning, geographic information science, mobile cartography, spatial cognition and interfaces, information science, ubiquitous computing; and secondly the business, content and legal, social and ethics aspects, before synthesising the key issues for this new field.

This article reviews a selected set of location–based services (LBS) that have been published in the research literature, focussing on mobile guides, transport support, gaming, assistive technology and health. The research needs and opportunities in each area are evaluated and the connections between each category of LBS are discussed. The review illustrates the enormous diversity of forms in which LBS are appearing and the wide range of application sectors that are represented. However, very few of these applications are implemented pervasively on a commercial basis as this is still challenging technically and economically.

GPS-based pedestrian navigation systems have become increasingly popular. Different interface technologies can be used to communicate/convey route directions to pedestrians. This paper aims to empirically study the influence of different interface technologies on spatial knowledge acquisition in the context of GPS-based pedestrian navigation. A field experiment was implemented to address this concern. Firstly, the suitability of the evaluation methods in assessing spatial knowledge acquisition was analyzed empirically (focusing on the ability of differentiating “familiar” and “unfamiliar” participants). The suitable methods were then used to compare the influence of mobile maps, augmented reality, and voice on spatial learning. The field test showed that in terms of spatial knowledge acquisition, the three interface technologies led to comparable results, which were not significantly different from each other. The results bring some challenging issues for consideration when designing mobile pedestrian nav...

User interfaces for geospatial information are the tools by which users interact with and explore that information. The provision of appropriate interface tools for exploiting the potential of contemporary geospatial visualization products is essential if they are to be used efficiently and effectively. This paper addresses issues and challenges in interface development and usage that are identified as paramount within the geospatial visualization community.

Eye movement data convey a wealth of information that can be used to probe human behaviour and cognitive processes. To date, eye tracking studies have mainly focused on laboratory-based evaluations of cartographic interfaces; in contrast, little attention has been paid to eye movement data mining for real-world applications. In this study, we propose using machine-learning methods to infer user tasks from eye movement data in real-world pedestrian navigation scenarios. We conducted a real-world pedestrian navigation experiment in which we recorded eye movement data from 38 participants. We trained and cross-validated a random forest classifier for classifying five common navigation tasks using five types of eye movement features. The results show that the classifier can achieve an overall accuracy of 67%. We found that statistical eye movement features and saccade encoding features are more useful than the other investigated types of features for distinguishing user tasks. We also identified that the choice of classifier, the time window size and the eye movement features considered are all important factors that influence task inference performance. Results of the research open doors to some potential real-world innovative applications, such as navigation systems that can provide task-related information depending on the task a user is performing.