It is common in mobile augmented reality (AR) applications to use clues or visual markers to indicate the location of points of interest (POIs) that are outside the current field of view of the camera or screen. These clues can take many forms, such as arrows, symbols, or text labels, and are typically overlaid on the camera view or displayed in a separate area of the screen. Visual clues indicating relevance, orientation, and distance to off-screen objects help users better understand their surroundings and explore or navigate more effectively. This paper compares 4 proposed focus+context techniques with visual clues to off-screen POIs' position and distance: a modified 2D arrow and three arc-based representations. Moreover, we apply already known techniques to represent POIs' relevance and to solve the visual cluttering in environments with many POIs. The results of a user study revealed that the participants preferred the 2D arrow technique and could find the most relevant POI faster with it, even with a large number of objects. However, the EqualArc technique reduces error rates for POI selection accuracy.

One of the most popular eSports (electronic sports) game types practiced is the Multiplayer Online Battle Arena (MOBA) genre, represented by one of the most popular competitive games, League of Legends (LoL). As in many traditional sports, to improve player and team performance, players and coaches analyze all the game events, such as, the positions and trajectories of the players, representing their movements, events and actions they performed during the game (spatial and temporal data). This paper presents VisuaLeague, a visualization tool for analysis of LoL matches for single player, teams, professional matches, and multiple games. The tool offers interaction with the visualizations, filtering and aggregation of data, and clustering to solve the common problems presented in analysis with voluminous amount of data, like cluttering and overlapping. We evaluated VisuaLeague through a user study covering the various types of analysis with two professional coaches. Results indicate that the tool was overall intuitive, useful, efficient and innovative and coaches show a particular interest in the analysis of professional training matches and multiple games as those provide visualizations that often lack in common tools, specially, regarding spatio-temporal data.

In this paper we propose RoseTrajVis, a visual analytics system for studying trajectories using rose diagrams, inspired in a classic visualization technique for aggregating data. The rose diagram is composed of sectors (petals) that aggregate trajectories given their direction and show their speeds within an area around a point in a map. It also includes colored arrows on the border that point to the origin and destination of the trajectories and show their average speed. RoseTrajVis enables users to place rose diagrams over a map view and interact with them by applying time, speed and distance to origin/destination filters, and by adjusting the aggregation radius, allowing the visualization of commuting patterns in a city. We prepared a prototype of RoseTrajVis with taxi data, and evaluated the completion times and errors of users carrying out trajectory analysis tasks, and also collected feedback from a domain expert. Results revealed that completion times varied with task complexity, being longer for tasks requiring exploratory analysis of the trajectories on a map, and that almost no errors were made by users. The expert mentioned that RoseTrajVis provides an innovative way of analysing aggregate trajectories and has the potential to be useful in several professional areas.

Augmented Reality (AR) applications for mobile devices are common nowadays, however the information superimposed over the image captured by the device's camera is limited to its field of view. To address this problem in the visualization of points of interest (POI), we have combined the AR view with the display of the POI's location in a map to increase user awareness about nearby POI. It gives insight about the orientation and distance between the POI and the user. We developed 3 solutions: (1) including cues to off-screen POI in a frame at the border of the AR view and providing an alternative map view; (2) combining the AR view with a mini-map superimposed on its top left corner; and (3) combining the AR view with a circular radar view on its top left corner. A usability study performed to compare these approaches revealed that, although all the approaches were positively appreciated, the solution with a mini-map was the preferred one.

The phenomenon of eSports has been increasing over the years, including the interest in online video games, by players, coaches and analysts. One of the most popular genres of these games is the Multiplayer Online Battle Arena (MOBA) and games like League of Legend (LoL) have increased its player base and event viewership. Similarly to other sports, players and coaches analyze the games and all the games events, such as players' movements and spatial, temporal events in which they participate, to understand how they play and to define new strategies to help them progress. This paper presents the VisuaLeague II tool that helps players and coaches to analyze LoL game data using animated maps and other visualizations; and presents a comparative study between VisuaLeague II and other two tools (LoL replay system and OP.GG) to understand if the proposed visualization techniques are adequate for analyzing player's performance. The results suggest that players are capable of visualizing game-related data through the use of animated maps, to solve common analytic tasks and the comparison suggests that participants prefer to visualize spatio-temporal data dynamically, i.e., using animated maps or the replay system.

Over the last years, with the increasing use of telemetry techniques, the extraction of data from video games became a much easier and reliable task. Among many types of video games, MOBAs (Multiplayer Online Battle Arena) are one of the most widely played and, consequently, the analysis of game events has received attention of players, spectators, coaches and analysts to assess the performance of players. This data can range from players' position, to more specific events, such as, the position of a player's death. Our main goal is to get a better understanding of which techniques are more adequate to handle the visualization of this type of spatio-temporal information data, associated to player performance analysis in video games. This paper addresses this problem presenting a user study to evaluate the adequacy of animated maps and the analytical strategies followed by players when using spatio-temporal data to analyse player performance. To support the user study, we developed the VisuaLeague prototype for the visualization of in-game player trajectories and events during a match in the MOBA game League of Legends. The results support the adequacy of using the animated maps technique to convey information to users in this context. Moreover, they also point out towards a high degree of importance given to the spatio-temporal components of the data for player performance analysis.

The field of Information Visualization seeks to identify the general principles of visualization, and makes use of these principles to propose new forms of visualization for specific types of data. Included in these different data types, it was identified a data type which was not thoroughly explored. The notion of entities where each entity is composed by different attributes, and one of these attributes is composed by one picture which invokes a strong feeling of familiarity to the user. The information that we are attempting to visualize is the most basic type of data structure, a table, where the number of entities to visualize should be higher than we can humanely count, yet smaller than a few thousands. In the field of visualization we identified a niche where the main focus is the image, and despite that it has a vast number of applicable scenarios, it hadn't been properly explored. One of the major attempts at doing so, was by Microsoft Live Labs and it demonstrated limitations that will be addressed by our approach. In order to evaluate the proposed forms of visualization they will be applied and evaluated with the Deloitte Portugal organizational case-study.

The integration of sensors in smartphones and tablets is enabling the emergence of augmented reality (AR) applications for these devices. However, the image captured and the superimposed information is limited to the camera's field-of-view and, in the visualization of points of interest (PoI), symbol overlapping may occur because there is no control over their number and location. Thus, we developed an Android prototype application that aims to: provide better environment awareness using clues to off-screen objects and a 2D map with a radar view, reduce symbol overlapping via aggregation visualizations, and guide the attention of the user by featuring different levels of symbol relevance according to user preferences. An empirical study with search tasks revealed participants preferred having all techniques active instead of none or only some of them. This suggests there is value in combining visualization techniques to address mobile AR challenges, and, possibly, that there is still space to integrate more techniques without overloading the users.

Nowadays, recommendation services that allow users to obtain the information needed in their vicinity have become very popular among users of mobile devices. In fact, these systems guide the user to choose amongst the available information, identifying and making suggestions of relevant information based on user's preferences and context information. Furthermore, this is particular important in mobile devices with small screens, where it is essential that what is shown is truly relevant for the user in order to help him/her in the decision process. Several mobile recommender systems have been proposed but there still exist open challenges. This paper proposes BomApetite a mobile system to recommend restaurants to a group, based on the preferences of all the group participants, which integrates restaurant information from well-known platforms. The recommendation strategy considers the importance the user gives to each platform. To support group decision making the system uses a method that determines the best alternatives for the group from individual preferences and provides a voting process so that the group can reach a consensus. The results of a user study assessing the usability and usefulness of the system showed that overall participants had a favorable opinion.

We present an AR application for visualizing solar radiation data on facades of buildings, generated from LiDAR data and climatic observations. Data can be visualized using colored surfaces and glyphs. A user study revealed the proposed AR visualizations were easy to use, which can lead to leverage the potential benefits of AR visualizations: to detect errors in the simulated data, to give support to the installation of photovoltaic equipment and to raise public awareness of the use of facades for power production.

With the prevalence of mobile computing systems and location based services, the research interest on spatiotemporal data has significantly increased, as evidenced by the collection of huge amounts of movement data. Consequently, this type of data raises several issues, namely in the research area of geographic information visualization. Despite the existence of several visual analysis techniques for the exploration of movement data, it is still unclear how usable and useful these techniques are, how can they be improved, and for which situations are these techniques most suitable. In this paper, we present current open challenges on the visual analysis of movement data, and the Ph.D work in progress aiming to address these problems. Our work will explore several factors that may affect the users' performance, and, based on those factors we will propose a taxonomy and an evaluation framework covering different tasks and techniques.

The evolution of mobile devices, especially the integration of sensors, is fostering the development of context aware visualization applications. The adaptation to usage contexts is crucial to overcome the diverse limitations that exist in a mobile device, namely the screen size. However, most of the currently develop applications focus on specific contexts. In this paper we propose an adaptive visualization framework that has the goal of enabling the development of applications that use a diverse set of contexts and adaptation methods.

In this paper we present an interactive point of interest query interface, based on the location and orientation of the user. This interface gives clues about the position, relative to the user, of all the points of interest in the vicinity. The interaction provided can also be used to complement the presentation of points of interest on a map, helping identify the association between the icons drawn on a map, and the corresponding real world objects in the neighbourhood of the user.

The increasing popularity of mobile devices has fostered the development of visualization applications for these devices. However, the reduced screen size and different interaction devices, which people are not familiarized, present some challenges to visualization in a mobile environment. This paper describes how, as a proof of concept, a combination of two different techniques can reduce the over cluttering of icons on a mobile device screen. An evaluation of these techniques is also presented.

We are developing a prototype for the visualization of geo-referenced information. The data is organized in several topics. The user interactively selects the geographical region and the topics he/she is interest on. The main features of this prototype are: filtering mechanisms to control the amount of data displayed; representations with different levels of detail selected according to the scale of representation. In order to include semantic criteria to reduce the amount of data to display, we use a degree of interest function.