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Through Space and Time: Spatio-Temporal Visualization of MOBA Matches
Abstract
With data about in-game behavior becoming more easily accessible, data-driven tools and websites that allow players to review their performance have emerged. Among the many different visualizations used as part of these systems, spatio-temporal visualizations which do not rely on animations have received little attention. In this paper, we explore if the established space-time cube (STC) visualization is a suitable means for simultaneously conveying information about space and time to players. Towards this end, we have created a STC visualization for reviewing matches, focusing on Heroes of the Storm as a use case, and conducted a study among 30 Multiplayer Online Battle Arena (MOBA) players to establish how successfully various tasks can be performed and how this kind of 3D representation is received. Our results indicate that such a visualization, despite its complexity, can be usefully applied for match analysis if the design and interaction possibilities are well chosen.KeywordsGameplay visualizationSpace-time cubeReplay analysis
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With the rise of competitive online gaming and esports, players’ ability to review, reflect upon, and improve their in-game performance
has become important. Post-play visualizations are key for such improvements. Despite the increased interest in visualizations of
gameplay, research specifically informing the design of player-centric visualizations is currently limited. As with all visualizations,
their design should, however, be guided by a thorough understanding of the goals to be achieved and which information is important
and why.
This paper reports on a mixed-methods study exploring the information demands posed by players on post-play visualizations and
the goals they pursue with such visualizations. We focused on three genres that enjoy great popularity within the competitive gaming
scene. Our results provide useful guideposts on which data to focus on by offering an overview of the relevance of different in-game
metrics across genres. Lastly, we outline high-level implications for the design of post-play visualizations.
The rapid increase in the availability of player data and the advancement of player modeling technologies have resulted in an abundance of data-driven systems for the domain of esports, both within academia and the industry. However, there is a notable lack of research exploring how players use their data to gain expertise in the context of esports. In this position paper we discuss the current state of the field and argue that there is a need for further research into how players use their data and what they want from data-driven systems. We argue that such knowledge would be invaluable to better design data-driven systems that can aid players in gaining expertise and mastering gameplay.
Competition and collaboration form complex interaction patterns between the agents and objects involved. Only by understanding these interaction patterns, we can reveal the strategies the participating parties applied. In this paper, we study such competition and collaboration behavior for a computer game. Serving as a testbed for artificial intelligence, the multiplayer bomb laying game Pommerman provides a rich source of advanced behavior of computer agents. We propose a visualization approach that shows an overview of multiple games, with a detailed timeline‐based visualization for exploring the specifics of each game. Since an analyst can only fully understand the data when considering the direct and indirect interactions between agents, we suggest various visual encodings of these interactions. Based on feedback from expert users and an application example, we demonstrate that the approach helps identify central competition strategies and provides insights on collaboration.
This study seeks to examine and analyze the spatial and temporal patterns of COVID‐19 outbreaks and identify the spatiotemporal distribution characteristics and changing trends of cases. Hence, local outlier analysis and emerging spatiotemporal hot spot analysis were performed to analyze the spatiotemporal clustering pattern and cold\hot spot trends of COVID‐19 cases based on space‐time cube during the period from January 23, 2020 to February 24, 2020. The main findings are as follows. 1) The outbreak had spread rapidly throughout the country within a short time and the current totality incidence rate has decreased. 2) The spatiotemporal distribution of cases was uneven. In terms of the spatiotemporal clustering pattern, Wuhan and Shiyan city were the center as both cities had high–high clustering pattern with a surrounding unstable multiple type pattern in partial areas of Henan, Anhui, Jiangxi, and Hunan provinces, and Chongqing city. Those regions are continuously in the hot spot on the spatiotemporal tendency. 3) The spatiotemporal analysis technology based on the space‐time cube can analyze comprehensively the spatiotemporal pattern of epidemiological data and produce a visual output of the consequences, which can reflect intuitively the distribution and trend of data in space–time. Therefore, the Chinese government should strengthen the prevention and control efforts in a targeted manner to cope with a highly changeable situation.
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Playtesting of games often relies on a mixed-methods approach to obtain more holistic insights about and, in turn, improve the player experience. However, triangulating the different data sources and visualizing them in an integrated manner such that they contextualize each other still proves challenging. Despite its potential value for gauging player behaviour , this area of research continues to be underexplored. In this paper, we propose a visualization approach that combines commonly tracked movement data with-from a visualization perspective rarely considered-gaze behaviour and emotional responses. We evaluated our approach through a qualitative expert study with five professional game developers. Our results show that both the individual visualization of gaze, emotions, and movement but especially their combination are valuable to understand and form hypotheses about player behaviour. At the same time, our results stress that careful attention needs to be paid to ensure that the visualization remains legible and does not obfuscate information.
A Space-Time Cube enables analysts to clearly observe spatio-temporal features in movement trajectory datasets in geovisualization. However, its general usability is impacted by a lack of depth cues, a reported steep learning curve, and the requirement for efficient 3D navigation. In this work, we investigate a Space-Time Cube in the Immersive Analytics domain. Based on a review of previous work and selecting an appropriate exploration metaphor, we built a prototype environment where the cube is coupled to a virtual representation of the analyst's real desk, and zooming and panning in space and time are intuitively controlled using mid-air gestures. We compared our immersive environment to a desktop-based implementation in a user study with 20 participants across 7 tasks of varying difficulty, which targeted different user interface features. To investigate how performance is affected in the presence of clutter, we explored two scenarios with different numbers of trajectories. While the quantitative performance was similar for the majority of tasks, large differences appear when we analyze the patterns of interaction and consider subjective metrics. The immersive version of the Space-Time Cube received higher usability scores, much higher user preference, and was rated to have a lower mental workload, without causing participants discomfort in 25-minute-long VR sessions.
In recent years, the phenomenon of eSports has been a growing trend and consequently, in addition to players, other groups of users, including coaches and analysts, took an interest in online video games and the data extracted from them. Among many types of video games, one of the most widely played is the MOBA (Multiplayer Online Battle Arena) League of Legend (LoL) game. Similary to traditional sports, players and coaches/analysts analyse all game events, such as, players’ movements, to understand how they play to define new strategies and improve their performance. Our main goal is to get a better understanding of which visualizations techniques are more adequate to handle this type of spatio-temporal information data, associated to player performance analysis in video games. To address this goal, we inquired players to identify the analytical questions they need to support for performance analysis and designed the VisuaLeague prototype for the visualization of in-game player trajectories, using animated maps, and events during a LoL match. This paper presents 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. 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.
Analysis of game data is used to study player behavior. For puzzle-based games where solutions are usually defined by their action sequences, player behavior can also be studied by their solution complexity. In this paper, we present a visualization system to help learning expert to understand how actions, timing and the resulting strategy change with regard to the solution complexity. To establish a novel perspective into the patterns not only in action choices but also in behavior complexity, we designed an interactive, customized line chart to track how complexity and performance change at each stage of skill acquisition. Specialized glyph system (Strategy Signature) is implemented to find strategy differences easily with simple visual cues. Contextual information can be explored by switching the view modes to see potential links between complexity and raw attributes. Evaluation with expert users shows that the system effectively reduced their time and effort in finding interesting subgroups and gave them unexplored angles of behavior complexity to contemplate player’s skill growth. In summary, this paper illustrates a visualization approach to enable analysis into the subtleties of behavior complexity in video games.
Visualization techniques can facilitate the understanding and exploration of relationships in usertesting data. For example, data from players' in-game movement can be combined with interview data or questionnaire results. However, the process of amalgamation is not straightforward, because the underlying data often exists in different formats. Another challenge is making these visualizations simple enough to provide a quick overview for producers, but also detailed enough to be usable and practical for gameplay programmers. Although various visualization techniques have already been introduced in this domain, most of these techniques focus on displaying large amounts of quantitative telemetry data without integrating qualitative or contextual data on player experience. Moreover, most of the current visualizations are static representations of usertesting data, so they cannot dynamically adjust to users' (e.g. producers, programmers) needs. Hence, there is a need for an interactive visualization tool that can adjust data representation based on the nature and detail level of data required from different members of a development team. This paper reports our current development efforts on a tool that assists data collection and provides a dynamic and interactive representation of usertesting data. We also report two initial studies to evaluate the effectiveness of the tool with game developers to guide our future development.
The Space–Time Cube (STC) is a suitable representation to display multiple characteristics of movement data and will especially reveal temporal patterns in the data. By adding annotations to the cube’s paths and stations, the narrative of the display is enhanced. To illustrate the STC’s storytelling capabilities, a historical event, Napoleon’s crossing of the Berezina River during his Russian campaign in 1812 is presented and linked to an event in 2012 when the authors made a similar trip. Also, a set of different visual queries and their results are presented, emphasizing the STC as an alternative addition to a more extended visualization environment.
This paper reports the results of an empirical usability experiment on the performance of the space-time cube in a GeoVisual analytics environment. It was developed to explore movement data based on the requirements of human geographers. The interactive environment consists of multiple coordinated views incorporating three graphical representations. For the experiment, two groups of the user, domain experts and non-domain experts, had to execute several map-use tasks to answers specific question. The data collected during the experiment were analysis resulting in a set of usability metrics related to the effectiveness, efficiency and user satisfaction of developed application. The comparison of both groups showed that domain experts were able to operate the visual analytical environment more effectively and efficiently due to their interest to explore their data. The user feedback derived from the analysis of both experiments was further processed for the improvement of the application.
GPS, RFID, and other technologies have made it increasingly common to track the positions of people and objects over time as they move through two-dimensional spaces. Visualizing such spatio-temporal movement data is challenging because each person or object involves three variables (two spatial variables as a function of the time variable), and simply plotting the data on a 2D geographic map can result in overplotting and occlusion that hides details. This also makes it difficult to understand correlations between space and time. Software such as GeoTime can display such data with a three-dimensional visualization, where the 3rd dimension is used for time. This allows for the disambiguation of spatially overlapping trajectories, and in theory, should make the data clearer. However, previous experimental comparisons of 2D and 3D visualizations have so far found little advantage in 3D visualizations, possibly due to the increased complexity of navigating and understanding a 3D view. We present a new controlled experimental comparison of 2D and 3D visualizations, involving commonly performed tasks that have not been tested before, and find advantages in 3D visualizations for more complex tasks. In particular, we tease out the effects of various basic interactions and find that the 2D view relies significantly on “scrubbing” the timeline, whereas the 3D view relies mainly on 3D camera navigation. Our work helps to improve understanding of 2D and 3D visualizations of spatio-temporal data, particularly with respect to interactivity.
Improving game design is a hard task. Few methods are available in games user research (GUR) to test formally how game designs work for players. In particular, the usefulness of user tests (UTs) for game designers has not been fully studied in the CHI community. We propose a novel GUR method called Biometric Storyboards (BioSt) and present a study demonstrating how a Classic UT and a BioSt UT both help designers create a better gameplay experience. In addition, we show that BioSt can help designers deliver significantly better visuals, more fun, and higher gameplay quality than designing without UTs and that classic UTs do not provide this significant advantage. Our interviews support the idea that BioSt provides more nuanced game design improvement. The design implication is that a game designed with the BioSt method will result in high gameplay quality.
An important aspect of the production of digital games is user-oriented testing. A central problem facing practitioners is however the increasing complexity of user-game interaction in modern games, which places challenges on the evaluation of interaction using traditional user-oriented approaches. Gameplay metrics are instrumentation data which detail user behavior within the virtual environment of digital games, forming accurate and detailed datasets about user behavior that supplement existing user-testing methods such as playtesting and usability testing. In this paper existing work on gameplay metrics is reviewed, and spatial analysis of gameplay metrics introduced as a new approach in the toolbox of user-experience testing and –research. Furthermore, Geographic Information Systems (GIS) are introduced as a tool for performing spatial analysis. A case study is presented with Tomb Raider: Underworld, showcasing the merger of GIS with gameplay metrics analysis and its application to game testing and –design.
Creating an interactive 3D environment can be challenging, in par-ticular because every user (player) will interact wit,h the envi-ronment in their own unique manner. Gaining feedback through playtesting is a common method for evaluating an interactive expe-rience. However, the information gathered from playtesting is of-ten subjective and hard to interpret. Visual representation of logged game player data can provide insight in order to discover patterns and behavior that would otherwise be hidden. In this paper, we explored the use of visual data mining and automated data pro-cessing to process the logged player data and present the results of finding five unique phenomena in our dataset of 3079 players. These phenomena include pirouettes, flusters, jumpers, learning, and emergent behavior. Using the visual data mining process, we can find occurrences of such behavioral patterns in order to under-stand player behavior and improve the interactive experience.
This paper proposes using the rich visual "language" of Hägerstrand's time geography to represent time-space relationships in sport, in particular within the spatial and temporal constraints of a game of rugby. Despite being applied outside of its traditional social context it is argued that time geography's ability to model movements and relationships at the individual level makes it (and its modelling constructs such as prisms and lifelines) a powerful visualisation tool able to provide valuable insights into goal-oriented team sport. The visual tools of time geography are shown in the context of a video information system, SCRUM (Spatio-Chronological Rugby Union Model).
Location-based games not only offer new experiences for the players, but also present new challenges for researchers in terms of analyzing player behaviour. Whilst many ethnographical studies have presented useful qualitative insights into this area, there is the potential to both improve support for these studies and to provide more effective representations of the quantitative data that can often be extracted from the game itself in a manner that enables greater understanding. In this paper, we illustrate how combined spatial and temporal information can be represented using the human geographers' technique of space-time paths to provide 3D visualizations of a player's or players' movement. Our analysis of a particular location-based game shows how a richer understanding of overall game play is obtained and highlights the possibilities for using the technique for a whole range of location-based services to provide a more complete view of complexities of journeys. Further, we discuss how these techniques can be utilized more generally by ethnographers who study the behaviour of mobile actors.
Eye movement recordings produce large quantities of spatiotemporal data, and are more and more frequently used as an aid to gain further insight into human thinking in usability studies in GIScience domain among others. After reviewing some common visualization methods for eye movement data, the limitations of these methods are discussed. This paper proposes an approach that enables the use of the Space-Time-Cube (STC) for representation of eye movement recordings. Via interactive functions in the STC, spatiotemporal patterns in eye movement data could be analyzed. A case study is presented according to proposed solutions for eye movement data analysis. Finally, the advantages and limitations of using the STC to visually analyze eye movement recordings are summarized and discussed.
Space time cube representation is an information visualization technique where spatiotemporal data points are mapped into a cube. Information visualization researchers have previously argued that space time cube representation is beneficial in revealing complex spatiotemporal patterns in a data set to users. The argument is based on the fact that both time and spatial information are displayed simultaneously to users, an effect difficult to achieve in other representations. However, to our knowledge the actual usefulness of space time cube representation in conveying complex spatiotemporal patterns to users has not been empirically validated. To fill this gap, we report on a between-subjects experiment comparing novice users' error rates and response times when answering a set of questions using either space time cube or a baseline 2D representation. For some simple questions, the error rates were lower when using the baseline representation. For complex questions where the participants needed an overall understanding of the spatiotemporal structure of the data set, the space time cube representation resulted in on average twice as fast response times with no difference in error rates compared to the baseline. These results provide an empirical foundation for the hypothesis that space time cube representation benefits users analyzing complex spatiotemporal patterns.
In exploratory data analysis, the choice of tools depends on the data to be analyzed and the analysis tasks, i.e. the questions to be answered. The same applies to design of new analysis tools. In this paper, we consider a particular type of data: data that describe transient events having spatial and temporal references, such as earthquakes, traffic incidents, or observations of rare plants or animals. We focus on the task of detecting spatio-temporal patterns in event occurrences. We demonstrate the insufficiency of the existing techniques and approaches to event exploration and substantiate the need in a new exploratory tool. The technique of space-time cube, which has been earlier proposed for the visualization of movement in geographical space, possesses the required properties. However, it must be implemented so as to allow particular interactive manipulations: changing the viewing perspective, temporal focusing, and dynamic linking with a map display through simultaneous highlighting of corresponding symbols. We describe our implementation of the space-time cube technique and demonstrate by an example how it can be used for detecting spatio-temporal clusters of events.
Currently, there is no formal taxonomy for the activities that users engage in when interacting with and making meaning from spatio-temporal game data visualizations. As data visualization, especially spatio-temporal visualization, becomes more popular for game data analytics, it becomes increasingly crucial that we develop a formal understanding of how users, especially players, interact with and extract meaning from game data using these systems. However, existing taxonomies developed for InfoVis are not directly applicable due to domain differences and a lack of consensus within the literature. This paper presents the beginnings of a taxonomy for user interaction with spatio-temporal data specific to the domain of games, developed from the results of a qualitative user study (n=7) in which experienced players were tasked with using a spatio-temporal visualization system to explore and understand telemetry data from Defense of the Ancients 2 (DotA 2). The taxonomy includes seven activities organized into three categories: Data Interaction, Sense Making, and Validation. We discuss the implications of these activities on design and future research.
Visualizations have become a relevant instrument in games user research and analytics as well as for players to facilitate the analysis of behavioral data, including the evaluation of spatial game metrics. A fundamental task in this respect is to understand how different metrics vary across the game environment and how they relate to each other. In this paper, we evaluate a visualization based on hexagonal binning paired with visual glyphs to represent multivariate information. To study how much information can be encoded in a reasonable manner we created variants with varying complexity and measured correctness and efficiency for different types of tasks through an online study (N = 197). Our results indicate that players appreciated the clear display of multivariate data but that correctness and efficiency were also largely influenced by the visual coding and the task at hand. Based on gained insights, we discuss implications for the design of such and similar gameplay visualizations.
Modeling players' behaviors in games has gained increased momentum in the past few years. This area of research has wide applications, including modeling learners and understanding player strategies, to mention a few. In this paper, we present a new methodology, called Interactive Behavior Analytics (IBA), comprised of two visualization systems, a labeling mechanism, and abstraction algorithms that use Dynamic Time Wrapping and clustering algorithms. The methodology is packaged in a seamless interface to facilitate knowledge discovery from game data. We demonstrate the use of this methodology with data from two multiplayer team-based games: BoomTown, a game developed by Gallup, and DotA 2. The results of this work show the effectiveness of this method in modeling, and developing human-interpretable models of team and individual behavior.
Playtesting is a key component in the game development process aimed at improving the quality of games through the collection of gameplay data and identification of design issues. Visualization techniques are currently being employed to help integrate quantitative and qualitative data. Despite that, two existing challenges are to determine the level of detail to be presented to developers based on their needs and to effectively communicate the collected data so that informed design changes can be reached. In this paper, we first propose an aggregated visualization technique that makes use of clustering, territory tessellation, and trajectory aggregation to simultaneously display mixed playtesting data. Secondly, to assess the usefulness of our technique we evaluate it through interviews with professional game developers and compare it to a non-aggregated visualization. The results of this study also provide an important contribution towards identifying areas of improvement in the portrayal of gameplay data.
Movement ecologists study animals' movement to help understand their behaviours and interactions with each other and the environment. Data from GPS loggers are increasingly important for this. These data need to be processed, segmented and summarised for further visual and statistical analysis, often using predefined parameters. Usually, this process is separate from the subsequent visual and statistical analysis, making it difficult for these results to inform the data processing and to help set appropriate scale and thresholds parameters. This paper explores the use of highly interactive visual analytics techniques to close the gap between processing raw data and exploratory visual analysis. Working closely with animal movement ecologists, we produced requirements to enable data characteristics to be determined, initial research questions to be investigated, and the suitability of data for further analysis to be assessed. We design visual encodings and interactions to meet these requirements and provide software that implements them. We demonstrate these techniques with indicative research questions for a number of bird species, provide software, and discuss wider implications for animal movement ecology.
Qualitative Content Analysis designates a bundle of text analysis procedures integrating qualitative and quantitative steps of analysis, which makes it an approach of mixed methods. This contribution defines it with a background of quantitative content analysis and compares it with other social science text analysis approaches (e.g. Grounded Theory). The basic theoretical and methodological assumptions are elaborated: reference to a communication model, rule orientation of analysis, theoretical background of those content analytical rules, categories in the center of the procedure, necessity of pilot testing of categories and rules, necessity of intra- and inter-coder reliability checks. Then the two main procedures, inductive category formation and deductive category assignment, are described by step models. Finally the procedures are compared with similar techniques (e.g. codebook analysis) and strengths and weaknesses are discussed.
We present a method for evaluating visualizations using both tasks and exploration, and demonstrate this method in a study of spatiotemporal network designs for a visual analytics system. The method is well suited for studying visual analytics applications in which users perform both targeted data searches and analyses of broader patterns. In such applications, an effective visualization design is one that helps users complete tasks accurately and efficiently, and supports hypothesis generation during open-ended exploration. To evaluate both of these aims in a single study, we developed an approach called layered insight- and task-based evaluation (LITE) that interposes several prompts for observations about the data model between sequences of predefined search tasks. We demonstrate the evaluation method in a user study of four network visualizations for spatiotemporal data in a visual analytics application. Results include findings that might have been difficult to obtain in a single experiment using a different methodology. For example, with one dataset we studied, we found that on average participants were faster on search tasks using a force-directed layout than using our other designs; at the same time, participants found this design least helpful in understanding the data. Our contributions include a novel evaluation method that combines well-defined tasks with exploration and observation, an evaluation of network visualization designs for spatiotemporal visual analytics, and guidelines for using this evaluation method.
With the rising interest in multiplayer gaming, gameplay statistics have become an increasingly important aspect of the overall game experience for many players. As a part of this trend, visualizations have gained great popularity among players, in particular heatmaps since they allow them to reenact the course of a game and to develop new strategies. In this paper we report results of a user study conducted with 29 players (i) to investigate how players use heatmaps and two further graphical representations that use clustering algorithms to interpret gameplay and (ii) to assess the three representations in regard to time efficiency, correctness, suitability, and player preference. Our results show that heatmaps were mainly used to detect hot spots while the cluster representations proved useful to compare variables, allowing the players to uncover relationships between them and in turn allowing a deeper insight into the gameplay data.
As increasing volumes of urban data are captured and become available, new opportunities arise for data-driven analysis that can lead to improvements in the lives of citizens through evidence-based decision making and policies. In this paper, we focus on a particularly important urban data set: taxi trips. Taxis are valuable sensors and information associated with taxi trips can provide unprecedented insight into many different aspects of city life, from economic activity and human behavior to mobility patterns. But analyzing these data presents many challenges. The data are complex, containing geographical and temporal components in addition to multiple variables associated with each trip. Consequently, it is hard to specify exploratory queries and to perform comparative analyses (e.g., compare different regions over time). This problem is compounded due to the size of the data-there are on average 500,000 taxi trips each day in NYC. We propose a new model that allows users to visually query taxi trips. Besides standard analytics queries, the model supports origin-destination queries that enable the study of mobility across the city. We show that this model is able to express a wide range of spatio-temporal queries, and it is also flexible in that not only can queries be composed but also different aggregations and visual representations can be applied, allowing users to explore and compare results. We have built a scalable system that implements this model which supports interactive response times; makes use of an adaptive level-of-detail rendering strategy to generate clutter-free visualization for large results; and shows hidden details to the users in a summary through the use of overlay heat maps. We present a series of case studies motivated by traffic engineers and economists that show how our model and system enable domain experts to perform tasks that were previously unattainable for them.
Multiple-view visualisations offer several advantages, such as providing different perspectives on the data. However, there are also associated cognitive costs, including the load on working memory and the effort required for comparison. Furthermore, little perception-based research has been conducted, with few answers to questions such as what tasks multiple views are best used for. Since task performance can be limited by visual attention and working memory, this article investigates how different tasks and their respective loads on attention and working memory affect the usability of two different multiple-view visualisations, namely sequential and simultaneous views. In Study 1, the effects when attention was loaded were studied, where users performed a real-time monitoring task. The results suggest that the divided attention problem was an issue with both view types, but design issues apparent with the simultaneous view were not issues with the sequential view. In Study 2, the effects when working memory was loaded were studied, where users made comparisons of different object sets. The results support the previous work on visual- and memory-based comparisons, i.e. the simultaneous view was more useful than the sequential view. The outcomes from both studies illustrate the importance of understanding how different tasks and their impact on attention and working memory can influence the usability of multiple-view visualisations.
The study of spatiotemporal dynamics is certainly not new, nor is it unique to the field of geography. Nevertheless, addressing complex human and environmental issues such as global warming and human impacts on the environment requires empirical examination from a much broader and integrated perspective than can be accomplished with current techniques.Although Geographic Information Systems (GIS) are intended to provide an integrated and flexible tool for analyzing large volumes of data, they are historically geared toward the representation and analysis of situations frozen in time. Efforts to enhance the temporal capabilities of GIS have served to reveal many problems at a fundamental conceptual level. In order to address this problem, this paper presents a new Triad representational approach that unifies temporal-as well as locational-and object-related aspects and that incorporates concepts from perceptual psychology, artifical intelligence, and other fields. The goal of this research is a drawing-together of a range of concepts and ideas not only to improve our representational and analytical capabilities, but also to provide more common ground among the various fields noted above.The discrete yet interrelated time-, location-, and object-based views incorporated within the Triad framework allow for questions to be asked and answered relative to each of those aspects. Fundamental types of temporal relationships are also defined as part of the temporal view.
As video games are becoming more and more complex and are reaching a broader audience, there is an increasing interest in procedures to analyze player behavior and the impact of design decisions. Game companies traditionally relied on user-testing methods, like playtesting, surveys or videotaping, to obtain player feedback. However, these qualitative methods for data collection are time-consuming and the obtained data is often incomplete or subjective. Therefore, instrumentation became popular in recent years to unobtrusively obtain the detailed data required to thoroughly evaluate player behavior. To make sense of the large amount of data, appropriate tools and visualizations have been developed.
This article reviews literature on visualization-based analysis of game metric data in order to give an overview of the current state of this emerging field of research. We discuss issues related to gameplay analysis, propose a broad categorization of visualization techniques and discuss their characteristics. Furthermore, we point out open problems to promote future research in this area.
Contemporary video games are highly complex systems with many interacting variables. To make sure that a game provides a satisfying experience, a meaningful analysis of gameplay data is crucial, particularly because the quality of a game directly relates to the experience a user gains from playing it. Automatic instrumentation techniques are increasingly used to record data during playtests. However, the evaluation of the data requires strong analytical skills and experience. The visualization of such gameplay data is essentially an information visualization problem, where a large number of variables have to be displayed in a comprehensible way in order to be able to make global judgments. This paper presents a visualization tool to assist the analytical process. It visualizes the game space as a set of nodes which players visit over the course of a game and is also suitable to observe time-dependent information, such as player distribution. Our tool is not tailored to a specific type of genre. To show the flexibility of our approach we use two different kinds of games as case studies.
Current software tools for visualization of spatio-temporal data, on the one hand, utilize the opportunities provided by modern computer technologies, on the other hand, incorporate the legacy from the conventional cartography. We have considered existing visualization-based techniques for exploratory analysis of spatio-temporal data from two perspectives: (1) what types of spatio-temporal data they are applicable to; (2) what exploratory tasks they can potentially support.The technique investigation has been based on an operational typology of spatio-temporal data and analytical tasks we specially devised for this purpose. The result of the study is a structured inventory of existing exploratory techniques related to the types of data and tasks they are appropriate for. This result is potentially helpful for data analysts—users of geovisualization tools: it provides guidelines for selection of proper exploratory techniques depending on the characteristics of data to analyze and the goals of analysis. At the same time the inventory as well as the suggested typology of tasks could be useful for tool designers and developers of various domain-specific geovisualization applications. The designers can, on the one hand, see what task types are insufficiently supported by the existing tools and direct their creative activities towards filling the gaps, on the other hand, use the techniques described as basic elements for building new, more sophisticated ones. The application developers can, on the one hand, use the task and data typology in the analysis of potential user needs, on the other hand, appropriately select and combine existing tools in order to satisfy these needs.
Graphics have been used since ancient times to portray things that are inherently spatiovisual, like maps and building plans. More recently, graphics have been used to portray things that are metaphorically spatiovisual, like graphs and organizational charts. The assumption is that graphics can facilitate comprehension, learning, memory, communication and inference. Assumptions aside, research on static graphics has shown that only carefully designed and appropriate graphics prove to be beneficial for conveying complex systems. Effective graphics conform to the Congruence Principle according to which the content and format of the graphic should correspond to the content and format of the concepts to be conveyed. From this, it follows that animated graphics should be effective in portraying change over time. Yet the research on the efficacy of animated over static graphics is not encouraging. In cases where animated graphics seem superior to static ones, scrutiny reveals lack of equivalence between animated and static graphics in content or procedures; the animated graphics convey more information or involve interactivity. Animations of events may be ineffective because animations violate the second principle of good graphics, the Apprehension Principle, according to which graphics should be accurately perceived and appropriately conceived. Animations are often too complex or too fast to be accurately perceived. Moreover, many continuous events are conceived of as sequences of discrete steps. Judicious use of interactivity may overcome both these disadvantages. Animations may be more effective than comparable static graphics in situations other than conveying complex systems, for example, for real time reorientations in time and space.
Modern positioning and identification technologies enable tracking of almost any type of moving object. A remarkable amount of new trajectory data is thus available for the analysis of various phenomena. In cartography, a typical way to visualise and explore such data is to use a space–time cube, where trajectories are shown as 3D polylines through space and time. With increasingly large movement datasets becoming available, this type of display quickly becomes cluttered and unclear. In this article, we introduce the concept of 3D space–time density of trajectories to solve the problem of cluttering in the space–time cube. The space–time density is a generalisation of standard 2D kernel density around 2D point data into 3D density around 3D polyline data (i.e. trajectories). We present the algorithm for space–time density, test it on simulated data, show some basic visualisations of the resulting density volume and observe particular types of spatio-temporal patterns in the density that are specific to trajectory data. We also present an application to real-time movement data, that is, vessel movement trajectories acquired using the Automatic Identification System (AIS) equipment on ships in the Gulf of Finland. Finally, we consider the wider ramifications to spatial analysis of using this novel type of spatio-temporal visualisation.
Analyzing observations over time and geography is a common task but typically requires multiple, separate tools. The objective of our research has been to develop a method to visualize, and work with, the spatial inter-connectedness of information over time and geography within a single, highly interactive 3-D view. A novel visualization technique for displaying and tracking events, objects and activities within a combined temporal and geospatial display has been developed. This technique has been implemented as a demonstratable prototype called GeoTime in order to determine potential utility. Initial evaluations have been with military users. However, we believe the concept is applicable to a variety of government and business analysis tasks. CR Categories: H.5.2 (User Interfaces): Graphical User Interfaces, I.3.6 (Computer Graphics): Interaction Techniques.
Graph visualizations are typically evaluated by comparing their differences in effectiveness, measured by task performance such as response time and accuracy. Such performance-based measures have proved to be useful in their own right. There are some situations, however, where the performance measures alone may not be sensitive enough to detect differences. This limitation can be seen from the fact that the graph viewer may achieve the same level of performance by devoting different amounts of cognitive effort. In addition, it is not often that individual performance measures are consistently in favor of a particular visualization. This makes design and evaluation difficult in choosing one visualization over another. In an attempt to overcome the above-mentioned limitations, we measure the effectiveness of graph visualizations from a cognitive load perspective. Human memory as an information processing system and recent results from cognitive load research are reviewed first. The construct of cognitive load in the context of graph visualization is proposed and discussed. A model of user task performance, mental effort and cognitive load is proposed thereafter to further reveal the interacting relations between these three concepts. A cognitive load measure called mental effort is introduced and this measure is further combined with traditional performance measures into a single multi-dimensional measure called visualization efficiency. The proposed model and measurements are tested in a user study for validity. Implications of the cognitive load considerations in graph visualization are discussed.
At the end of the sixties Hägerstrand introduced a space-time model which included features such as a Space-Time-Path, and a Space-Time-Prism. His model is often seen as the start of the time-geography studies. Throughout the years his model has been applied and improved to understand our movements through space. Problems studied can be found in different fields of geography, and range from those on an individual movement to whole theories to optimize transportation. From a visualization perspective the Space-Time-Cube was the most prominent element in Hagerstrand’s approach. In its basic appearance these images consist of a cube with on its base a representation of geography (along the x- and y-axis), while the cube’s height represents time (z-axis). A typical Space-Time-Cube could contain the space time-paths of for instance individuals or bus routes. However, when the concept was introduced the options to create the graphics were limited to manual methods and the user could only experience the single view created by the draftsperson. An alternative view on the cube would mean to go through a laborious drawing exercise. Today’s software has options to automatically create the cube and its contents from a database. Data acquisition of space-time paths for both individuals and groups is also made easier using GPS. Today, the user’s viewing environment is, by default, interactive and allows one to view the cube from any direction. In this paper an extended interactive and dynamic visualization environment is proposed, and demonstrated, in which the user has full flexibility to view, manipulate and query the data in a Space-Time-Cube. Included are options to move slider planes along each of the axes
Existing system level taxonomies of visualization tasks are geared more towards the design of particular representations than the facilitation of user analytic activity. We present a set of ten low level analysis tasks that largely capture people's activities while employing information visualization tools for understanding data. To help develop these tasks, we collected nearly 200 sample questions from students about how they would analyze five particular data sets from different domains. The questions, while not being totally comprehensive, illustrated the sheer variety of analytic questions typically posed by users when employing information visualization systems. We hope that the presented set of tasks is useful for information visualization system designers as a kind of common substrate to discuss the relative analytic capabilities of the systems. Further, the tasks may provide a form of checklist for system designers.
We present a system for enhancing observation of user interactions in virtual environments. In particular, we focus on analyzing behavior patterns in the popular team-based first-person perspective game Return to Castle Wolfenstein: Enemy Territory. This game belongs to a genre characterized by two moderate-sized teams (usually 6 to 12 players each) competing over a set of objectives. Our system allows spectators to visualize global features such as large-scale behaviors and team strategies, as opposed to the limited, local view that traditional spectating modes provide. We also add overlay visualizations of semantic information related to the action that might be important to a spectator in order to reduce the information overload that plagues traditional overview visualizations. These overlays can visualize information about abstract concepts such as player distribution over time and areas of intense combat activity, and also highlight important features like player paths, fire coverage, etc. This added information allows spectators to identify important game events more easily and reveals large-scale player behaviors that might otherwise be overlooked.
Game telemetry with DNA tracking on assassin
- J Dankoff
Beyond 50/50: Breaking down the percentage of female gamers by genre
- N Yee
What about people in regional science? In: European Congress of The Regional Science Association Copenhagen
- T Hägerstrand