DYNAMIC VISUALIZATION VARIABLES IN ANIMATION TO SUPPORT MONITORING Connie A. Blok International Institute for Geo-Information Science and Earth Observation (ITC) Geo-Information Processing Department P.O.Box 6, 7500 AA Enschede, the Netherlands
[email protected]; http://www.itc.nl/personal/blok Abstract: A main objective of my research was to develop methods by which dynamic visualization variables can be used to acquire information from imagery in a monitoring context. Vegetation data were used as case study. User task analysis revealed generic monitoring questions and aspects that experts want to analyse. Investigation into ways to support experts in finding answers to their questions with the dynamic variables resulted in aNimVis, an application to visually explore large animated time series. After some adjustment based on a focus group session, detailed evaluation of the use of the prototype by experts has been conducted. The evaluation revealed main problem solving phases, user strategies, use of tools and effects generated by these tools. Results indicate that interactive animations can be useful to support computational approaches to reveal information from imagery. Recommendations for animation design and further research could also be derived.
INTRODUCTION Our world is dynamic, changes occur constantly in all components of the earth’s system. An application that focuses on the dynamics of spatial phenomena is monitoring; it relies to a large extent on time series of remotely sensed data. Abundant satellite data are available, even to such an extent that they can hardly be fully exploited. Current data handling focuses mainly on analytical and statistical approaches. But as a member of the focus group that discussed an early version of a prototype to visually explore animated representations mentioned: ‘An expert should first look at the data and then apply statistical techniques.’ Experts using image processing and GIS software to reveal information from time series usually analyze a few images only; large quantities are difficult to handle. But the real dynamics cannot easily be discovered from a few static images. Large numbers of images can easily be animated to enable quick browsing through the data sets. Animation is dynamic by nature as well: changes are visible in display time; spatio-temporal pattern, relationships and trends may be discovered that are not easily seen from numerical or static maps only (e.g. Koussoulakou, 1990; Openshaw et al., 1994; Peterson, 1995). But animation can be overloading as well, bombarding the user with rapid sequences of changes (Monmonier, 1992). Can they also be effective: is it possible to extract relevant information and to acquire knowledge from animated representations? Morrison et al. (2000) reviewed literature on static and dynamic graphics to support learning. They concluded that static graphics are usually better than animations; if animations are more effective, then it is because they better display the micro steps between bigger changes than static graphics. Can animations be effective for applications in which detailed sequential information about changes is important, like in monitoring of spatial phenomena? Effectiveness obviously depends on more than the ability to show micro steps. One likely contributing factor is user control by interaction (Dorling, 1992), but in the geosciences there are relatively few empirical investigations in interactive animation, and the results are mixed. For example, interactions with a temporal legend, intended to assist students in learning about global weather, were not particularly effective (Harrower et al., 2000). On the other hand, epidemiologists could effectively detect spatio-temporal patterns in interactive animations (MacEachren et al., 1998). A number of interactive animation controls have been provided (e.g. Dykes, 1996, Andrienko & Andrienko, 1999; Peterson, 1999), but we still know little about the way users actually work with them (Slocum et al., 2001). This paper is about incorporating interactive controls in the design of an animation and about animation use, but it focuses on the dynamic visualization variables: the variables of the temporal dimension (display time) of a running animation. Issues related to ‘seeing change’ in an animated representation will be described: potential problems and ways to (partially) overcome them by animation design and provision of user controls. Results of animation use by domain experts in a monitoring task are also described. Emphasis will be on cognitive aspects of the problem-solving behaviour, like animation use strategies, problem-solving phases and answers provided by the experts.
VISUAL EXPLORATION OF CHANGE A domain expert who uses an animation for visual exploration relies on impressions from (external) data representations. In the geosciences these external representations are often maps or images. The creative process to derive meaning from maps and images is centered around identification and comparison of patterns, their spatial and temporal characteristics, relationships and trends. It can be facilitated by controls that enable users to interact with the data and their graphic representations. The expert will try to recognize patterns by matching sensory input to (internal) cognitive representations: encoded spatial information resulting from direct experience with the environment and from secondary sources, like maps, models, written sources. He/she may also try to categorize sensory input to infer patterns that become noticed, which may in turn lead to new or altered cognitive representations. The often cyclic process of seeing patterns and trying to imbue what is seen with meaning characterizes visual exploration and is (for example) described by MacEachren (1995) as iterative ‘seeing that’ and ‘reasoning why’ phases. Reliance on visual impressions to gain information can be powerful. Perception can be used as an additional resource in information processing and the availability of an external representation reduces the memory load. Maps and images provide compact stimulus spaces. Certain visual stimuli are quickly recognized and the spatially structured information facilitates pattern recognition. Quick qualitative impressions can be obtained and in contrast to analytical approaches, no predefined queries are required. A main advantage of animation for visual exploration (in addition to showing the micro steps in changes) is its suitability to show processes: one can actually ‘see’ how patterns shrink or expand, break up, the direction and speed of change, frequencies of events, etc. The difference between abrupt and gradual changes, important for many applications, can be easily observed. Outliers are often important as well; users are attracted to outliers in animated representations (Ogao, 2002). But reliance on visual impressions has some pitfalls as well. It is not completely foolproof (ambiguous figures and visual illusions can be seen) and the qualitative impressions usually need to be checked with computational techniques. Users of an animation may be bombarded with changes. Stimuli fade away quickly and it needs effort to maintain them (Kosslyn & Osherson, 1995); even if one tries, changes may still go unnoticed. Change blindness refers to failure to detect change in the visual field (Rensink et al. 1997). It may happen when the view is interrupted (e.g. during eye movements) or when motion signals, caused by local and temporal variations in the visual field, are too weak or too slow to be noticed. It is also possible that attention is overwhelmed by too many signals. Attention, needed to bring changing stimuli into consciousness, can only be directed to a limited number (4-5) of changing items at any time. Attention is not only drawn by motion signals; the layout and meaning (gist) of a scene and characteristics of the user (such as knowledge, experience, ability, intentions and interests) also influence which parts of the visual field are attented. Finally, the task of the user plays a role. If users are actively engaged in a task, unexpected, non-task related changes may go unnoticed, even if these changes happen in the centre of the visual field. This process is referred to as inattentional blindness (Mack & Rock, 1998; Simons & Chabris, 1999). Hence, although animation seems promising for monitoring, at least to complement more computationally oriented approaches, there are no doubt also limitations. It is perhaps possible to (partly) overcome these limitations by careful animation design, including the provision of controls.
CASE STUDY: VEGETATION MONITORING Vegetation indices like the NDVI (Normalized Difference Vegetation Index) are calculated from the reflectance of the red and near-infrared parts of the electro-magnetic spectrum. These indices are frequently used as a measure of ‘greenness’ to support the monitoring of vegetation dynamics, together with various ancillary data (e.g.: topographic data and DEM’s, information about farming systems and crop calendars, weather data, etc.). NDVI data have been selected as case study in the research described here. Preprocessed SPOT 4 VEGETATION images have been obtained of part of Iran and some parts of Iraq and Kuwait for the period April 1998-May 2002. In data set consisted of 147 synthesis images, containing the maximum NDVI value per pixel over ten-days (3 images per month). Interviews with experts in monitoring and literature study revealed that experts are mainly interested in the following aspects (e.g.: Eastman et al., 1995; Groten & Immerzeel. 1999): recent developments and longer term dynamics, changes in phenomena of interest (location, type and time), anomalies; deviations from normal values may require immediate action, ongoing processes (such as deforestation, degradation, erosion),
possible causes and relationships (for example between vegetation and rainfall), spatio-temporal patterns or trends (such as a gradual loss of biodiversity). In an animation, visual stimuli should trigger domain knowledge in experts to enable reasoning about these aspects. One of the steps towards animation design, therefore, was to investigate characteristics of change that can be visually perceived in an animation. This resulted in a framework of concepts that describe these characteristics (see Blok, 2000 for details). It was assumed that these characteristics could trigger the knowledge needed to answer monitoring questions. Generic monitoring questions were derived from literature study and from analysis of the aspects, important for monitoring that are mentioned above. Most questions are used in a task, given to vegetation experts in the test (see figure 7). Questions about anomalies were not included in the test however, since long term average values were not available for the case study data. These averages are needed for comparison with current values to judge anomalous behaviour.
DYNAMIC VISUALIZATION VARIABLES Two types of representation variables are visible in an animation: the graphic variables – originally distinguished by Bertin in 1967 (Bertin, 1974) and later extended by various other researchers – are visible within the spatial dimensions used to represent geodata. Their appearance may vary in successive maps or images, but in order to make dynamics like speed of movement or frequency of change visible, dynamic visualization variables are also needed. Six of these variables have been distinguished by DiBiase et al. (1992) and MacEachren (1994), but two of them (rate of change and synchronization) are here not considered to be dynamic visualization variables, but effects (see below). Rate of change is influenced by many things: the characteristics of the underlying data, design choices like number of frames per second, and all kinds of user interactions. Two simultaneously running, chronologically ordered animations might reveal synchronization of patterns between (perhaps related) phenomena. Possible time lags require careful selection of the moments to start these animations, in other words: interaction with the variable moment of display. Additional interaction with the individual animations might be needed. For example, some types of vegetation respond more slowly to rainfall than others, so it might be desirable to run the animations at different display speed. Hence, pattern correspondence has to be discovered by exploration, and various interactions may be required. Applying a representation variable seems a more elementary activity. The four variables that have been investigated are defined in figure 1. In these definitions, ‘change’ and ‘state’ are used. A change can be the result of an alteration in the data underlying the representation or be caused by (interactive) alteration in the representation itself. A state is a condition or mode of existence not affected by change in the representation. The variables are not independent of each other. The moments of display that mark the initiation of a change or a new state form the basis for perception of the other variables. Duration – the distance between at least two marked moments of display – and order are primary derived variables. Frequency, being a function of order and duration, can be considered as secondary derived variable. Dynamic visualization variable
Definition
Moment of display Order
Position of a state or a change in the representation in display time. Structured sequence of states or changes in the representation in display time. Order is structured because it is based on a chosen principle or criterion (e.g. chronological or based on particular attribute values). Length in display time of a state or change in the representation. Repetition or number of identical states or changes in the representation per unit of display time.
Duration Frequency
Figure 1. Dynamic visualization variables, visible in a running animation
From a design perspective, the variables can be applied in many ways to represent characteristics of geodata (see also Ormeling, 1996). A rather obvious way is to mimic characteristics of the data in World Time by the variables of display time in a chronological representation (Kraak & MacEachren, 1994). Other options are to use the variables for a sequential representation of different attributes, locations, graphic representations or views on the data. There are many possibilities in an interactive environment, because the variables cannot only be used to depict data, but also to control the animated display. Examples of interactive controls are given in figure 2.
Figure 2. Selected interactions for a chronologically ordered animation and the main dynamic visualization variable involved
Tuning contains interactions that are typically needed to observe synchronization of patterns in two animations: different starting points for each animation can be selected; the animations can be linked to run together, and there is an option to synchronize the display speed. Many interactions listed in figure 2 can be combined (e.g. selection of time and thematic attributes). No attempt has been made to give a complete overview of interactions with an animation. Taking monitoring as application into account, a range of options to control a default chronological representation of geodata has been selected. For example, dynamically linked alternative data representations or interactions that require recalculation of the data are not included. A number of these controls have been applied to animation by other researchers as well (e.g. Moellering, 1976; Slocum et al, 1990; DiBiase et al., 1992; Andrienko et al., 2000; Harrower et al., 2000), but they often implemented it in a different way and/or the controls were not tested in a comparable context. From a user’s perspective, playing an animation and interacting with various controls generates effects. Some authors have attempted to extend Bertin’s approach, suitable for the selection of graphic variables, to the dynamic variables (e.g. MacEachren, 1995; Köbben & Yaman, 1996). The choice of variables is in this approach based on a link between perception properties (or effects) of these variables with measurement levels of the data (figure 3a). But the dynamic visualization variables can basically be applied to data at any measurement level. Therefore, other effects are distinguished for the dynamic visualization variables (see figures 4 and 5) and an attempt is made to link these effects to (monitoring) tasks or questions in this paper (figure 3b). The most characteristic effect of an animation is dynamic behaviour: it is automatically generated (an implicit effect) whenever an animation is playing, together with rate of change (figure 5). But just playing creates many motion signals. Since attention can only be directed to a limited number of changing items, many things will be missed, particularly in complex (e.g. satellite) data. Some of these change blindness problems can perhaps be solved if special effects, caused by controls other than play, can be generated. The special effects may prevent that users become overwhelmed by motion signals, and they support focused attention to subsets of the data. Most of the effects mentioned in figure 5 are special; rate of change has a dual role: it can be caused by use of some other controls than play as well. Originally, swapping (interchanging two screen displays that show different times, locations, thematic attributes or representations) was included in figures 4 and 5 as (special) effect of the options to alternate moments, values and classifications/colour schemes. It has been excluded from the tables in this paper, since user tests revealed that swapping should not be considered as a separate effect. It is either visual isolation or re-expression, as indicated in the tables.
Figure 3. Bertin’s method to select variables for the representation of data (a) and the approach followed in this paper (b)
The approach in figure 3b was based on the assumption that users interact because they want to generate certain effects, and the kind of effects that are desired are influenced by the task at hand. Linking effects of the dynamic visualization variables to (monitoring) tasks/questions was one of the aims of the research. If we know what effects support users in their tasks, methods and controls that generate these effects can be offered to the users. Effect
Definition
Dynamic behaviour
Succession of moments of display, orders, durations and frequencies in an animation. Magnitude of change per unit of display time in an animation. Segregation of one or more selected times, locations and/or thematic attributes from the default animated representation. Alternative graphic representation of (or perspective on) the default animated representation. Correspondence between spatio-temporal patterns of two chronologically ordered representations of geodata in display time, irrespective of the time differences in reality (World Time). Adaptation of the speed at which moments of display, orders, durations and frequencies are represented in an animation. Enhancement to stress selected elements in an animation. Multiple time views on (parts of) the animated representation.
Rate of change Visual isolation Re-expression Synchronization
Pacing Emphasis Review
Figure 4. Effects of interactions with dynamic visualization variables defined
PROTOTYPE DEVELOPMENT: ANIMATED IMAGE VISUALIZATION (aNimVis) An interface was developed to interact with the animation as indicated in figure 2. Main aims were to facilitate identification and comparison of patterns and to limit the burden on perceptual and cognitive processing mechanisms. Figure 6 shows the default appearance of the main window with common media player and display speed controls, base map layers options and a legend that supports thematic attribute selections, several menu’s and a tool bar (for details, see Blok, 2005). In the time bar, a graph with average NDVI-values per image is embedded. It shows seasonal patterns and helps users in all kinds of temporal selections; it also provides context for interpretation. The two display areas in the tuning window are here used to run the same data set. Users are able to start the animations at different selectable moments (e.g. to compare the growing seasons of 1999 and 2001) to explore to what extent patterns are similar, whether there is a time lag, etc. Patterns of different static maps can be mentally integrated, but there is no evidence about two simultaneously running animations. Detailed local processing will not be possible, but maybe a more global processing of broad patterns, if the display areas are kept small. aNimVis is a Microsoft.NET application developed in Visual Studio.NET and C#.
Figure 5. Effects of animation use activities in which the dynamic visualization variables are involved
To improve the prototype and minimize problems in a later evaluation, a focus group session was organized. Five experts evaluated the effectiveness of the system in the context of their domain; another expert commented later. Results led to some adjustments the prototype.
Figure 6. Main window (left) and tuning window (right) of the prototype aNimVis
USER TESTS The prototype mainly served as a vehicle to learn more about cognitive aspects and actual use of the dynamic visualization variables in a problem solving task; software evaluation was not the main aim. The think aloud method is very suitable to gather data on cognitive processes (Ericsson & Simon, 1993; Van Someren et al, 1994). Participants in such an evaluation are requested to execute a problem-solving task while thinking aloud. It is a direct method to tap non-interpreted, non-rationalized thoughts. Since data are collected during task execution, no memory errors occur.
Rich data can be recorded: verbalizations, problem-solving behaviour including all actions on the screen and body language. Main disadvantages are that the data gathered might, to some extent, be selective and not complete. Verbalizing thoughts is not easy for everyone and observation may influence behaviour. Data analysis is time consuming, difficult and it relies on interpretation by the experimenter. But the disadvantages do not outweigh the advantages, and they can partly be avoided by a combination of techniques. The think aloud method (with full audio/video recordings) was used here as main data gathering technique. It was supplemented by post-test interviews to ask questions about task execution and to reveal problems, alternatives and comments. Personal data and opinions were further collected in a post-test questionnaire. Ten experts (six male and four female persons) participated in individual test sessions. The group was rather homogeneous in many aspects: highly educated, with experience in monitoring and in use of time series (only one was less experienced). All had relevant domain knowledge, but they varied in main discipline of educational background. After a brief introduction, a demonstration and a familiarizing phase with aNimVis, the participants executed a task while thinking aloud. The task consisted of five typical monitoring questions; it was assumed that the cognitive load (and thus of complexity) would increase from the first to the last question. Four questions were more specific about parts of the data; the last question enabled a less constrained exploration of the whole animation (figure 7). Interviews and questionnaires were completed immediately after the think aloud phase. Generic monitoring questions
Period to be considered
1. What changes do you consider significant? 2. Are there any significant differences or correspondences between the two periods? 3. What changes do you consider significant? 4. Are there any significant differences or correspondences between the two years? 5. Try to discover whether there are any: - specific processes, also reason about causes; - spatio-temporal patterns (cycles, trends, etc.); - significant spatial or temporal relationships.
March–August 1999 April–August 1999 and 2000
Main cognitive tasks assumed to be involved Identification and comparison Comparison
April 1998-1999 April 1998-2000
Identification and comparison Comparison
April 1998-May 2002 (whole time series)
Identification Identification Comparison
Figure 7. Questions in the task given to participants in the evaluation session
Verbal protocols (almost literally transcribed) and action protocols derived from the video tapes were used to analyze the problem solving behaviours of the participants. Main results are summarized below (for details, see Blok, 2005). The raw verbal protocols were segmented into distinguishable phases in problem-solving behaviours. Most frequently occurring phases were: selection of time, followed by identification and comparison, then comparison. Identification only could hardly be distinguished, also not for the discovery of processes and spatio-temporal patterns in question 5. There are hardly signs that the cognitive load increased during the task, at least not from questions 1 to 4. Both number of phases and time used to provide answers dropped after question 1, but increased again in question 5. Participants also found question 5 more difficult than the others. Further analysis of the problem-solving behaviour within the phases and of the action protocols revealed, amongst others, three main animation use strategies. Some users mainly wanted to reduce the amount of data and then focus on subsets that were considered relevant for further exploration. Other users were mainly playing and/or stepping, but they also frequently interacted in various ways with the representation. The last group played the animation almost continuously, exploring the dynamics without much interaction, because that distracts from ‘the movie’. Common animation tools (e.g. media player and display speed controls, loop, zoom) were used by all participants. The same applies to base map options, temporal selection controls and tuning. The result for tuning, having two animations together, was unexpected: one animation may already overload users and cause change blindness. The participants may, however, have detected changing patterns without really identifying the features, since one can only attend to a limited number of moving objects. It is also possible that attention was limited to changes that attract attention or to features of interest. Whether important changes have been missed is not tested. Apparent movement of vegetation patterns in the time series, showing broad, cyclic west-east moving patterns, may have contributed to the use of tuning: changes on moving objects are easier noticed than on static objects (Rensink et al., 1997). Finally, interactions like speed control, stepping and looping may also have contributed. Protocols and feedback revealed that users were really interested in tuning, but they also wanted its functionality to be extended (e.g. not only selection of dates to start the animations, but also dates to end the simultaneous display). Thematic interactions were hardly used during the task. This was mainly due to lack of knowledge about NDVI-values for specific types of vegetation in the area, but some tools also did not
seem very useful (e.g. duration thematic). There are indications that some decision about tools to use were already made in the familiarizing phase, before the tasks was given to the participants. Before the evaluation sessions, predictions were made about the effects needed to find answers to each monitoring question and about the tools that would be used to generate those effects. The predictions were compared to actual use of tools and effects. Although some deviations occurred in the contribution of individual tools, predictions for the first five effects mentioned in figure 8 were correct for questions 1-4. Question 5 had predictions for each subquestion, but clear distinctions could not be made in the behaviours of the participants. Therefore, effects for question 5 could not be compared and no prediction are included in figure 8. Decrease in use of the special effect rate of change for questions 3 and 4 was not expected. This is mainly due to reduced control of display speed, perhaps because a comfortable speed was found after questions 1 and 2. Limited speed control also affects pacing in question 3, but this effect becomes more important again in question 4 because of stepping in the main and in the tuning window. Review does not seem to be very important for questions 2-5, but this due to the recording of tool use. Only loop on/off was recorded, while some participants switched the loop on early and never switched it off. Figure 9 summarizes effects in relation to tasks. Answers show that all participants were able to extract relevant information from the animation. In eight participants, domain knowledge was clearly triggered. The other cases were less clear: these participants gave very general answers. Predicted usQQQQQActual usQQQQQ5 Dynamic behaviour Rate of change Visual isolation S ynchronizatiol Synchronizatio Re-expression Rate of change Pacing Emphasis Review
Dynamic behaviour Rate of change Visual isolation Re-expression Rate of changh Pacinh Emphasil Reviel
Figure 8. Use of effects; light, middle and dark grey means used by 10-25%, 25-60%, and ≥ 60% of the participants respectively
Effect
Overall importance
Dynamic behaviour / rate of change (implicit) Visual isolation Synchronization Re-expression Rate of change (special) Pacing Emphasis Review
High for all tasks High for all tasks (but low contribution of thematic selection) High for comparisons in time only Medium for all tasks Initially high, the low until the task changes Variable Not important (as currently implemented) High for all tasks
Figure 9. Summary of overall importance of the effects of animation controls
CONCLUDING REMARKS This research was limited to the dynamic visualization variables, but it made clear that useful information for monitoring can be obtained from interactive animated representations of geodata. The prototype is suitable to reveal information from imagery, particularly phenological aspects of vegetation. Best served with additional animation controls are users that apply the strategy to visually isolate relevant parts of the content before further exploration. Additional tools that generate visual isolation are also good with a view to change blindness problems. Effects may indicate in which direction research on further tool development should go; they offer room for a variety of controls. A start has been made to link effects that users want to generate to user tasks; these links can be extended in the future. Recommendations for animation design can further be derived from participant’s feedback on aNimVis. The prototype received overall high usability ratings, but, if turned into an real application, it should be improved and can be extended with functions beyond interactions with the dynamic visualization variables only. All participants would like to use it if it is linked to GIS or image processing software; at least some of its functionality is complementary and could enrich more computationally oriented environments.
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CURRICULUM VITAE Connie Blok is employed at the International Institute for Geo-Information Science and Earth Observation (ITC) in the Netherlands since 1986. Her current position is Assistant Professor in the Geo-Information Processing Department (GIP). So far, her main involvement was in all activities related to education and in research in cartography, geovisualization and geoinformatics. This paper is based on her PhD research, entitled: Dynamic visualization variables in animation to support monitoring of spatial phenomena. This research was successfully defended in January 2005 at Utrecht University, the Netherlands, promotors were Prof. Dr. F.J. Ormeling and Prof. Dr. M.J. Kraak. During the research work, she obtained several travel awards, e.g. to attend NCGIA's Varenius Project meeting on Cognitive Models of Dynamic Phenomena and their Representations, sponsored by the U.S. National Science Foundation, in Pittsburgh (PA), USA (1998). Further details and references to all her publications can be found on the web site mentioned at the beginning of this paper (http://www.itc.nl/personal/blok). Connie Blok has been an editor of the Netherlands Cartographic Journal (Kartografisch Tijdschrift) for more than ten years, of which two year as chair of the editorial board. She is a member of the Dutch society Geo-Informatie Nederland and a corresponding member of the ICA Commission on Visualization and Virtual Environments.
