EXPLORATION OR COMMUNICATION: DEFINING EFFECTIVE VISUALISATIONS FOR SPATIAL DATA

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1 EXPLORATION OR COMMUNICATION: DEFINING EFFECTIVE VISUALISATIONS FOR SPATIAL DATA Xiaogang Chen 1 Ian Bishop 2 Mingzheng Shi Centre for GIS and Modelling University of Melbourne VIC 3010, Australia 1 x.chen4@pgrad.unimelb.edu.au 2 idbishop@unimelb.edu.au Abstract: Recognised as an effective tool for data exploration and information communication, visualisation has been widely used either for realistic simulation, or abstract presentation of the real world to help understand spatial patterns, relations and changes. However, there has been little development for understanding the appropriate choices and implementations of visualization to maximize its effectiveness. This on-going research examines multiple visualisation approaches, investigates situations of use and surveys different users in the context of exploration and communication towards effective visualisations. Pairs of multiple visualisations in land use/cover changes (LUCC) are compared, within different user groups in a test environment, to identify the effectiveness of visualisation approaches. The preliminary outcomes seem to favour 2D and realistic simulation for understanding LUCC. More surveys will be conducted for different user assessments and further detailed conclusions will emerge as the research develops. 1. INTRODUCTION Visualisation has been widely recognised as an effective and rapidly developing tool for spatial data exploration and information communication. A range of visualisation approaches have recently been used either to (a) visualize the real world as an approximation of reality, or (b) abstract major components from the real world and represent them as required. Many examples have depicted changes over time using exploratory approaches (Dykes, 1997; Kraak, et al., 1997; Fairbairn, et al., 2001; Andrienko, et al., 2003) or representing realism through 3D visualisations, or virtual environments (VEs) (Bishop et al., 1997; Verbree et al., 1999; Jepson et al., 2001; Appleton et al., 2003). These, over the last few years, have been demonstrating the possibilities for visualising the real world more effectively. The extensive use of dynamic and realistic visualisations to best represent the real world and its changes will continue to develop in the coming decades. On the other hand, however, as new tools emerge we need to know how well users can benefit from advanced visualisations and use them appropriately. Many challenges in visualisation research may go beyond what we have assumed. The specific needs of potential users, the situations for effective use, and the appropriate mix of different visualisation approaches for data exploration or information communication are issues to be explored (Slocum et al., 2000). According to Orland et al. (2001), our knowledge of when and how to use various visualisation approaches and how well they work to help understand the nature of the real world lags behind the rapidly changing technology. Daniel et al. (2001) argued that different representations produce different environmental responses, and pushed for additional research to determine the merits and demerits of different visualization options. Bishop (1994) suggested critical tests

2 for the validity of new techniques, so that they can be used properly and their effectiveness for applications maximised. Our on-going research described here examines multiple visualisation approaches, such as 2D, 3D, static, dynamic, abstract and realistic simulations, investigates situations of visualisation use, and surveys different users in the context of data exploration and information communication. Pairs of visualised environments representing in land use/cover changes (LUCC) are compared by different user groups with different levels of cognitive capability in a test environment, to identify the effectiveness of the visualisation approaches. Although the preliminary outcomes favour 2D temporal serial visualisations and realistic simulation for understanding LUCC, multiple visualisation approaches have much potential to enhance comprehension of related themes. More detailed evaluations will be undertaken in wider audience as the research develops. Guidelines towards effective visualisations are anticipated. 2. TAXONOMY OF MULTIPLE VISUALISATIONS 2.1 Abstract vs. realistic visualisations MacEachren et al. (1999) argued that cartographic abstraction is an effective tool for understanding geo-objects, geo-phenomena and spatial relations among them. Realistic VEs, by contrast, enhance the spatial cognitive aspects of human awareness and are recognized as a powerful tool for spatial knowledge acquisition from a real environment, as opposed to the use of abstract maps. According to Darken et al. (1998), VEs are more effective than use of the map alone in spatial knowledge training, but only for users in an intermediate skill level category. However, results indicate that those using maps were significantly more accurate in estimating distances in the target placement task. Daniel and Meitner (2001) suggest further research on abstract vs. realistic approaches to determine what representations are necessary and sufficient to achieve valid perceptual, behavioural or valuation responses for particular environmental conditions and characteristics. Affective processes may require very realistic representations. More abstract representations may be sufficient for responses based on more cognitive processes D vs. 3D visualisations The portrayal of landscapes on a conventional 2D map is a fundamental limitation of traditional cartography and will lead to a loss of information and difficulty of understanding for the public (Verbree et al., 1999). Comparatively, 3D visualisation provides an excellent approach for viewing detailed geographical areas and requires less interpretation to understand landscapes by ordinary users. Researchers at the University of California, Los Angeles have developed an Urban Simulator which links a real-time 3D visual simulation system with traditional 2D GIS and databases to facilitate the modelling, display and evaluation of existing and proposed physical environments. Apart from 3D modelling and simulation, the changes over time, either in rural or urban area, can also be simulated by visualization systems capable of providing high quality photo-realistic simulations (Jepson et al., 2001; Stock et al., 2002). However, 2D visualisations obviously enhance people s understanding of the distribution and patterns of geo-phenomena and their spatial relationships. Consequently, Verbree et al. (1999) combines the 2D, 2.5D and 3D representations to facilitate the targeted tasks in different phases of the plan design process for the modeling and visualization. This demonstrates that different merits of visualization options contribute differently in real tasks. 2.3 Static vs. dynamic visualisations Static cartographic visualisation involves a single view of the real world without user interaction with data and display or without animated elements in the representation. Dynamic visualisation refers to the displays that change

3 continuously, either with or without user control. The static aspects of maps describe the elements, properties and relations between elements. A common impression has been that animated representation is superior to static and creates more effective cognition of spatial knowledge. However, this is not always true. In terms of animated representation, studies of effectiveness of animated versus static maps have produced mixed results (Slocum et al., 2000). According to the study by Tversky et al. (2002), in some of the most carefully controlled cases, the animations conveyed detailed information about the micro-steps. It may be that this sort of information is more easily conveyed in animations than in static diagrams, and that would be sufficient reason for using them. From a cognitive viewpoint, human cognitive models are generated in long memory, and shaped by static maps, while dynamic representation is able to facilitate comprehension and improve interpretation by representing the changing properties of the reality. Dynamics extend the capability of static aspects; however, the fundamental properties are inherited from static representations. Given the state of general cartographic knowledge that is widely based in static paper maps, the systematic use of dynamism in cartography does not necessarily provide a good alternative. Based on the above investigation on commonly used visualisations, multiple visualisation approaches are proposed. We will be using multiple visualisations to represent land use/cover changes (LUCC) and examining their suitability for understanding it by local and non-local experts. As a result, the effectives of multiple visualisations in understanding LUCC will be discussed. 3. VISUALISATIONS OF THE STUDY AREA 3.1 Sangong River Watershed Figure 1. Location of study area (Courtesy of Geping Luo) Since the 1950 s, large-scale land reclamation and cultivation of water resources in Xinjiang Uygur Autonomous Region in the Northwest of China, has tremendously changed the land use and cover (Luo et al., 2003). Adversely, it has caused shrinkage or vanishing of lakes, emasculation or cut-off of rivers, severe desertification or salinisation of soil and other environmental pollution. Sangong River Watershed was selected as our study area, due to its location within this dramatic area of changes. Covering approximately 940 km 2, it extends in the south from the foot of the Tianshan

4 Ranges, to the central oasis irrigation farmland, and further to the northern desert within Junggar Basin (Fig.1). 3.2 LUCC visualisations LUCC includes conversion and modification, the former referring to the complete replacement of one cover by another, and latter referring to more subtle changes without changing overall classification (Lambin et al., 2000). Models of LUCC were created from a spatial database, generated from topographic maps, DEM and aerial photos in 1978/87, TM image in 1998, 2000 and SPOT image in Land-use types of irrigated, shrubbery, grassland, city, town, village, wasteland, saline-alkaline, swamp, desert, etc. were interpreted and 2D land use/cover maps were produced at a scale of 1: (Fig. 2a, 2b, 3a, 3b and 3c). A DEM was created from contours at the original scale of 1: to generate various 3D views of study area. From this data, we have modelled and visualised LUCC of each type. ArcMap and ArcScene were used for abstract 2D, 3D and textured 3D (Fig. 4a, 4b, 4c and 4d) representations while Visual Nature Studio (VNS) was applied as a photo-realistic visualisation tool for scenario analysis. The changes in conversion, particularly in height, density and colours of crops or vegetation species in a sample area, were modelled and visualised (Fig.5 and Fig. 6). N Figure 2a. Land use/cover in 1978 Legend Figure 2b. Land use/cover in 1998 Figure 3a. Conversion from Saline-alkaline to Irrigated land Figure 3b. Conversion from converted Irrigated land to Wasteland Figure 3c. Conversion from Saline-alkaline to Wasteland

5 Figure 4a. Textured 3D Figure 4b. Abstract perspective 3D Figure 4c. Abstract 3D Figure 4d. Abstract 2D Figure 5. A simulation of conversion from saline-alkaline to irrigated land between 1978 (left) and 1987 (right) Figure 6. A simulated view of LUCC

6 4. USER EVALUATION 4.1 Goals We define visualisations as effective if they both sufficiently describe spatial patterns and relationships between land use/cover types at one time, and reveal the changes of these patterns and relationships over time. The effectiveness should also be validated through evaluations by a wide range of users. In order to find out how well and in what way multiple visualisations enable enhanced comprehension of LUCC, we set our goals in user evaluation as follows: Examine effectiveness of different visualisations for understanding LUCC; Identify most effective combination of multiple visualisations for understanding LUCC. 4.2 Questionnaire Consequently, questions were designed in the following four categories. 1) Understanding LUCC using 2D maps with the intention of Identification of spatial patterns of and relationships between land use/covers at one time with sample questions as List five dominant land use/cover types shown on the map and Rank irrigated land, city, desert and grassland from highest to lowest in average elevation Identification of spatial patterns of and relationships between land use/covers over time with a sample question as What land use/covers changed significantly from 1978 to 1998? 2) Understanding LUCC using alternative visualisations for enhanced comprehension Provision of map animation (MA), temporal series map (TSM) and interactive analysis (IA) for LUCC to compare the effectiveness with a sample question, such as Is there any land use/cover conversion from saline-alkaline (1978) to irrigated land (1987), and then from the converted irrigated land (1987) to wasteland (1998) Provision of different visualisations with different level of details, such as abstract vs. textured vs. realistic visualisations in 3D formats with an identical viewpoint. Participants were asked to score these visualisations for identifying land use types. Interaction is available for a sub-set of users to help identify a most appropriate option from these three alternatives. Provision of realistic views to identify landscape changes 3) Understanding drivers of LUCC and consequent effects Participants were asked to rate the relevance of LUCC drivers from the list. Options included climate changes, available water increase, population growth, roads built-up, farmland cultivation, irrigation system improvement and urban expansion, etc. As a follow-up, questions such as What land use types may increase in the near future and What land use types may decrease in the near future were then asked. 4) Rating and combining multiple visualisations Finally, participants were requested to rate visualisation approaches used in the survey and to decide on the most effective combination of multiple visualisations.

7 4.3 Test The main objective of this research is to examine what and how visualisations can assist scientists to answer questions with respect to understanding LUCC and driving forces, and help planners or decision-makers tackle with real issues of environmental management. Accordingly, postgraduate students or research scientists with the background of GIS, geo-visualisation, ecology, geography, landscape planning, environmental studies or the other related were recruited. In the pilot survey, 6 local experts from the Department of Geomatics, the University of Melbourne were surveyed. Sitting in a theatre, they were shown slides with multiple visualisations on a large screen, and 30 questions corresponding to slides were answered by each participant independently. Besides, an in-depth interview was made with two local research scientists from the Xinjiang Institute of Ecology and Geography and a certain interaction with the slide show was provided. Conversation with the local scientists followed. Responses to each question were rated on a 7-level of assurance (LoA) scale to determine not only whether the visualisations allowed users to know the correct answers but also to know the level of confidence which was provided. 4.4 Results and analysis Understanding LUCC using 2D maps Although 2D mapping is less favoured than it used to be, it is still the essential medium for communicating information to a wide audience with rich spatial meaning. 6 respondents made such comments as 2D static map model is fine for understanding land use/covers. It clearly shows land use/cover types, patterns and relationships, and allows more viewing time to extract information for effective understanding (Fig. 2a and 2b). One local expert, however, raised the issues that extra time might be needed to get correct answers using 2D model, which may indicate the inefficiency of this approach in some situations. In order to identify spatial patterns of and relationships between land use/covers over time, both MA and TSM were compared in their effectiveness of visualising changes. The results consistently show that TSM is more effective. Comments on MA include difficult to see, hard to compare, hard to get overall sense of change, difficult to understand and concentrates only on one part of the map. In contrast, TSM enables clear representation of changes, easy to compare and can concentrate on one area. In general, MA received average score of 5.5 (from 1 to 10 scale, 10 is the highest) and TSM 8. It should be noticed that two local experts couldn t reach the consensus towards TSM and MA. One held the same idea as above; the other working on LUCC in this area supported MA as it helps reach a quick judgement. His extensive knowledge in, and mental images of, local LUCC may be mixed with visual understanding providing his judgement with high certainty Understanding LUCC using alternative visualisations It should be noted that the effectiveness of MA increases remarkably when fading in/out effects show the transition through an overlaid view of land use/covers in different years. This was shown in a test to compare the effectiveness of MA, TSM and IA in the identification of land use/cover conversion. It implies that MA may have potential to increase its visual perception and effectiveness. It might, however, be too early to conclude that MA is superior to TSM or vice versa. The animation rate is a key parameter for clear presentation and the situations of use and appropriate symbolisation are also crucial to the effectiveness. One local expert suggested a reasonable overlap of changed area and animated time delay for effective understanding and correct judgement. It is clear that care must be taken to resolve the merits of these two visualisations.

8 Obviously, the provision of IA is very helpful to identify land use/cover conversion. Given knowledge and experience of GIS spatial analysis, the visual representation of IA outcomes is more effective than other visualisations (Fig. 3a, 3b and 3c). Some of comments from respondents include The focus is what you want to know, Explicitly defined and shown, The best one for representation. IA received most positive comments and consistently highest score. However, IA is not pleasing and only suitable for analysis on subtle or very specific changes, according to local experts. It does not so well convey the overall picture of LUCC. A comparison between abstract 2D and 3D visualisations indicated the former seems more favoured for identification of land use/covers; while the latter for visual perception owing to its diverse view angles. 2D map is scored highest, followed by abstract 3D overview, abstract 3D perspective view and textured 3D perspective view (Fig. 4a, 4b, 4c, 4d). Single viewpoint available in a 2D map could be supplemented by 3D visualisation. The combination of abstract 2D plus textured 3D perspective view was most favoured by both local and non-local experts as an aid to understanding. Crops, shrub and grass can be reasonably identified in a realistically simulated visualisation. Each response for identification was over 5 in LoA. Based on temporal series simulation, and realistic animation shown in the interview, respondents can clearly identify the changes, such as land use conversion, vegetation growth and seasonal changes. This implies realistic visualisation is also effective for identifying LUCC at micro-scale level. The linkage between GIS analysis and realistic simulation is very helpful to identify land use/cover conversion and present it in a very impressive format as well (Fig. 5 and Fig. 6). Two local experts were very impressed with realistic visualisation for both its realism and efficient LUCC identification. More land use/cover types can be readily identified when their local knowledge and experience of LUCC study are combined Understanding LUCC drivers and consequent effects Farmland cultivation, community development and urban expansion are identified as major drivers by both non-local and local experts with high LoA. This explanation appears to be that, in the former sections of this questionnaire, most respondents clearly identified an increase in irrigated land and village/towns area. Although regarded as the most common driver, population growth is not obviously portrayed in any visualisations. As a result, LoA for the answer is inconsistent, ranging from LoA 2 to 7 in six entries. It seems that multiple visualisations enable identification of driving forces with different levels of assurance, depending on the details of representations and the knowledge of respondents to associated causes and effects. Problems arose in the survey when experts made a judgement according to their knowledge different from that anticipated, and consequently the intended issues were addressed diversely. One expert rated the importance of population factor high and the other rated it low, as they related it to the changes at different spatial scales and time frames. It is thought that population growth may have more significant impact at a regional or national level than that at local level. Recently, LUCC is driven mostly by technology transition, economic policy or market conditions. Accordingly, visualisations at different scales are desirable to identify different drivers and their importance on LUCC, so that LUCC can be related with various spatial and temporal scales. Knowledge extracted from local practice or constructed from visualisations seems to have a mixed contribution to identifying drivers. Consequently, further examinations are certainly required The selected combination of visualisations

9 The final survey has indicated that realistic simulation of environments is most favoured by both local and non-local experts, and contributes to understanding LUCC effectively together with alternative visual approaches. Investigations have shown the preference for combined visualisations for effective comprehension of LUCC. Five non-local respondents favoured the combination of 2D model, 3D views and realistic animation, and one of them suggested an efficient combination of realistic animation and TSM. Similar responses were given by two local experts. 5. CONCLUSION It is concluded that the on-going research supports combination of different visualisation approaches, which effectively increases visual communication of LUCC to different audiences. Maximisation of representational effectiveness requires knowledge based on detailed analysis and comparison of each approach in certain situations. Effective visualisation for understanding LUCC can be attained based on our survey technique and interviews with both local and non-local scientists. The research shows that 2D abstract visualisation is the fundamental visualisation approach to communicate land use/cover to different audiences. As temporal series maps are introduced, changes in land use/cover are clearly conveyed. Map animation, in 2D domain, may contribute to comprehensive understanding; however, this is case-dependant and a reasonable time delay and faded overlap for each view is necessary. Different situations of use, tasks and user groups, etc. may define the boundaries of effectiveness. Either temporal series map or map animation must be applied under scrutiny for fitness in any case. Interactive analysis obviously quantifies the results of LUCC most effectively and is favoured by most participants in a 2D visualisation domain, but requires a single user interactive environment. It is not an ideal visualisation approach. When extended into 3 dimensional views, 3D visualisation triggers cognitive response by broadening view angles to enhance the understanding of land use/cover patterns and relations more effectively. 2D overview and 3D perspective views of spatial objects best support visual cognitive comprehension. Animation used in virtual environments has increased the visual effects vividly and best represents the world as similar as possible to reality. 2D temporal series model, 3D perspective views and animated virtual environment are favoured for effective and efficient combinations of visualisations to enhance LUCC comprehension. It is known that different visualisations produce different effects among local or non-local experts. To reach maximum effectiveness, additional work is needed to examine each visualisation approach for non-experts. Visualisations have much potential for decision support and public participation (Chen, et al., 2002; Appleton, et al. 2005). Therefore, a wide range of surveys for decision makers and stakeholders appears warranted and likely to generate significant findings. REFERENCES 1. Andrienko, N., Andrienko, G. and Gatalsky, P., Exploratory Spatio-Temporal Visualization: an Analytical Review. Journal of Visual Languages and Computing, special issue on Visual Data Mining. 14 (6), Appleton, K. and Lovett, A., GIS-based visualisation of rural landscapes: defining sufficient realism for environmental decision-making. Landscape and Urban Planning, 65, Appleton, K. and Lovett, A., GIS-based visualisation of development proposals: reactions from planning and related professionals. Computers, Environment and Urban Systems. 29, Bishop, I.D The role of visual realism in communicating and understanding spatial change and process. In: Hearnshaw, H.M., Unwin, D.J. (Eds.), Visualization in Geographical Information Systems. Wiley, Chichester, Bishop, I. D. and Karadaglis, C., Linking modelling and visualization for natural resources management: Environment and Planning B: Planning and Design, v. 24, p

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