VISUALIZATION OF SPATIO-TEMPORAL PATTERNS IN PUBLIC TRANSPORT DATA

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1 VISUALIZATION OF SPATIO-TEMPORAL PATTERNS IN PUBLIC TRANSPORT DATA Yedendra babu Shrinivasan, Menno-Jan Kraak International Institute for Geo-Information Science and Earth Observation, The Netherlands ABSTRACT In this paper, we discuss geovisualization techniques to explore spatio-temporal patterns formed by people traveling through public transport system (PTS). The Spatio-temporal reasoning by PTS operators/policy makers to extract patterns from public transport system data is studied. The resulting questions are related to basic visual tasks like locate, identify, associate and compare. These visual tasks are incorporated in a set of visual tools. A visual environment is developed based on time geography s space-time cube and multiple dynamically linked views based on above visual tools that offer alternative perspectives on the data and patterns extracted. A preliminary usability test for the visual environment is conducted with the PTS experts and its results are presented. 1. INTRODUCTION Visualization application for spatio-temporal processes can be categorized into process tracking, post-processing and process steering [1]. Here, we discuss the visualization application for post-processing a spatio-temporal process. Postprocessing involves exploration of archived or measured spatio-temporal processes. Visual exploration can reveal spatiotemporal patterns and help users gain insight into the process. This paper discusses these techniques for exploring public transport system. The process of getting insight into spatio-temporal data through visualization techniques is complex. It should bring out spatio-temporal patterns associated with the data, effectively. Hence, visualization of spatio-temporal data requires different treatment compared to geospatial data visualization that focuses on development of concrete visual representations. Langran [2] suggests few temporal representations in GIS such as time sequences (e.g. multiple editions or time series), change data (e.g. text, graphic, or digital amendments to base representation), static maps with thematic symbols of a temporal theme (e.g. symbols depicting dates, rates, paths, or order of occurrence), and animations. This basic treatment of visualization of temporal component of geospatial data is further extended using visual variables in temporal maps [3]. Temporal maps are defined as a representation or abstraction of changes in geographical reality. Temporal maps can be single static maps, strip maps and animated maps. For representing temporal maps, Bertin s visual variables are not sufficient and appropriate treatment for movement is necessary to bring out changes over time effectively [4]. DiBiase [5] proposed three dynamic variables for temporal mapping: duration, order and rate of change. Further MacEachren added three dynamic variables to it: display date, frequency and synchronization. However, we need to represent the temporal components in geospatial data not only to see the changes but also to see the patterns and reason why such a change has happened. This will improve insight into the process. For instance, visualization of hourly traffic volume data on a road shows the variation of usage of road. If the visual environment helps to relate concurrent events, it may provide clues to why such a variation in the usage of road has occurred. This contextual visualization will improve insight into the spatiotemporal process. This paper discusses the method of creating such visual environment that allows user to perform spatiotemporal reasoning with it and see patterns in the data. 2. TIME GEOGRAPHY APPROACH In late 1960 s, the Space-time cube (STC) was conceptualized by Hägerstrand to represent the movement of people in space and time. This representation was very useful for human geographers to understand the behavior of the people. One of the intentions behind time geography is the desire to develop a contextual approach. Pred [6] brings out this intention through his analysis as what kind of situation is an included object or individual or unit of observation to be found and what connections exist between the object s or individual s characteristics and it s behavior in different situations or context which centers on structure and process. Recently, attempts are made to utilize concepts uncovered by STC to visualize human activity and flow patterns [7, 8, 9], personal diary data [10]. With the advent of computing technology and software

2 engineering, 3D rendering of STC is possible. The STC helps to study behavior of the moving objects in space and time. Hence, it provides a behavior view for the spatiotemporal process. STC is a three-dimensional environment, where X-Y plane represents geographic space and Z-axis represents progression in time. This helps to represent the object s location and attribute changes over time. It is also possible to represent concurrent events in the space-time cube that occur during the lifespan of the object. It can uncover local connectedness of real world phenomena as well as the connections between spatially separate configurations of locally connected phenomena [6]. 3. SPATIO-TEMPORAL PATTERN VISUALIZATION In a visual environment, a user has to do spatio-temporal reasoning to reveal spatiotemporal patterns within the data. Spatiotemporal patterns in the data can provide information about spatio-temporal connectedness and repetition of a process and concurrency to other processes. The queries such as what, where, and when form basic spatio-temporal reasoning components. In traditional GIS view, reasoning can be performed based on location (where) and attributes (what) of the objects. However, a visual environment created to reveal spatio-temporal pattern must support all basic reasoning components. Spatio-temporal reasoning is done by executing visual tasks in a visual environment. These visual tasks are the action protocols in the form of visual functions (overview, zoom, filter, relate, details on demand) and executed based on the concept of dynamic queries and linked views. Knapp [11] identifies visual tasks related to geodata exploration as locate, identify, associate and compare. The function of a visual task must be redefined for each phenomenon, to make it context specific. Use of alternative representations enhances spatio-temporal pattern visualization. These alternate representations provide different perspectives on the patterns revealed in each representation. This helps to verify similarities and dissimilarities expressed in those patterns for the same data. Alternative representations can be created based on the concepts of multiple views and multi-form visualization. Multiform visualization involves use of different visual metaphor for representing data, altering geometry (projection, simplification) or appearance of the visual objects [12]. Multiple views provide more than one view of data created through either different mapping operations or different filtering operations [12]. Linking these multiple views, enable users to rapidly explore complex information, dynamically mix and correlate them visually [13]. Some of the coordination techniques used to link multiple views is brushing, drill down, synchronized scrolling or navigation. 4. PUBLIC TRANSPORT SYSTEM DATA A public transport system (PTS) offers a choice of movement to people. The operation of the PTS depends on the demand created by people. In turn, the PTS also affects the activity system of those people. The operators and policy makers conduct surveys to measure this demand, in terms of patronage (number of people traveling in the PTS). This demand depends on the activity system, fare structure, network operation layout, accessibility, service type and the willingness of people to use PTS. As a result, number of people traveling through the PTS network varies at different place and time. These surveys are conducted to understand the status of PTS to create new plans or policies for the PTS. For effective planning and management of the PTS, insight into the PTS data is required. 4.1 Data description Public transport data used for this research is obtained from Connexxion bus transportation system. Connexxion is one of the major bus service operators in the Netherlands. The data is about number of passengers traveling in their network collected through a survey. This survey is done every year during November for a period of one week or 10 days. This survey is conducted to measure usage of the PTS network and make decision about bus line operations. 4.2 Characteristics of the transport data Ontological characteristics: A bus line has fixed origin and destination. A transit of a bus from origin to destination is called a trip. Each bus line has one or more trips. Each trip has a set of bus stops. Each trip does not necessarily operate over the entire bus line and does not have same set of bus stops. For measuring performance on the bus lines, observation, i.e., number of people traveling in the bus, is made at certain bus stops. The bus stop, where the observation is made, is referred

3 as observation point. Figure 1(a) shows the ontological characteristics. Spatial characteristics: Observations are made at observation points that represent a region covered by the bus line as shown in figure 1(b). Hence, each observation has a valid path. Observation points are ordered for a bus line. This means always a transit passes through the observation points in a pre-fixed order. This gives rise to three potential representations for a bus line such as geographic map, schematic map and linear form shown in Figure 2. Temporal characteristics: A bus line has a set of trips scheduled for a week. These set of trips are repeated throughout the year until a new set of trips are scheduled. In this construct, the observation is taken for one or two week period continuously and aggregated as working days, Saturday and Sunday. Figure 1. Characteristics of PTS data Figure 2. Representation of bus lines 5. VISUAL TASKS The spatio-temporal reasoning required by the PTS operators is studied through discussion with PTS operators and researchers. Some of the resulting questions are: How often is the bus over-crowed? Where and when did such over-crowding happen? What is the repetition pattern in overcrowding for a bus line? What happens in activity system, in the event of over-crowding and under-utilization and is there any relationship between activity system and public transportation system? What is the difference in the trend for a bus line during different periods like working days, Saturday, Sunday? How does the trend in patronage vary among bus lines? Based on these spatio-temporal reasoning, functions of the visual tasks such as locate, identify, associate and compare are defined as below. The locate task helps to find out when and where certain patronage has occurred. The identify task helps to provide information behind the visual representation based on information in database. The associate task helps to provide information about activity system and other parameters like vehicle type, reliability measures to relate visually, the patterns of patronages. This provides clues to what has happen in concurrent events. The compare task helps to compare performance of bus line at different period and to compare performance between bus lines. 6. REPRESENTATION OF PUBLIC TRANSPORT DATA A bus trip is represented as space-time paths, as the bus transits from origin to destination in both space and time. When trips for bus lines are plotted in the space-time cube, it presents both spatial and temporal characteristics of the PTS data. Activity centers are represented as stations in STC. For each hour block, activity centers are given certain weight on scale 1-10, depending on probable contribution to the PTS patronage. This helps to associate the activity system to the measured patronage visually. The STC is shown in figure 3, with trips as space-time paths and activity centre as station. Attribute information about a bus trip such as vehicle type, patronage, etc, can be represented using graphic variables in space-time cube similar to geodata visualization. However, there is a limitation in the use of graphic variables due to the complexities in the 3D perception. Hence, the spatio-temporal patterns in patronage revealed in the space-time cube based on the graphic variables such as color, size is not clearly understood. Nevertheless, space-time cube clearly represents when and where objects are present. Creating alternative representations where the variation in attribute information is clearly visible could solve this problem. In addition, these alternative representations could be linked to the space-time cube, to accomplish the required spatio-temporal reasoning.

4 7. ALTERNATIVE REPRESENTATIONS Patronage is measured in space and time in the case of PTS data. It represents what component of the data. We are interested in visualizing the spatio-temporal patterns in the patronage. Patronage is a ratio measure and following views are created based on this scale for attribute description Variation in what The frequency of occurrence of the patronage at observation points during a trip shows the variation in patronage. A histogram [14] provides a graphic summary of the distribution of uni-variate datasets. Apart from showing the distribution, a histogram [15] also helps to identify outliers (exceptional cases in the distribution, if any). Hence, the histogram is used to represent the frequency of occurrence of the patronage. This provides information about how often a bus is over-crowded or under-utilized at observation points. However, when and where such over-crowding or under-utilizing has happened, is not shown. Hence, it is not possible to accomplish the locate task through histogram. Linking the space-time cube with the histogram will help in making spatio-temporal context. When certain patronage is selected in histogram, corresponding space-time paths should be highlighted in space-time cube. This locate task is shown in figure 4. The associate task to the selected patronage could be accomplished by displaying activity centers in space-time cube. The identify task in histogram should provide information about the frequency of a selected patronage value. The compare task could not be accomplished within a single histogram representation. However, different histograms could be created for distinct constraints (on object, space and time) and compared visually. Table 1 lists the visual functions incorporated in the Histogram. Figure 3. Space-time cube Figure 4. Linked views Histogram and Space-time cube Table 1: Functions in histogram Function accomplished through dynamic query Purpose Identify Frequency To show the frequency of the selected patronage 1 Drill-down to space-time cube To make spatio-temporal context to the patronage shown in the histogram 2 Locate by selection 7.2. Variation in what + where The yellow region represents the selected patronage and highlights the corresponding space-time path in the space-time cube Reference in figure 4 A trip can be represented as a set of observation points. Visualizing the variation in patronage (what) along with observation points (where) will show trends in PTS usage within trips of a bus line. Sets of observation points can be treated as multivariate of spatial type for a trip. A parallel coordinate plot is often used to represent the multi-variate objects. Inselberg [16] 3

5 proposed the parallel coordinate plot that could represent the multi-variate objects through connected points on axes drawn parallel on a plane. Parallel axes could be constructed at observation points for a bus line in linear form (see figure 2). The axes could represent patronage at each observation points. A trip could be represented by connecting patronage on each observation point axis. Thus, variation within a trip can be represented along with an opportunity to compare variation in other trips. Parallel Coordinate Plot (PCP) representing variation in patronage within trips is shown in figure 5. Figure 5. Parallel coordinate Plot Figure 6. Circle view Here, observation points are represented in linear form. The PCP is linked to a map to locate the geographic location of observation points and trips. It is also linked to space-time cube to locate the space-time path of a trip and to associate the trip with the activity system. The identify task provides information about the trip: object (bus line no, trip no, vehicle type), time (year, weekday, hour block), space (set of observation point names with patronage). The PCP supports association of attribute information such as vehicle type, reliability measures with the trips. In figure 5, a dynamic query interface - vehicle type is shown to associate vehicle type with trips. When a vehicle type is selected, indication to maximum seating capacity (yellow line) and total capacity (magenta line) of the vehicle are plotted. The compare task helps to compare trips of bus line grouped in time like between line1a-2002 and line1a It also helps to compare between different weekdays for the trips of the bus line. Table 2 lists dynamic queries in parallel coordinate plot to execute these visual tasks. Table 2: Functions in parallel coordinate plot Function accomplished Reference Purpose through dynamic query in figure 5 Drill down to space-time to make spatio-temporal context for the aggregated patronage over hour cube block 1 Drill down to space box to make spatial context to the observation points in schematic space 2 Layer management To select the bus line of certain year for identification and visibility purpose 3 Switch for trigger behavior To enable or disable the trigger behavior 4 Switch for week day To switch display of trip belonging to working day, Saturday and Sunday 5 Switch for Vehicle type To switch display of trip belonging to certain vehicle type to accomplish association between vehicle capacity and patronage Variation in what + when Visualizing the variation in patronage (what) with aggregation over hour block per day (when) for a bus line will show trends in PTS bus line per day. In addition, this view helps to compare the trend in PTS between bus lines at different times. Keim [17] proposed the Circle view, which can compare continuous data changing over time, identify patterns, exceptions and similarities in the data. The Circle view is a combination of hierarchical visualization techniques, such as tree maps, and

6 circular layout techniques such as Pie Charts and Circle Segments. A circle is divided into number of sectors corresponding to the number of attributes. Then each sector is divided into number of tracks corresponding to the time interval. In addition, each segment in a sector is colored corresponding to the value of the attribute at the time, t n. If the patronage is aggregated over hour block, then the variation in the number of trip for different bus lines per hour block can be ignored. However, the number of trips for each hour block for bus line should be shown to avoid the bias while comparing. The circle view representing the change in patronage for a bus lines for different times is shown in figure 6. Figure 7. Three-axis plot Figure 8. Visual query interface A circle view does not present location information about the bus line. It is linked to a map to locate where the bus line is routed in geographic space. The identify task should provide information about the aggregate patronage for the selected circle segment in a sector. A circle view representation does not support any associate task. Table 3 lists dynamic queries in circle view to execute these visual tasks. The main goal of circle view is to accomplish compare task as the interface is defined. To remove bias in the comparison, following dynamic queries are used (shown in figure 6): Hour block at the centre can be changed Assignment of bus line for each sector can be changed Class breaks for classifying the aggregated patronage can be changed Table 3: Functions in circle view Function accomplished Reference Purpose through dynamic query in figure 6 Drill down to space box To make spatial context to the observation points in schematic space 1 Identify aggregated patronage To show aggregated observation for a selected hour block 2 Hour at centre To change the hour at the centre 3 Sector assigning To change assignment of bus line for each sector 4 Patronage classifier To change class breaks for classifying the aggregated patronage Variation in what + when + where We have three variables in this case: observation point (where), hour block (when) and patronage (what). The variables, observation point and hour block are not continuous but are of categorical domain. The spatial order and temporal order of the PTS data could be used to show variation in patronage as the bus travels in space and time. A three dimensional space is used for visualizing these variables. The x-axis and y-axis are discrete and represents the hour block and linear form respectively. The z-axis is continuous and represents the patronage. A point is plotted at (observation point O, hour block T) with aggregated patronage over hour block (T) at observation point (O) as z-value. This representation is termed as threeaxis plot in which two axes represents linear space and time against single valued function like aggregated patronage. Three axis plot is shown in figure-7, with the points connected using a surface. Wilkinson [18] suggests such a surface connecting the data points in 3D space provides a holistic judgment on relations among the variables assigned to the axes. However, a warning message that such a surface should not be interpolated between data points as we do in the case of digital elevation model (DEM, TIN, etc.) should be presented to the users. A surface is created for a bus line for certain weekday for a year like line1a-working day-2002, line1a-saturday Each surface is termed as layer similar to notation

7 of layers in a GIS. Thus, three-axis plot represents variation in patronage aggregated over hour block in schematic space and time. The three-axis plot is linked to a map to locate the observation points in geographic space. The space-time cube is linked to three-axis plot, to highlight the space-time path corresponding to the selected observation point in three-axis plot for an hour block. The identify task provides information about the aggregated patronage: object (bus line no), time (year, weekday, hour block), space (observation point name) and aggregated patronage value. The associate task is similar to that in histogram, which is accomplished via space-time cube. The compare task in the three axis plot helps to compare the bus line grouped in time as layers like between line1a-working day-2002 and line1a-saturday-2002, between line1a-working day and line1a-working day This comparison is accomplished by studying the visual overlap between layers (see figure 7). In addition, to study the variation in each layer, artificial shift along the aggregated patronage axis can be given to a layer to separate it from other layers (see figure 7). Table 4 summaries these visual function in three-axis plot. Table 4: Functions in three-axis plot Function accomplished Reference Purpose through dynamic query in figure 7 Drill down to space-time cube To make spatio-temporal context for the aggregated patronage over 1 hour block Drill down to space box To make spatial context to the observation points in schematic space 2 Layer management To select the bus line of certain year for identification and visibility purpose 3 Artificial shift to layer To give artificial shift to the layer along the aggregated patronage axis and separate it from other layers to study the variation in that layer 4 Draw surface over data points To provide a holistic judgment on the relations among the data points representing aggregated patronage in schematic space and time 5 Switch for trigger behavior To enable or disable the trigger behavior 6 3DNavigation assistance To assist the user in 3D rotation of three-axis 7 Highlighting identified object To provide visual aid for identified objects in three-axis plot 8 Warning message It presents the warning message that such a surface should not be interpolated between data points as we do in the case of digital elevation model (DEM, TIN, etc.) to the users 9 8. ENHANCING SPACE-TIME CUBE FUNCTIONALITY Table 5 lists the dynamic queries available in space-time cube to explore the space-time paths. The following functions have been realized in the space-time cube environment to enhance its usability 1. Moving space along time - the geographic space in space-time cube can be shifted to required point in time axis to make clear spatial context. 2. Multi-form visualization - instead of displaying the space-time path on geographic map, it can also be displayed on schematic map, to reduce the complexity in representing many bus trip lines. 3. Dynamic query for highlighting Selected space- time paths are highlighted with the following options to locate them easily and have better expression of spatio-temporal patterns revealed in STC. Highlighting the selected space-time path with a unique color (yellow) and retaining the default color of other spacetime path. Highlighting the selected space-time path with a unique color (yellow) and reducing the intensity of color (dim) of other space-time path. Highlighting the selected space-time path with a unique color (yellow) and removing other space-time path. 9. VISUALIZATION ENVIRONMENT just A visual environment just is created to implement the visualization techniques discussed in this paper. A visual query interface comprising of object box, time box and space box initializes the visualization process. This visual query interface (see figure 8) is used to apply spatial, temporal and object filters on the PTS data. Next, the user can select any alternative representations such as Histogram, Parallel Coordinate Plot, Circle view and Three-axis plot to visual the patterns in

8 patronage. The space-time cube can be opened from any of these alternative representations. Thus, the space-time cube with Histogram, Parallel Coordinate Plot, Circle view and Three-axis plot form linked views. To perform the spatio-temporal reasoning, users can execute the visual tasks through dynamic queries available in each visual tool and linked views. The interface of this visual environment is improved based on the feedback from focus group consisting of six visualization experts. Table 5: Functions in space-time cube Function accomplished Reference Purpose through dynamic query in figure 4 Shifting base map along the time axis To make better spatial context during locate task 1 Representation method Provides multi-form visualization to change between geographic map and schematic map representations of the bus trips 2 Highlighting method To clearly see the objects selected by locate task 3 Layer Management To select the bus line and the color code those bus line to identify them in the space-time cube representation 4 Associate with activity To display the activity centers in the space-time cube environment to have system visual association between the activity system and the transportation system 5 Reset locate task To reset the selected objects in the space-time cube environment 6 3D Navigation assistance To assist the user in 3D rotation of space-time cube 7 Highlighting identified object To provide visual aid for identified objects in space-time cube environment PRELIMINARY USABILITY EVALUATION A think aloud method was used to conduct the preliminary usability testing qualitatively. A problem scenario was given to test persons. Cognitive process of the test persons, while solving that problem is observed through action and verbal protocols along with evaluator s observation. Six test persons participated in the evaluation. Based on this observation and retrospect interaction with test persons, the following inferences were made: Visual tasks defined for exploring PTS data helps to accomplish spatio-temporal reasoning and reveal patterns in the data. Alternative representations developed based on the concept of multiple views and multi-form visualization help in getting insight into the data by providing opportunity to review the opinion (hypothesis) created during visual exploration. Visual representations used in each view were appropriate to extract spatio-temporal patterns Functions provided in space-time cube have enhanced its usability especially base map shifting and multi-form visualization for highlighting and changing representation method for space-time path 11. CONCLUSION In this paper, we discussed the visualization techniques required for post-processing a spatio-temporal process such as public transport system operations. Spatio-temporal reasoning is performed by executing visual tasks in a visual environment to extract relevant spatio-temporal patterns. The visual task associate provides contextual information about concurrent events that helps to reason why certain pattern is revealed. The display of activity centers as station in space-time cube and display of vehicle type through dynamic queries in parallel coordinate plot provides contextual information. This contextual visualization helps to understand the occurrence of over-crowding and under-utilization patterns to certain extent. The use of alternative representations such as histogram (what), parallel coordinate plot (what + where), circle view (what + when) and three-axis plot (what + where + when) has enhanced the spatio-temporal pattern visualization by offering different perspective to the PTS data. Three-axis plot brings out spatio-temporal patterns clearly and helps to compare them between different objects (bus lines). The parallel coordinate plot brings out the spatial patterns and circle view brings out the temporal patterns in the data. These alternative representations are linked to space-time cube and map. These linked views help to accomplish the locate task to reason when and when certain patronage has occurred. Finally, we presented a visual environment just - which incorporated the visual tools based on the concept of alternative representations, linked views and dynamic query. The results of the qualitative usability evaluation, based on think aloud method, have proved the

9 above visualization techniques help to explore the spatio-temporal data and get insight into them. 11. REFERENCES [1] A. M. MacEachren, How Maps Work -Representation, Visualization and Design, ch. GVIS: Relationships in Space and Time, pp The Guilford Press, [2] G. Langran, Time in Geographic Information Systems. Technical Issues in Geographic Information Systems, Taylor & Francis Inc., [3] M.-J. Kraak and A. M. MacEachren, Visualization of the temporal component of spatial data, in Proceedings of the 6th international symposium on spatial data handling: advances in GIS research (T. Waugh and R. Healey, eds.), pp , University of Edinburgh, Scotland, Taylor & Francis, September [4] A. Kousoulakou and M.-J. Kraak, Spatio-temporal maps and cartographic communication, Cartographic Journal, vol. 29, no. 2, pp , [5] D. DiBiase, A. M. MacEachren, J. Krygier, and C. Reeves, Animation and the role of map design in scientific visualization, Cartography and geographic Information Systems, vol. 19, no. 4, pp , [6] A. Pred, The choreography of existence: Comments on Hägerstrand s time geography and its usefulness, Economic Geography, vol. 53, pp , [7] P. Forer, H. F. Chen, and J. F. Zhao, Building, unpacking and visualizing human flows with gis, in Proceeding of Geographical Information Systems Research - UK 2004, [8] P. Forer and J. F. Zhao, The extraction and visualisation of flows from movement, in Proceedings of the GeoCart 2003 Conference, New Zealand Cartographic Society, Auckland, [9] M.-P. Kwan and J. Lee, Spatially Integrated Social Science, ch. Geovisualization of Human Activity Patterns Using 3D GIS: A Time-Geographic Approach, pp Oxford University Press, [10] M.-J. Kraak, The space-time cube revisited from a geovisualization perspective, in 21st International Cartographic Conference, ICA Durban, pp , [11] L. Knapp, Cognitive aspects of human-computer interaction for Geographic Information Systems, ch. A Task analysis approach of geographic data, pp Kluver, Netherlands, NATO ASI 83, [12] J. C. Roberts, Multiple-view and multiform visualization, in Visual Data Exploration and Analysis VII, Proceedings of SPIE (R. Erbacher, A. Pang, C. Wittenbrink, and J. Roberts, eds.), vol. 3960, pp , IS&T and SPIE, January [13] C. North and B. Shneiderman, Snap-together visualization: A user interface for coordinating visualizations via relational schemata, in Advanced Visual Interfaces, pp , ACM Press, [14] Chambers, J., Cleveland, W., Kleiner, B., and Tukey, P. (1983). Graphical Methods for Data Analysis. Wadsworth. [15] Nist/sematech e-handbook of statistical methods. [16] Inselberg, A. (1984). The plane with parallel coordinates. The Visual Computer, 1: [17] Keim, D. A., Schneidewind, J., and Sips, M. (2004). Circleview: a new approach for visualizing time-related multidimensional datasets. In AVI 04: Proceedings of the working conference on Advanced visual interfaces, pages ACM Press. [18] Wilkinson, L. (1999). The Grammar of Graphics, chapter Statistics, page 176. Statistics and Computing. Springer- Verlag New York, Inc. VISUALIZATION OF SPATIO-TEMPORAL PATTERNS IN PUBLIC

10 TRANSPORT DATA Kraak, M-J. ITC, Department of Geo-Information Processing, Hengelosestraat 99, 7514 AE Enschede, The Netherlands. P O Box 6, 7500 AA Enschede, The Netherlands. Tel: Fax: kraak@itc.nl. Website: Short CV Menno-Jan Kraak: Career path: 1981 Graduated in Cartography from Faculty of Geographical Sciences, Utrecht University 1982 Army service 1983 Started to work at Faculty of Geodesy, Delft University of Technology as (senior) lecturer in Cartography 1988 PhD in Cartography in at Delft University of Technology 1996 Started at ITC as professor in Geovisualization 1998 additional appointment as professor in new visualization techniques at Department of Geographical Sciences, Utrecht University Current positions: Professor in Geovisualization at ITC & University of Utrecht Head of ITC.s Geoinformation Processing Department Past President of the Netherlands Cartographic Society President of the Netherlands Geoinformation Society Member of editorial boards of several international Cartographic Journals Co-chair of the ICA Commission on Visualization and Virtual Environments Co-chair of the ISPRS WG II/6 (Spatial analysis and visualization systems) Books: Cartography, visualization of spatial data (with Ormeling) and Web cartography, developments and prospects (editor with Brown) Over 200 other publications

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