The Recognition of Temporal Patterns in Pedestrian Behaviour Using Visual Exploration Tools
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1 The Recognition of Temporal Patterns in Pedestrian Behaviour Using Visual Exploration Tools I. Kveladze 1, S. C. van der Spek 2, M. J. Kraak 1 1 University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), 7500 AE Enschede {kveladze; kraak}@itc.nl 2 Delft University of Technology, Faculty of Architecture, 2628 BL Delft s.c.vanderspek@tudelft.nl 1. Introduction The paper describes part of a wider urban planning study to observe and evaluate the use of public space of the historical city center of Delft. In previous studies domain experts have concentrated on the characteristics of the spatial distribution of pedestrian activities studying destinations, frequently used routes, and gaps in pedestrian network. They conducted an extensive analysis of recorded GPS tracks and produced various maps (Van der Spek 2009). However, questions related to the temporal aspects of the patterns still remain partly unanswered. This paper addresses the problem on how to use GPS tracks of pedestrian to understand the temporal movement behavior (shopping/leisure) in different streets, while considering the context (nature/origin) and movement patterns (speed/directions). This is lead to questions like: When are the movement speed high or low in particular streets, and what is the nature of these streets?, and How long did people stay in the most frequently visited shops, and what kind of shops are those? First, some background information, and the methods and techniques used are described. Followed by an elaboration of the case study. For the visual exploration of this data the space-time cube is used, because of its ability to represent time. Figure 1. Density analysis of Wednesday (left) and Thursday (right) of people visiting the city center of Delft, image by S. C. Van der Spek (2009). GISscience 2012 / Columbus / Ohio Paper 127 Page 1 of 6
2 2. Urban Mapping Based on Tracking Technologies GPS tracking technology as an instrument to collect data on movement in public spaces is considered as valuable method by urban planners (Van der Spek 2009). It has been applied for analysis of the actual use of public space in cities like Koblenz, Rouen and Norwich, and Delft (Van der Spek et al. 2009). This last dataset has been extensively studied at the Delft University of Technology (TU delft). Figure 1 gives the result of aggregated data over two days based on the behavior of the tourists/visitors. The similarities and differences of use of the city center by above group were investigated. 3. Time Geography and Space-Time Cube The above visualizations are examples of aggregated movement maps. Alternatively one can use flow map, which allows the visualization of both aggregated and individual movements. However, these maps do not explicitly map time, and are therefore less useful in answering temporal questions. An interesting alternative is the Space-Time cube introduced by Hägerstrand (1970) as an element of his time geography theory. It shows space in its horizontal plane and time along the vertical plane axis. It has been applied in various domains, and during the last decades it became increasingly popular due to the data availability and current software and hardware, that allow interactive visual analysis of the movements. For example, Huisman and Forer (1998) investigated human activities with similar life styles, Kwan (1999) applied it to research on gender differences. Miller (1999) studied accessibility of transport networks, Demšar and Virrantaus (2010) looked vessel movements, and used to investigate movement behavior of visitors in a national park. 4. Use Case Study: Data Collection and Characteristics The data for the use case were collected by the Faculty of Architecture, Delft University of Technology. At two parking facilities located immediately west (Phoenix) and south (Zuidpoort) of city center participants were issued a GPS unit during their trip on foot into the city center, where each participant has made one trip for several hours. Additional personal information such as gender, age, occupation, marital status, purpose of visit, etc. was obtained by interviews. As a result of the collection process, that covered four days between 18 th and 21 st of November 2009, from 10:00 to 17:00, the data set contains information on the movement behavior of 300 pedestrians. The highly detailed data with accuracy of 5 seconds was processed and filtered for use in the STC (see Figure 2). Page 2 of 6 Paper 127 GISscience 2012 / Columbus / Ohio
3 A B C Figure 2. Data before (A and B) and after filtering (C and D). D 5. Visual Exploration The exploration of the use case study in STC environment follows a visualization strategy, based on Shneiderman s (1996) mantra: overview, zoom/filter and details on demand. The utilization of the strategy depends on the data that have been represented, and on the tasks that have to be executed (Buja et al. 1996; Hinneburg et al. 1999; Keim 2002). Each step of the strategy is supplemented by design guidelines which, based on the cartographic design theory suggest how the data should look like for optimal visual representation. The resulting map images will display the complex relationships, and allow for insight in the nature of pedestrian movement and activity patterns. A smooth interactive transition between the different phases of the detail exploration process is required. 6. Temporal Variations in Spatial Distribution The specific aim of the research was to compare the shopping activities undertaken during the four days between two different streets, considering the proximity of both garages. The domain expert selected Choorstraat as an example of an old shopping area and Paradijspoort as a new development (see Figure 3). The purpose was to determine the intensity of their use by public for different days at different time period. To compare the activity types different types of shopping are distinguished, which each have their typical movement pattern: Window shopping walking in shops Efficient shopping visit of particular shop for particular reason No shopping pass by the street without stop GISscience 2012 / Columbus / Ohio Paper 127 Page 3 of 6
4 As an example the movements in the Choorstraat and Pradaijspoort for four different days are analyzed (see Figure 4). The representation in the STC gives ability to detect the above mentioned shopping behaviors regarding to the two garages for a different days. Figure 3. The movements from both parking facilties into the city centre (blue= Phoenix garage; orange = Southport garage; labels indicated location of the streets (see also figure 4)). Figure 4. The pedestrians movement behaviour in the Choorstraat (left) and Paradaijspoort (right), blue - Phoenix garage and orange - Southport garage Figure 4 shows both streets in the STC, with left Choorstraat and right Paradijspoort. The first was mostly visited by pedestrians who came from Phoenix garage due to its near location. Only few visitors from the Southport garage pass the street. The second shows that it was mostly visited by pedestrians coming from Southport garage, and only few from visitors from Phoenix garage. The flow of people varied day by day. It can be seen that 19 and 20 of November are most busy. The flow of people on Thursday is due to the open Page 4 of 6 Paper 127 GISscience 2012 / Columbus / Ohio
5 market that takes place every week the same day and Friday s flow is related to the end of the week and leisure. The domain expert was also interested in the differentiation of the shopping activities during the day. For this reason, special attention was given to the speed of pedestrian s movement, because this can be related to the types of shopping mentions above. A Time Profile Graph (TPG) was developed to support exploration of pedestrian speeds. The TGP s in Figure 5 represent the movement activities detected for Choorstraat during four days. The different days give different patterns. Nearly horizontal lines are person just passing by, the vertical line in a path represents a visit to a shop and a line with a high inclination corresponds to window shoppers. The interrupted movements indicate the place of the entrance to other streets. The personal profiles can be linked to the graph. Wednesday - 18/11/2009 Thursday - 19/11/2009 Friday - 20/11/2009 Saturday - 21/11/2009 Figure 5. Time profile graph of Choorstraat for four days. The vertical axis in graph indicates time, and the horizontal axis represents the street. The vertical grid lines are located at positions along the street were it bends or where side streets join. The horizontal grid lines refer to time. The blue trajectories are pedestrians from Phoenix garage and orange pedestrians from Southport garage. The case study demonstrated that a visual exploration of movement patterns in the space-time cube can reveal a differentiating behaviour of the pedestrian. The analysis provided highlevel description of the attributes and behaviors of the shopping/leisure based pedestrians. This research project offered the urban planner an analytical tool, and the cartographers could apply their design ideas on real world data. Future cooperation is foreseen. GISscience 2012 / Columbus / Ohio Paper 127 Page 5 of 6
6 References Buja A, Cook D and Swayne DF, 1996, Interactive High-Dimensional Data Visualization. Computational and Graphical Statistics, 5(1): Demšar U and Virrantaus K, 2010, Space time density of trajectories: exploring spatiotemporal patterns in movement data. International Journal of Geographical Information Science, 24(10): Hägerstrand T, 1970, What about people in Regional Science? Papers in Regional Science, 24(1):6-21. Hinneburg A, Keim DA and Wawryniuk M, 1999, HD-Eye: visual mining of highdimensional data. Computer Graphics and Applications IEEE, 19(5): Huisman O and Forer P, 1998, Computational agents and urban life spaces: a preliminary realisation of the time-geography of student lifestylesproceedings of the 3 rd International Conference on GeoComputation, University of Bristol, United Kingdom, Sept , Keim D, 2002, Information Visualization and Visual Data Mining. Transactions on Visualization and Computer Graphics IEEE, 8(1):1-8. Kwan MP, 1999, Gender, the Home-Work Link, and Space-Time Patterns of Nonemployment Activities. Economic Geography, 75(4): Miller HJ, 1999, Measuring space-time accessibility benefeits within transportation networks: basic theory and computational procedures Geographical analysis, 31(1):1-26. Shneiderman B, 1996, The Eyes Have It: A Task by Data Type Taxonomy for Information VisualizationsProceedings of the 1996 IEEE Symposium on Visual Languages, USA, Van der Spek SC, 2009, Mapping Pedestrian Movement: Using Tracking Technologies in Koblenz. In Gartner G and Rehrl K (eds), Location Based Services and TeleCartography II, , Springer, Heidelberg, Berlin. Van der Spek SC, Van Schaick JV, De Bois P and De Haan R, 2009, Sensing Human Activity: GPS Tracking. Sensors, 9(4): Page 6 of 6 Paper 127 GISscience 2012 / Columbus / Ohio
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