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Chapter 14 Analysing Point Motion with Geographic Knowledge Discovery Techniques Patrick Laube 1, Ross S. Purves 2, Stephan Imfeld 2 and Robert Weibel 2 1 School of Geography and Environmental Science, University of Auckland, New Zealand 2 Department of Geography, University of Zurich, Switzerland 14.1 Introduction Mobility is key to contemporary life. In a globalised world, people, goods, data and ideas move in increasing volumes at increasing speeds over increasing distances, and more and more leave a digital trail behind them. More and more such tracking data is automatically collected in large databases. Exploring the dynamic processes afforded by the study of such digital trails—in other words motion—is an emerging research area in Geographical Information Science. This chapter argues that Geographical Information Science can centrally contribute to discovering knowledge about the patterns made in space-time by individuals and groups within large volumes of tracking data. Whereas the representation and visualisation of motion is quite widespread within the discipline, approaches to actually quantitatively analysing motion are rare. Hence, this chapter introduces an approach to analysing the tracks of moving point objects, which are considered as the most basic and commonly used conceptualisation in representing motion in geography. The methodological approach adopted is Geographic Knowledge Discovery (GKD)—an interactive and iterative process integrating a collection of methods from geography, computer science, statistics and scientific visualisation (Miller and Han, 2001). Its goal is the extraction of high-level information from low-level data in the context of large geographic datasets (Fayyad et al., 1996). This chapter sets out to illustrate that the integration of knowledge discovery methods within Geographical Information Science provides a powerful means to investigate motion processes captured in tracking data. The chapter is structured as follows. Section 14.2 provides a literature overview on analysing point motion, identifies some shortcomings and proposes a set of objectives that the remainder of the chapter attempts to address. In Section 14.3 the central tenets of the proposed motion analysis approach are introduced. The methods are illustrated in Section 14.4, using case studies from biology, sport’s ____________________________________________________________________________________ Dynamic and Mobile GIS: Investigating Changes in Space and Time. Edited by Jane Drummond, Roland Billen, Elsa João and David Forrest. © 2006 Taylor & Francis © 2007 by Taylor & Francis Group, LLC 264 Dynamic and Mobile GIS: Investigating Changes in Space and Time scene analysis and spatialisation of political science data. Section 14.5 critically discusses this methodological approach to the mining of motion data. The chapter concludes by identifying the key steps made in integrating knowledge discovery techniques in Geographical Information Science for analysing motion and gives an outlook as to possible future work. 14.2 Motion analysis in Geographical Information Science This section discusses the role of motion analysis in the field of Geographical Information Science and associated disciplines. The potential and limitations of recent work are discussed, and a set of objectives underpinning the work presented in this chapter are formulated. The analysis approach proposed in this chapter focuses on the motion of points. Although all three fundamental abstractions of spatial entities, points, lines and polygons, may move in space and time, the most common representation of moving objects is points. Be it for tracked animals, taxi cabs or carriers of location-aware devices, the simplest way to track motion is to specify location at any time t by either a record of (x,y,t) coordinates or by a record of (x,y,z,t) coordinates. Thus, the prime object of interest of this chapter is the moving point object, irrespective of its real-world counterpart. The most basic conceptualisation of the path of a moving point object is the so called ‘geo-spatial lifeline’ (Hornsby and Egenhofer, 2002; Mark, 1998). Mark (1998, p. 12) defines a geo-spatial lifeline as a ‘continuous set of positions occupied in space over some time period’. Geo-spatial lifeline data usually consists of discrete fixes, describing an individual`s location in geographic space with regular or irregular temporal intervals. 14.2.1 Visual exploration of motion data The simplest way to visualise the motion of a moving point object is to map its complete trajectory on a Cartesian plane. Labelling of intermediate positions can add temporal information to the track in order to visualise the object`s past locations. The symbology and the colour of the trajectory can also code motion speed, acceleration or motion azimuth (Dykes and Mountain, 2003). Adding time as a third dimension allows the visual representation of trajectories in 3-D. Thus, increasing computational power in recent decades has given rise to a diverse set of applications adopting the space-time aquarium data model suggested by Hägerstrand’s time geography (Hägerstrand, 1970). Most prominent is the work by Forer`s group on visualising (and analysing) student lifestyles and tourism flows in New Zealand (Forer, 1998; Huisman and Forer, 1998; Forer et al., 2004). Most static visualisations of motion can be animated by browsing through the temporal dimension. Andrienko et al. (2000) propose the ‘dynamic interval view’ in a case study of migrating storks. The interval view shows trajectory fragments during the current interval. In their prototype application for transport demand modelling, Frihida et al. (2004) provide an animated 2-D map view to dynamically visualise individual space-time paths. Tools for the animated visualisation of motion © 2007 by Taylor & Francis Group, LLC 14. Analysing Point Motion with Geographic Knowledge Discovery Techniques 265 have recently found their way into commercial GIS. For example, ESRI offers the ArcGIS Tracking Analyst extension to visualise tracking data. It features various symbology options and a sophisticated playback manager. However, its power lies almost exclusively in the functionality provided to define events and to visualise where and when they occur. Exploratory data analysis (EDA) of motion data aims to find potentially explicable motion patterns. ‘Modern EDA methods emphasise the interaction between human cognition and computation in the form of dynamic statistical graphics that allow the user to directly manipulate various ‘views’ of the data. Examples of such views are devices such as histograms, box plots, q-q plots, dot plots, and scatter plots’ (Anselin, 1998, p. 78). Kwan (2000) proposes a set of 3-D techniques to explore disaggregate activity-travel behaviour from travel diary data. Kraak and Koussoulakou (2004) present an exploratory environment featuring alternative views, animation and query functions for motion data. As an excellent example of the exploratory analysis of motion data Brillinger et al. (2004) present a set of techniques applied to a huge collection of VHF telemetry tracked elk and deer. Parallel boxplots of the square roots of objects’ speed by hour of the day are used to analyse circadian rhythms. Collapsing all available data for one time of day creates ‘temporal transects’ well suited to descriptive statistics. Decomposing the object’s velocity to cardinal directions using a separate ‘X-component velocity plot’ and a ‘Y-component velocity plot’ provides insights on the directional bias in the joint motion of a group. Finally, ‘vector fields’ address the issue of the spatial distribution of motion properties and provide a sophisticated overview of the motion of a group moving in a distinct area over a distinct time period. However, most exploratory approaches stop at representation and delegate the analytical process to user interpretation. Furthermore, many visualisation approaches focus on position, ignoring inherent motion properties such as speed, acceleration, motion azimuth and sinuosity. However sophisticated the exploratory tools may be, the human capability to recognise complex visual patterns decreases rapidly with an increasing number of investigated trajectories and larger numbers of moving objects as shown in Figure 14.1. Kwan (2000, p. 197) states that ‘although the aquarium is a valuable representation device, interpretation of patterns becomes difficult as the number of paths increases…’. Thus, the exploratory power of ‘flying through the space-time aquarium’ is, in general, limited to a small number of moving point objects. © 2007 by Taylor & Francis Group, LLC 266 Dynamic and Mobile GIS: Investigating Changes in Space and Time Figure 14.1. Exploration of geo-spatial lifelines. (A) Mapping the geo-spatial lifelines of moving point objects in a static map ignores completely the temporal aspect of motion and leads to confusing representations, as illustrated here with the tracks of only a dozen caribou migrating by the Beaufort Sea during two seasons. (B) The turning angle distribution of the same group of caribou illustrates the directional persistence in their motion (0° for straight on). See colour insert following page 132. 14.2.2 Descriptive statistics of motion data Individual lifelines or aggregations of many lifelines and lifeline segments can be statistically described with respect to motion quantifiers such as travel distances, speed, acceleration, motion azimuth and sinuosity. The appropriate statistical description of motion is an important precondition for simulating motion processes, for example, in the field of behavioural ecology. For many ecological questions, for instance animal metapopulation dynamics, knowledge about the dispersal capability of animals is necessary and acquired through extensive empirical and theoretical research (Berger et al., 1999). Berger et al. identify three frequently used linear mobility measures to describe an individual`s motion in ecological field studies: mean daily movement, maximal distance between two fixes and the mean activity radius (that is the average distance between the capture point and all consecutive fixes). In behavioural ecology, frequency distributions of ‘step length’ and ‘turning angle’ are investigated to gain an overall impression of the motion of the animals under study (e.g. Hill and Häder, 1997; Ramos-Fernandez et al., 2004). Directional persistence is often a key issue investigating turning angle distributions (see Figure 14.1B). Trajectories are normally characterised using frequency distributions of discrete classes between –180° and 180° (e.g. Schmitt and Seuront, 2001; Ramos-Fernandez et al., 2004). When describing the motion direction, a motion azimuth (absolute direction with respect to North) distribution is sometimes preferred over the turning angle. Radar plots visualise the turning angle distributions around the compass card in a very illustrative way. © 2007 by Taylor & Francis Group, LLC 14. Analysing Point Motion with Geographic Knowledge Discovery Techniques 267 Mean values and frequency distributions may give an appropriate overview of the way that certain moving point objects move in space and time. However, summarising the complex motion phenomena found, for instance, in the geo-spatial lifelines of seasonally migrating caribou in just a few holistic statistical descriptors removes all dynamic aspects of the motion process. The authors argue therefore that descriptive statistics are not well suited to acquiring more insights into individual motion patterns or inter-object relations in the motion process. 14.2.3 Knowledge discovery and data mining in motion data Tracking motion processes very rapidly generates very large datasets. The Database Management Systems (DBMS) community, especially researchers interested in Spatiotemporal Database Management Systems (STDBMS), has introduced various approaches to querying databases covering moving objects (e.g. Sistla et al., 1998; Güting et al., 2003; Grumbach et al., 2003). However, querying a database means retrieval of stored objects, collections of objects or their observations from a database. Aronoff (1989) and Golledge (2002) argue that motion analysis, in contrast, must go beyond mere querying and requires the production of new information and knowledge that is not directly observed in the stored data. Thus, the aim of motion analysis must be to derive value-added knowledge about motion events. In recent years various techniques developed especially for large volume and multi-source data, such as Knowledge Discovery in Databases and its component data mining, have entered the field of Geographical Information Science. Fayyad et al. (1996, p. 40) define Knowledge Discovery in Databases (KDD) as the ‘nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data’. Data mining is just one central component of the overall knowledge discovery process denoting the application of specific algorithms for extracting patterns from data. Miller and Han (2001) identified unique needs and challenges for integrating KDD into Geographical Information Science because of the special properties of geographic data. Hence, they propose the development of specific Geographic Knowledge Discovery (GKD) and geographic data mining approaches. The latter ‘involves the application of computational tools to reveal interesting patterns in objects and events distributed in geographic space and across time. These patterns may involve the spatial properties of individual objects and events (such as shape, extent) and spatiotemporal relationships among objects and events in addition to non-spatial attributes of interest in traditional data mining’ (Miller and Han, 2001, p. 16). Although the ideas of geographic knowledge discovery match very closely the requirements for analysing motion, very few approaches actually mining motion data are found in the literature. Frihida et al. (2004) propose a knowledge discovery approach in the field of transport demand modelling. Their approach is designed to extract useful information from an origin–destination survey, i.e. to build individual space-time paths in the space-time aquarium. In a similar context Smyth (2001) presents a knowledge discovery approach to mine mobile trajectories. The overall © 2007 by Taylor & Francis Group, LLC ... - tailieumienphi.vn
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