Patrick Laube

Discovering relative motion patterns in groups of moving point objects

Technological advances in position‐aware devices are leading to a wealth of data documenting motion. The integration of spatio‐temporal data‐mining techniques in GIScience is an important research field to overcome the limitations of static Geographic Information Systems with respect to the emerging volumes of data describing dynamics. This paper presents a generic geographic knowledge discovery approach for exploring the motion of moving point objects, the prime modelling construct to represent GPS tracked animals, people, or vehicles. The approach is based on the concept of geospatial lifelines and presents a formalism for describing different types of lifeline patterns that are generalizable for many application domains. Such lifeline patterns allow the identification and quantification of remarkable individual motion behaviour, events of distinct group motion behaviour, so as to relate the motion of individuals to groups. An application prototype featuring novel data‐mining algorithms has been implemented and tested with two case studies: tracked soccer players and data points representing political entities moving in an abstract ideological space. In both case studies, a set of non‐trivial and meaningful motion patterns could be identified, for instance highlighting the characteristic ‘offside trap’ behaviour in the first case and identifying trendsetting districts anticipating a political transformation in the latter case.

Finding REMO — Detecting Relative Motion Patterns in Geospatial Lifelines

Technological advances in position aware devices increase the availability of tracking data of everyday objects such as animals, vehicles, people or football players. We propose a geographic data mining approach to detect generic aggregation patterns such as flocking behaviour and convergence in geospatial lifeline data. Our approach considers the object’s motion properties in an analytical space as well as spatial constraints of the object’s lifelines in geographic space. We discuss the geometric properties of the formalised patterns with respect to their efficient computation.

Analyzing Relative Motion within Groups of Trackable Moving Point Objects

The overall goal of the ongoing project is to develop methods for spatio-temporal analysis of relative motion within groups of moving point objects, such as GPS-tracked animals. Whereas recent efforts of dealing with dynamic phenomena within the GIScience community mainly concentrated on modeling and representation, this research project concentrates on the analytic task. The analysis is performed on a process level and does not use the traditional cartographic approach of comparing snapshots. The analysis concept called REMO (RElative MOtion) is based on the comparison of motion parameters of objects over time. Therefore the observation data is transformed into a 2.5-dimensional analysis matrix, featuring a time axis, an object axis and motion parameters. This matrix reveals basic searchable relative movement patterns. The current approach handles points in a pure featureless space. Case study data of GPS-observed animals and political entities in an ideological space are used for illustration purposes.

Reporting Leaders and Followers among Trajectories of Moving Point Objects

Widespread availability of location aware devices (such as GPS receivers) promotes capture of detailed movement trajectories of people, animals, vehicles and other moving objects, opening new options for a better understanding of the processes involved. In this paper we investigate spatio-temporal movement patterns in large tracking data sets. We present a natural definition of the pattern ‘one object is leading others’, which is based on behavioural patterns discussed in the behavioural ecology literature. Such leadership patterns can be characterised by a minimum time length for which they have to exist and by a minimum number of entities involved in the pattern. Furthermore, we distinguish two models (discrete and continuous) of the time axis for which patterns can start and end. For all variants of these leadership patterns, we describe algorithms for their detection, given the trajectories of a group of moving entities. A theoretical analysis as well as experiments show that these algorithms efficiently report leadership patterns.

How fast is a cow? Cross-Scale Analysis of Movement Data

Data representing the trajectories of moving point objects are becoming increasingly ubiquitous in GIScience, and are the focus of much methodological research aimed at extracting patterns and meaning describing the underlying phenomena. However, current research within GIScience in this area has largely ignored issues related to scale and granularity – in other words how much are the patterns that we see a function of the size of the looking glass that we apply? In this article we investigate the implications of varying the temporal scale at which three movement parameters, speed, sinuosity and turning angle are derived, and explore the relationship between this temporal scale and uncertainty in the individual data points making up a trajectory. A very rich dataset, representing the movement of 10 cows over some two days every 0.25 s is investigated. Our cross-scale analysis shows firstly, that movement parameters for all 10 cows are broadly similar over a range of scales when the data are segmented to remove quasi-static subtrajectories. However, by exploring realistic values of GPS uncertainty using Monte Carlo Simulation, it becomes apparent that fine scale measurement of all movement parameters is masked by uncertainties, and that we can only make meaningful statements about movement when we take these uncertainties into account.

Movement beyond the snapshot – Dynamic analysis of geospatial lifelines

Abstract Geographical Information Science is challenged by an unprecedented increase in the availability of tracking data related to human and animal movement, typically captured through location-aware portable devices such as GPS receivers. Capture of trajectory data at fine temporal and spatial granularities has allowed with the representation of detailed geospatial lifelines, opening new options for analysis. In this respect we propose a dynamic perspective to analysis which, in contrast to summary trajectory statistics on speed, motion azimuth or sinuosity, that refers to the variability of motion properties throughout the developing lifeline. Four specific lifeline context operators are identified in this paper: ‘instantaneous’, ‘interval’, ‘episodal’ and ‘total’. Using this framework, we discuss standardisations that integrate the extended set of motion descriptors within various temporal and spatial frames of reference and the proposed lifeline context operators and standardisations are illustrated using high resolution trajectory data obtained from homing pigeons carrying miniature global positioning devices.

Movement similarity assessment using symbolic representation of trajectories

This article describes a novel approach for finding similar trajectories, using trajectory segmentation based on movement parameters (MPs) such as speed, acceleration, or direction. First, a segmentation technique is applied to decompose trajectories into a set of segments with homogeneous characteristics with respect to a particular MP. Each segment is assigned to a movement parameter class (MPC), representing the behavior of the MP. Accordingly, the segmentation procedure transforms a trajectory to a sequence of class labels, that is, a symbolic representation. A modified version of edit distance called normalized weighted edit distance (NWED) is introduced as a similarity measure between different sequences. As an application, we demonstrate how the method can be employed to cluster trajectories. The performance of the approach is assessed in two case studies using real movement datasets from two different application domains, namely, North Atlantic Hurricane trajectories and GPS tracks of couriers in London. Three different experiments have been conducted that respond to different facets of the proposed techniques and that compare our NWED measure to a related method.

Semantic Trajectory Compression

In the light of rapidly growing repositories capturing the movement trajectories of people in spacetime, the need for trajectory compression becomes obvious. This paper argues for semantic trajectory compression (STC) as a means of substantially compressing the movement trajectories in an urban environment with acceptable information loss. STC exploits that human urban movement and its large---scale use (LBS, navigation) is embedded in some geographic context, typically defined by transportation networks. STC achieves its compression rate by replacing raw, highly redundant position information from, for example, GPS sensors with a semantic representation of the trajectory consisting of a sequence of events . The paper explains the underlying principles of STC and presents an example use case.

An approach to evaluating motion pattern detection techniques in spatio-temporal data

This paper presents a method to evaluate a geographic knowledge discovery approach for explor- ing the motion of point objects. The goal is to provide a means of considering the signiWcance of motion patterns, described through their interestingness. We use Monte-Carlo simulations of con- strained random walks to generate populations of synthetic lifelines, using the statistical properties of real observational data as constraints. Pattern occurrence in the synthetic data is then compared with observational data to assess the potential interestingness of the found patterns. We use motion data from wildlife biology and spatialisation in political science for the evaluation. The results of the numerical experiments show that the interestingness of found motion patterns is largely dependant on the conWguration of the pattern matching process, which includes the pattern extent, the temporal granularity, and the classiWcation schema used for the motion attributes azimuth and speed. The results of the numerical experiments allow interestingness to be attached only to some of the patterns found—other patterns were suggested to be not interesting. The evaluation method helps in estimat- ing useful conWgurations of the pattern detection process. This work emphasises the need to further investigate the statistical aspects of the problem under study in (geographic) knowledge discovery.

Semantic trajectory compression: Representing urban movement in a nutshell ∗

There is an increasing number of rapidly growing repositories capturing the movement of people in space-time. Movement trajectory compression becomes an ob- vious necessity for coping with such growing data volumes. This paper introduces the concept of semantic trajectory compression (STC). STC allows for substantially compressing trajectory data with acceptable information loss. It exploits that human urban mobility typically occurs in transportation networks that define a geographic context for the move- ment. In STC, a semantic representation of the trajectory that consists of reference points localized in a transportation network replaces raw, highly redundant position information (e.g., from GPS receivers). An experimental evaluation with real and synthetic trajectories demonstrates the power of STC in reducing trajectories to essential information and illus- trates how trajectories can be restored from compressed data. The paper discusses possible application areas of STC trajectories.