Somayeh Dodge

Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects

We propose a segmentation and feature extraction method for trajectories of moving objects. The methodology consists of three stages: trajectory data preparation; global descriptors computation; and local feature extraction. The key element is an algorithm that decomposes the profiles generated for different movement parameters (velocity, acceleration, etc.) using variations in sinuosity and deviation from the median line. Hence, the methodology enables the extraction of local movement features in addition to global ones that are essential for modeling and analyzing moving objects in applications such as trajectory classification, simulation and extraction of movement patterns. As a case study, we show how the method can be employed in classifying trajectory data generated by unknown moving objects and assigning them to known types of moving objects, whose movement characteristics have been previously learned. We have conducted a series of experiments that provide evidence about the similarities and differences that exist among different types of moving objects. The experiments show that the methodology can be successfully applied in automatic transport mode detection. It is also shown that eye-movement data cannot be successfully used as a proxy of full-body movement of humans, or vehicles.

The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data

BackgroundThe movement of animals is strongly influenced by external factors in their surrounding environment such as weather, habitat types, and human land use. With advances in positioning and sensor technologies, it is now possible to capture animal locations at high spatial and temporal granularities. Likewise, scientists have an increasing access to large volumes of environmental data. Environmental data are heterogeneous in source and format, and are usually obtained at different spatiotemporal scales than movement data. Indeed, there remain scientific and technical challenges in developing linkages between the growing collections of animal movement data and the large repositories of heterogeneous remote sensing observations, as well as in the developments of new statistical and computational methods for the analysis of movement in its environmental context. These challenges include retrieval, indexing, efficient storage, data integration, and analytical techniques.ResultsThis paper contributes to movement ecology research by presenting a new publicly available system, Environmental-Data Automated Track Annotation (Env-DATA), that automates annotation of movement trajectories with ambient atmospheric observations and underlying landscape information. Env-DATA provides a free and easy-to-use platform that eliminates technical difficulties of the annotation processes and relieves end users of a ton of tedious and time-consuming tasks associated with annotation, including data acquisition, data transformation and integration, resampling, and interpolation. The system is illustrated with a case study of Galapagos Albatross (Phoebastria irrorata) tracks and their relationship to wind, ocean productivity and chlorophyll concentration. Our case study illustrates why adult albatrosses make long-range trips to preferred, productive areas and how wind assistance facilitates their return flights while their outbound flights are hampered by head winds.ConclusionsThe new Env-DATA system enhances Movebank, an open portal of animal tracking data, by automating access to environmental variables from global remote sensing, weather, and ecosystem products from open web resources. The system provides several interpolation methods from the native grid resolution and structure to a global regular grid linked with the movement tracks in space and time. The aim is to facilitate new understanding and predictive capabilities of spatiotemporal patterns of animal movement in response to dynamic and changing environments from local to global scales.

Environmental drivers of variability in the movement ecology of turkey vultures (Cathartes aura) in North and South America.

Variation is key to the adaptability of species and their ability to survive changes to the Earths climate and habitats. Plasticity in movement strategies allows a species to better track spatial dynamics of habitat quality. We describe the mechanisms that shape the movement of a long-distance migrant bird (turkey vulture, Cathartes aura) across two continents using satellite tracking coupled with remote-sensing science. Using nearly 10 years of data from 24 satellite-tracked vultures in four distinct populations, we describe an enormous amount of variation in their movement patterns. We related vulture movement to environmental conditions and found important correlations explaining how far they need to move to find food (indexed by the Normalized Difference Vegetation Index) and how fast they can move based on the prevalence of thermals and temperature. We conclude that the extensive variability in the movement ecology of turkey vultures, facilitated by their energetically efficient thermal soaring, suggests that this species is likely to do well across periods of modest climate change. The large scale and sample sizes needed for such analysis in a widespread migrant emphasizes the need for integrated and collaborative efforts to obtain tracking data and for policies, tools and open datasets to encourage such collaborations and data sharing.

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.

Analysis of movement data

The study of movement is progressing rapidly as a subdiscipline in Geographic Information Science (GIScience). At the fulcrum of this new research area in GIScience are movement observations. Movement observations may be understood as spatiotemporal signals, which carry information on the movement of dynamic entities and the underlying mechanisms that drive their movement. These observations are key to the study and understanding of movement. Technological advancements in global positioning systems (GPS) and related satellite tracking technologies have resulted in significant increases in the availability of highly accurate data on moving phenomena, dramatically outpacing the development of appropriate methods with which to analyze them. In addition to increased spatial accuracy and temporal resolution of the locational information, improvements are being made to accelerometers and ‘biologgers’ that enable the collection of ancillary behavioral and physiological information. This special issue emerged from a pre-conference event associated with the GIScience 2014 conference held in Vienna: a workshop organized by the authors on ‘Analysis of Movement Data’ (AMD 2014). The workshop and this special issue explore recent trends in the study of movement and novel methods for analyzing and contextualizing movement data. A broad range of topics is covered concerning movement analysis, representation, and modeling. The studies presented use movement data from different domains, such as transportation (vehicles, marine traffic), cyclists and athlete tracking data, storm events, and movement ecology (birds, mammals, etc.). This editorial intends to frame and position the papers included in this special issue and to provide recommendations for future directions in the analysis of movement data. In order to frame the work presented here, we use the overarching research framework for the study of movement proposed by Dodge (2015). This framework, shown in an adapted version in Figure 1, posits that the study of movement consists of a continuum of research ranging from understanding movement to construct knowledge of the behavior of dynamic objects, to using this knowledge for modeling and prediction of movement. Visualization facilitates this process through data exploration, hypothesis generation, and communication of the outcomes (Andrienko et al. 2013, Wood et al. 2011, Zhang et al. 2013, Xavier and Dodge 2014). The framework relies on an iterative validation process, where analytics and models are parameterized, calibrated, and improved using real movement observations. Understanding movement, shown on the right side of Figure 1, entails development of methods for quantification of movement and its parameters (Dodge et al. 2008, Long and Nelson 2013, Laube 2014, Demšar et al. 2015); analysis of its context INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2016 VOL. 30, NO. 5, 825–834 http://dx.doi.org/10.1080/13658816.2015.1132424

Context-Aware Similarity of Trajectories

The movement of animals, people, and vehicles is embedded in a geographic context. This context influences the movement. Most analysis algorithms for trajectories have so far ignored context, which severely limits their applicability. In this paper we present a model for geographic context that allows us to integrate context into the analysis of movement data. Based on this model we develop simple but efficient context-aware similarity measures. We validate our approach by applying these measures to hurricane trajectories.

Similarity of trajectories taking into account geographic context

The movements of animals, people, and vehicles are embedded in a geographic context. This context influences the movement and may cause the formation of certain behavioral responses. Thus, it is essential to include context parameters in the study of movement and the development of movement pattern analytics. Advances in sensor tech- nologies and positioning devices provide valuable data not only of moving agents but also of the circumstances embedding the movement in space and time. Developing knowl- edge discovery methods to investigate the relation between movement and its surround- ing context is a major challenge in movement analysis today. In this paper we show how to integrate geographic context into the similarity analysis of movement data. For this, we discuss models for geographic context of movement data. Based on this we develop simple but efficient context-aware similarity measures for movement trajectories, which combine a spatial and a contextual distance. These are based on well-known similarity measures for trajectories, such as the Hausdorff, Frechet, or equal time distance. We validate our approach by applying these measures to movement data of hurricanes and albatross.

Exploring movement-similarity analysis of moving objects

Extracting knowledge about the movement of different types of mobile agents (e.g. human, animals, vehicles) and dynamic phenomena (e.g. hurricanes) requires new exploratory data analysis methods for massive movement datasets. Different types of moving objects share similarities but also express differences in terms of their dynamic behavior and the nature of their movement. Extracting such similarities can significantly contribute to the prediction, modeling and simulation dynamic phenomena. Therefore, with the development of a quantitative methodology this research intends to investigate and explore similarities in the dynamics of moving objects by using methods of GIScience in knowledge discovery. This paper presents a summary of the ongoing Ph.D. research project.

Characterizing change points and continuous transitions in movement behaviours using wavelet decomposition

Summary Individual behaviour, that is, the reaction of an organism to internal state, conspecifics and individuals of other species as well as the environment, is a crucial building block of their ecology. Modern tracking techniques produce high-frequency observations of spatial positions of animals and accompanying speed and tortuosity measurements. However, inferring behavioural modes from movement trajectories remains a challenge. Changes in behavioural modes occur at different temporal and spatial scales and may take two forms: abrupt, representing distinct change points; or continuous, representing smooth transitions between movement modes. The multi-scale nature of these behavioural changes necessitates development of methods that can pinpoint behavioural states across spatial and temporal scales. We propose a novel segmentation method based on the discrete wavelet transform (DWT), where the movement signal is decomposed into low-frequency approximation and high-frequency detail sub-bands to screen for behavioural changes at multiple scales. Approximation sub-bands characterizes broad changes by taking the continuous variations between behavioural modes into account, whereas detail sub-bands are employed to detect abrupt, finer scale change points. We tested the ability of our method to identify behavioural modes in simulated trajectories by comparing it to three state-of-the-art methods from the literature. We further validated the method using an annotated dataset of turkey vultures (Cathartes aura) relating extracted segments to the expert knowledge of migratory vs. non-migratory patterns. Our results show that the proposed DWT segmentation is more versatile than other segmentation methods, as it can be applied to different movement parameters, performs better or equally well on the simulated data, and correctly identifies behavioural modes identified by the experts. It is hence a valuable addition to the toolbox of land managers and conservation practitioners to understand the behavioural patterns expressed by animals in natural and human-dominated landscapes.

An exploratory visualization tool for mapping the relationships between animal movement and the environment

Movement ecologists and environmental scientists are increasingly utilizing large volumes of spatiotemporal data collected from animal tracking and remote sensing of the environment to explore the environmental drivers of animal movements and long distance migrations. For scientists, the visual exploration and mapping of animal tracks in space and time and in relation to their environment is key to the generation of new hypotheses and investigation of dependencies. Effective visualization of such multidimensional data is a time consuming and challenging process. Creating professional, interactive, and fluid looking animations for public outreach and presentation purposes is similarly arduous. We present a new exploratory visualization tool for the analysis of animal movement datasets enriched with environmental variables and other miscellaneous data. This interactive visualization tool is designed to be effective and intuitive for users from biology and ecology disciplines, who are rather unfamiliar with GIS and mapping software. The tool is able to illustrate how environmental factors influence movement patterns of animals by offering to the user a variety of visual variables that can be combined in novel ways. We demonstrate the capabilities of this new visualization tool by analyzing the movement data of nine Galapagos Albatrosses and one Turkey Vulture migration track.