A Refined Definition for Groups of Moving Entities and its Computation

One of the important tasks in the analysis of spatio-temporal data collected from moving entities is to find a group: a set of entities that travel together for a sufficiently long period of time. Buchin et al. [JoCG, 2015] introduce a formal definition of groups, analyze its mathematical structure, and present efficient algorithms for computing all maximal groups in a given set of trajectories. In this paper, we refine their definition and argue that our proposed definition corresponds better to human intuition in certain cases, particularly in dense environments. We present algorithms to compute all maximal groups from a set of moving entities according to the new definition. For a set of n moving entities in R^1, specified by linear interpolation in a sequence of tau time stamps, we show that all maximal groups can be computed in O(tau^2 n^4) time. A similar approach applies if the time stamps of entities are not the same, at the cost of a small extra factor of alpha(n) in the running time. In higher dimensions, we can compute all maximal groups in O(tau^2 n^5 log n) time (for any constant number of dimensions). We also show that one tau factor can be traded for a much higher dependence on n by giving a O(tau n^4 2^n) algorithm for the same problem. Consequently, we give a linear-time algorithm when the number of entities is constant and the input size relates to the number of time stamps of each entity. Finally, we provide a construction to show that it might be difficult to develop an algorithm with polynomial dependence on n and linear dependence on tau

Space transformation for understanding group movement

We suggest a methodology for analyzing movement behaviors of individuals moving in a group. Group movement is analyzed at two levels of granularity: the group as a whole and the individuals it comprises. For analyzing the relative positions and movements of the individuals with respect to the rest of the group, we apply space transformation, in which the trajectories of the individuals are converted from geographical space to an abstract 'group space'. The group space reference system is defined by both the position of the group center, which is taken as the coordinate origin, and the direction of the group's movement. Based on the individuals' positions mapped onto the group space, we can compare the behaviors of different individuals, determine their roles and/or ranks within the groups, and, possibly, understand how group movement is organized. The utility of the methodology has been evaluated by applying it to a set of real data concerning movements of wild social animals and discussing the results with experts in animal ethology

Mining candidate causal relationships in movement patterns

This is an Accepted Manuscript of an article published by Taylor & Francis in the International Journal of Geographical Information Science on 01 October 2013, available online: http://wwww.tandfonline.com/10.1080/13658816.2013.841167In many applications, the environmental context for, and drivers of movement patterns are just as important as the patterns themselves. This paper adapts standard data mining techniques, combined with a foundational ontology of causation, with the objective of helping domain experts identify candidate causal relationships between movement patterns and their environmental context. In addition to data about movement and its dynamic environmental context, our approach requires as input definitions of the states and events of interest. The technique outputs causal and causal-like relationships of potential interest, along with associated measures of support and confidence. As a validation of our approach, the analysis is applied to real data about fish movement in the Murray River in Australia. The results demonstrate the technique is capable of identifying statistically significant patterns of movement indicative of causal and causal-like relationships. 1365-8816Australian Research Council Discovery Projec

Developing a Wildlife Tracking Extension for ArcGIS

Wildlife tracking is an essential task to gain better understanding of the migration pattern and use of space of the wildlife. Advances in computer technology and global positioning systems (GPS) have lowered costs, reduced processing time, and improved accuracy for tracking wild animals. In this thesis, a wildlife tracking extension is developed for ArcGIS 9.x, which allows biologists and ecologists to effectively track, visualize and analyze the movement patterns of wild animals. The extension has four major components: (1) data import; (2) tracking; (3) spatial and temporal analysis; and (4) data export. Compared with existing software tools for wildlife tracking, the major features of the extension include: (1) wildlife tracking capabilities using a dynamic data layer supported by a file geodatabase with 1 TB storage limit; (2) spatial clustering of wildlife locations; (3) lacunarity analysis of one-dimensional individual animal trajectories and two-dimensional animal locations for better understanding of animal movement patterns; and (4) herds evolvement modeling and graphic representation. The application of the extension is demonstrated using simulated data, test data collected by a GPS collar, and a real dataset collected by ARGOS satellite telemetry for albatrosses in the Pacific Ocean

An Experimental Evaluation of Grouping Definitions for Moving Entities

One important pattern analysis task for trajectory data is to find a group: a set of entities that travel together over a period of time. In this paper, we compare four definitions of groups by conducting extensive experiments using various data sets. The grouping definitions are different by one or more of three different characteristics: whether they use the measured sample points or continuous movement, how distance is used to decide if entities are in the same group, and whether the duration of the group is measured cumulatively or as one contiguous time interval. We are interested in the differences between the definitions and comparisons to human-annotated data, if available. We concentrate on pedestrian data and on different crowd densities. Furthermore, we analyze the robustness of the definitions with respect to their dependence on different sampling rates. We use two types of trajectory data sets: synthetic trajectories and real-life trajectories extracted from video surveillance. We present the results of the quantitative evaluations. For experiments with real-life trajectories, we augment them with a qualitative evaluation using videos that show groups in the trajectories with a color coding

Detecting and Identifying Collective Phenomena within Movement Data

Collective phenomena are ubiquitous in our every day lives; each day we are likely to observe or take part in a collective. Examples include a traffic jam on the way to work, a flock of birds in the sky or a queue in the shop. These examples include only three types of collective that are considered in this thesis: those phenomena whose individual members can be assigned a physical location in geographic space. However, this criterion is satisfied by many different types of collective. The movement patterns that are exhibited by collectives are one of their most prominent properties; it is often the property that we wish to reason about most. For example, the movement patterns of crowds, traffic or demonstrations. This thesis hypothesises that, given a dataset that comprises the movement data for a group of individuals, the presence of certain collectives can be achieved through an examination of the exhibited movement patterns. To identify the different types of collective that exist, a general taxonomy of collectives is presented. A class of collectives are found to manifest themselves through spatial coherence. Therefore, a set of spatial coherence criteria have been developed that can be applied to a movement dataset to indicate if any individuals within that dataset may be participating in a spatial collective. To indicate the different types of spatial collective that may be extracted, a taxonomy of spatial collectives is also presented

Doctor of Philosophy

dissertationRecent advancements in mobile devices - such as Global Positioning System (GPS), cellular phones, car navigation system, and radio-frequency identification (RFID) - have greatly influenced the nature and volume of data about individual-based movement in space and time. Due to the prevalence of mobile devices, vast amounts of mobile objects data are being produced and stored in databases, overwhelming the capacity of traditional spatial analytical methods. There is a growing need for discovering unexpected patterns, trends, and relationships that are hidden in the massive mobile objects data. Geographic visualization (GVis) and knowledge discovery in databases (KDD) are two major research fields that are associated with knowledge discovery and construction. Their major research challenges are the integration of GVis and KDD, enhancing the ability to handle large volume mobile objects data, and high interactivity between the computer and users of GVis and KDD tools. This dissertation proposes a visualization toolkit to enable highly interactive visual data exploration for mobile objects datasets. Vector algebraic representation and online analytical processing (OLAP) are utilized for managing and querying the mobile object data to accomplish high interactivity of the visualization tool. In addition, reconstructing trajectories at user-defined levels of temporal granularity with time aggregation methods allows exploration of the individual objects at different levels of movement generality. At a given level of generality, individual paths can be combined into synthetic summary paths based on three similarity measures, namely, locational similarity, directional similarity, and geometric similarity functions. A visualization toolkit based on the space-time cube concept exploits these functionalities to create a user-interactive environment for exploring mobile objects data. Furthermore, the characteristics of visualized trajectories are exported to be utilized for data mining, which leads to the integration of GVis and KDD. Case studies using three movement datasets (personal travel data survey in Lexington, Kentucky, wild chicken movement data in Thailand, and self-tracking data in Utah) demonstrate the potential of the system to extract meaningful patterns from the otherwise difficult to comprehend collections of space-time trajectories