Towards semantic‐aware multiple‐aspect trajectory similarity measuring

The large amount of semantically rich mobility data becoming available in the era of big data has led to a need for new trajectory similarity measures. In the context of multiple‐aspect trajectories, where mobility data are enriched with several semantic dimensions, current state‐of‐the‐art approaches present some limitations concerning the relationships between attributes and their semantics. Existing works are either too strict, requiring a match on all attributes, or too flexible, considering all attributes as independent. In this article we propose MUITAS, a novel similarity measure for a new type of trajectory data with heterogeneous semantic dimensions, which takes into account the semantic relationship between attributes, thus filling the gap of the current trajectory similarity methods. We evaluate MUITAS over two real datasets of multiple‐aspect social media and GPS trajectories. With precision at recall and clustering techniques, we show that MUITAS is the most robust measure for multiple‐aspect trajectories.     

Numerical stability for modelling of dynamic two‐phase interaction

Dynamic two‐phase interaction of soil can be modelled by a displacement‐based, two‐phase formulation. The finite element method together with a semi‐implicit Euler–Cromer time‐stepping scheme renders a discrete equation that can be solved by recursion. By experience, it is found that the CFL stability condition for undrained wave propagation is not sufficient for the considered two‐phase formulation to be numerically stable at low values of permeability. Because the stability analysis of the two‐phase formulation is onerous, an analysis is performed on a simplified two‐phase formulation that is derived by assuming an incompressible pore fluid. The deformation of saturated porous media is now captured in a single, second‐order partial differential equation, where the energy dissipation associated with the flow of the fluid relative to the soil skeleton is represented by a damping term. The paper focuses on the different options to discretize the damping term and its effect on the stability criterion. Based on the eigenvalue analyses of a single element, it is observed that in addition to the CFL stability condition, the influence of the permeability must be included. This paper introduces a permeability‐dependent stability criterion. The findings are illustrated and validated with an example for the dynamic response of a sand deposit. Copyright © 2015 John Wiley & Sons, Ltd.     

A multitracer study of peat profiles from Tunguska, Siberia

Two peat columns from Tunguska (Siberia) were analysed for pollen, spores, charcoal, trace elements and γ-emitters in order to identify the fingerprints of the impact of a still unidentified cosmic body (TCB), which occurred in the summer of 1908, and the level of environmental pollution in a background area of central Siberia. Peat layers were subject to non-destructive γ-ray spectrometry to derive radiochronology by the excess ²¹⁰Pb method. The age-to-depth relationship was crosschecked by using both 1963 horizon of ¹³⁷Cs associated to maximum global fallout deposition and palynological data profiles. Vertical distributions of trace elements in the peat columns were obtained by PIXE multielemental analysis allowing determination of the levels of environmental contamination in a background region of the Siberian taiga.The association of heavy metals such as Ni, Co and Cu in the profiles suggests the connection of the area with mining and metal smelting activity in the north of the region through atmospheric circulation. As concerns global scale contamination, the inventory of the artificial radionuclide ¹³⁷Cs (4.6 kBq m^− 2) shows a value typical of remote slightly contaminated areas resulting from global scale redistribution of radioactive fallout from Cold War nuclear weapon testing. The atmospheric inventory of the natural radionuclide ²¹⁰Pb, for which a mean annual flux of 200 Bq m^− 2 yr^− 1 has been calculated, is typical of continental regions.The influence of Tunguska Cosmic Body in the peat is recognizable by a large discontinuity in the palynological profile of the peat monolith at a depth coinciding with the 1908 layer as determined by the ²¹⁰Pb technique, showing a large peak of total pollen counting attributed to the impact of the shockwave on the area in which huge tree stands were destroyed. Following the event, tree pollen concentration decreases abruptly showing the temporary inception of a mire environment with an increase of Sphagnum spore concentrations. Results of elemental analysis so far available do not show anomalies in the concentration profiles at depths coinciding with the Tunguska event layer indicating the need for pre-concentration technique enabling the detection of element associations typical of extraterrestrial materials.     

Unveiling movement uncertainty for robust trajectory similarity analysis

Trajectory data analysis and mining require distance and similarity measures, and the quality of their results is directly related to those measures. Several similarity measures originally proposed for time-series were adapted to work with trajectory data, but these approaches were developed for well-behaved data that usually do not have the uncertainty and heterogeneity introduced by the sampling process to obtain trajectories. More recently, similarity measures were proposed specifically for trajectory data, but they rely on simplistic movement uncertainty representations, such as linear interpolation. In this article, we propose a new distance function, and a new similarity measure that uses an elliptical representation of trajectories, being more robust to the movement uncertainty caused by the sampling rate and the heterogeneity of this kind of data. Experiments using real data show that our proposal is more accurate and robust than related work.     

MASTER: A multiple aspect view on trajectories

For many years trajectory data have been treated as sequences of space‐time points or stops and moves. However, with the explosion of the Internet of Things and the flood of big data generated on the Internet, such as weather channels and social network interactions, which can be used to enrich mobility data, trajectories become more and more complex, with multiple and heterogeneous data dimensions. The main challenge is how to integrate all this information with trajectories. In this article we introduce a new concept of trajectory, called multiple aspect trajectory, propose a robust conceptual and logical data model that supports a vast range of applications, and, differently from state‐of‐the‐art methods, we propose a storage solution for efficient multiple aspect trajectory queries. The main strength of our data model is the combination of simplicity and expressive power to represent heterogeneous aspects, ranging from simple labels to complex objects. We evaluate the proposed model in a tourism scenario and compare its query performance against the state‐of‐the‐art spatio‐temporal database SECONDO extension for symbolic trajectories.     

Discovering Chasing Behavior in Moving Object Trajectories

With the increasing use of mobile devices, a lot of tracks of movement of objects are being collected. The advanced trajectory data mining research has allowed the discovery of many types of patterns from these data, like flocks, leadership, avoidance, frequent sequences, and other types of patterns. In this article we introduce a new kind of pattern: a chasing behavior between trajectories. We present the main characteristics of chasing and propose a new method that extracts this new kind of trajectory behavior pattern, considering time, distance, and speed as the main thresholds. Experimental results show that our method finds patterns that are not discovered by related approaches.     

Weka‐STPM: a Software Architecture and Prototype for Semantic Trajectory Data Mining and Visualization

Enormous quantities of trajectory data are collected from many sources, such as GPS devices and mobile phones, as sequences of spatio‐temporal points. These data can be used in many application domains such as traffic management, urban planning, tourism, bird migration, and so on. Raw trajectory data, as generated by mobile devices have very little or no semantics, and in most applications a higher level of abstraction is needed to exploit these data for decision making. Although several different methods have been proposed so far for trajectory querying and mining, there are no software tools to help the end user with semantic trajectory data analysis. In this article we present a software architecture for semantic trajectory data mining as well as the first software prototype to enrich trajectory data with both semantic information and data mining. As a prototype we extend the Weka data mining toolkit with the module Weka‐STPM, which is interoperable with databases constructed under OGC specifications. We tested Weka‐STPM with real geographic databases, and trajectory data stored under the Postgresql/PostGIS DBMS.     

ST‐DMQL: A Semantic Trajectory Data Mining Query Language

Mobile devices are becoming very popular in recent years, and large amounts of trajectory data are generated by these devices. Trajectories left behind cars, humans, birds or other objects are a new kind of data which can be very useful in the decision making process in several application domains. These data, however, are normally available as sample points, and therefore have very little or no semantics. The analysis and knowledge extraction from trajectory sample points is very difficult from the user's point of view, and there is an emerging need for new data models, manipulation techniques, and tools to extract meaningful patterns from these data. In this paper we propose a new methodology for knowledge discovery from trajectories. We propose through a semantic trajectory data mining query language several functionalities to select, preprocess, and transform trajectory sample points into semantic trajectories at higher abstraction levels, in order to allow the user to extract meaningful, understandable, and useful patterns from trajectories. We claim that meaningful patterns can only be extracted from trajectories if the background geographical information is considered. Therefore we build the proposed methodology considering both moving object data and geographic information. The proposed language has been implemented in a toolkit in order to provide a first software prototype for trajectory knowledge discovery.     

The regional ecology of elderly falls in Sweden

The study assesses exploratory the geography of the elderly fall in Sweden in relation to the ecology of the socio-demographic characteristics of the Swedes older population. Kendall Test is used to measure the association between elderly fall rates and demographic, socio-economic characteristics of the population, costs of elderly care and accessibility measures at county level. Results show a number of significant associations: high rates of the elderly fall are associated with high cost of the elderly care but also low rate of elderly fall and good accessibility to basic services (e.g., grocery store, health care and cash machines). The articles finalizes with reflections of the results and suggestions for future research.