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A dimension-reduced and linearly ordered (DRLOP) index structure for network-constrained moving objects is proposed to address the challenges posed by the rapid increase in the volume, diversity, and intensity of spatio-temporal data. The critical metadata rectangles are projected into reduced-dimensional phase points, and then arranged into a linear order structure to speed up retrieval through filtering and binary search. Using a dataset generated by a moving object generator from the underlying road network in Texas, our experimental results demonstrated the superior efficiency of the proposed index compared with the MON-Tree in terms of query and index creation. DRLOP exhibits the advantages of simple index structure, small storage space consumption, and high efficiency of spatio-temporal query operation, which is especially suitable for retrievals of large-scale historical trajectory data. [ABSTRACT FROM AUTHOR]

Contact mining is discovering objects in close proximity in their movements in order to reveal possible interactions, infections, collisions or contacts. This process can be significantly beneficial in a spread of an infectious disease situation to identify potential victims from a known infected human or animal, especially when the victims are asymptomatic. Movements of objects are captured by spatio-temporal trajectories represented by a series of geospatial locations and corresponding timestamps. A large amount of spatio-temporal trajectory data is being gathered by various location acquiring sensor devices by tracking movement behaviours of people, animals, vehicles and natural events. Trajectory data mining techniques have been proposed to discover useful patterns to understand the behaviours of spatio-temporal trajectories. One unexplored pattern is to identify contacts of targeted trajectory in spatio-temporal trajectories, which is defined as contact mining. The aim of this study is to investigate contact mining from spatio-temporal trajectories. The approach will be initiated by preprocessing spatio-temporal data and then by investigating a robust contact mining framework to efficiently and effectively mine contacts of a trajectory of interest from a given set of trajectories. Experimental results demonstrate the efficiency, effectiveness and scalability of our approach. In addition, parameter sensitivity analysis reveals the robustness and insensitivity of our framework.

Spatio-temporal trajectory is a movement of an object in space over a certain time period, represented by a series of nodes composed of geospatial location and corresponding timestamp. A large amount of spatio-temporal trajectory data is being gathered through various trajectory acquiring devices by tracking the movement of objects such as people, animals, vehicles and natural events. Various trajectory data mining techniques have been proposed to discover useful patterns to understand the behaviour of spatio-temporal trajectories. One unexplored pattern is to identify potential contacts of targeted trajectories which can be defined as contact mining, that is useful for many applications. One such example would be to identify potential victims from known infected humans or animals, especially when the victims are asymptomatic in a rapid spread of infectious disease environments. Another one would be to identify individuals who have been close contacts with known terrorist networks or law breakers. This paper proposes a robust contact mining framework to efficiently and effectively mine contacts of multiple trajectories-of-interest from a given set of spatio-temporal trajectories. Experimental results demonstrate the efficiency, effectiveness and scalability of our approach. In addition, parameter sensitivity analysis reveals the robustness and insensitivity of our framework.

Abstract Characterizing the movement patterns of animals is crucial to improve our understanding of their behavior and thus develop adequate conservation strategies. Such investigations, which could not have been implemented in practice only a few years ago, have been facilitated through the recent advances in tracking methods that enable researchers to study animal movement at an unprecedented spatio-temporal resolution. However, the identification and extraction of patterns from spatio-temporal trajectories is still a general problem that has relevance for many applications. Here, we rely on the concept of resting event networks to identify the presence of daily mobility patterns in animal spatio-temporal trajectories. We illustrate our approach by analyzing spatio-temporal trajectories of several fish species in a large hydropeaking river.

Recent years have seen rising needs for location-based services in our everyday life. Aside from the many advantages provided by these services, they have caused serious concerns regarding the location privacy of users. Adversaries can monitor the queried locations by users to infer sensitive information, such as home addresses and shopping habits. To address this issue, dummy-based algorithms have been developed to increase the anonymity of users, and thus, protecting their privacy. Unfortunately, the existing algorithms only assume a limited amount of side information known by adversaries, which may face more severe challenges in practice. In this paper, we develop an attack model termed as Viterbi attack, which represents a realistic privacy threat on user trajectories. Moreover, we propose a metric called transition entropy that enables the evaluation of dummy-based algorithms, followed by developing a robust algorithm that can defend users against the Viterbi attack while maintaining significantly high performance in terms of the traditional metrics. We compare and evaluate our proposed algorithm and metric on a publicly available dataset published by Microsoft, i.e., Geolife dataset. [ABSTRACT FROM AUTHOR]

It is well-known that online services resort to various cookies to track users through users’ online service identifiers (IDs) – in other words, when users access online services, various “fingerprints” are left behind in the cyberspace. As they roam around in the physical world while accessing online services via mobile devices, users also leave a series of “footprints” – i.e., hints about their physical locations – in the physical world. This poses a potent new threat to user privacy: one can potentially correlate the “fingerprints” left by the users in the cyberspace with “footprints” left in the physical world to infer and reveal leakage of user physical world privacy, such as frequent user locations or mobility trajectories in the physical world – we refer to this problem as user physical world privacy leakage via user cyberspace privacy leakage. In this paper we address the following fundamental question: what kind – and how much – of user physical world privacy might be leaked if we could get hold of such diverse network datasets even without any physical location information. In order to conduct an in-depth investigation of these questions, we utilize the network data collected via a DPI system at the routers within one of the largest Internet operator in Shanghai, China over a duration of one month. We decompose the fundamental question into the three problems: i) linkage of various online user IDs belonging to the same person via mobility pattern mining; ii) physical location classification via aggregate user mobility patterns over time; and iii) tracking user physical mobility. By developing novel and effective methods for solving each of these problems, we demonstrate that the question of user physical world privacy leakage via user cyberspace privacy leakage is not hypothetical, but indeed poses a real potent threat to user privacy. [ABSTRACT FROM AUTHOR]

An increasing share of people and economic activities are attracted by the cities. This fact shows positive aspects and at the same time causes challenges, mainly in reference to the soil whose ecosystem services can be disrupted when the land cover is modified. Therefore, urbanization is a critical issue for the land management. Contrasting the urban sprawl (i.e. the spontaneous, unplanned process transforming vegetated land covers into artificial ones) is relevant for soil protection in terms of minimizing the land take. Remote sensing technologies have provided support (with an ex-post approach) to understand urban sprawl as a process and to assess its impacts on the sustainability of land management. The current availability of high-resolution/very-high-resolution (HR/VHR) satellite data suggests to explore a different approach, aiming to deploy timely adequate countermeasures. The analysis of the urban sprawl processes pinpoints how the induced land cover changes show some specific patterns; moreover, distinctive trajectories in the space of multitemporal/multispectral imagery data can be elicited, relying also upon vegetation indices as the Normalized Difference Vegetation Index (NDVI). Accordingly, suitable precursors of urban sprawl processes can be detected. Such precursors can support a novel ex-ante approach in preventing the consolidation of the outcomes of the urban sprawl processes. [ABSTRACT FROM AUTHOR]

Agribusiness is an activity that generates huge amounts of temporal data. There are research centers that collect, store and create indexes of agricultural activities, providing multidimensional time series composed by years of data. In this paper, we are interested in studying the behavior of these time series, especially in what regards the evolution of agricultural price indexes over the years. We explore data mining techniques tailored to analyze temporal data, aiming to generate spatio-temporal trajectories of grains price indexes for six years of data. We propose the use of Tucker decomposition to both analyze the temporal patterns of these price indexes and map trajectories that represent their behavior over time in a concise and representative low-dimensional subspace. The case study presents an application of this methodology to real databases of price indexes of corn and soybeans in Brazil and the United States. [ABSTRACT FROM AUTHOR]

Nowadays, the accumulation of people's whereabouts due to location-based applications has made it possible to construct their mobility profiles. This access to users' mobility profiles subsequently brings benefits back to location-based applications. For instance, in on-line social networks, friends can be recommended not only based on the similarity between their registered information, for instance, hobbies and professions but also referring to the similarity between their mobility profiles. In this article, we propose a new approach to construct and compare users' mobility profiles. First, we improve and apply frequent sequential pattern mining technologies to extract the sequences of places that a user frequently visits and use them to model his mobility profile. Second, we present a new method to calculate the similarity between two users using their mobility profiles. More specifically, we identify the weaknesses of a similarity metric in the literature, and propose a new one which not only fixes the weaknesses but also provides more precise and effective similarity estimation. Third, we consider the semantics of spatiotemporal information contained in user mobility profiles and add them into the calculation of user similarity. It enables us to measure users' similarity from different perspectives. Two specific types of semantics are explored in this article: location semantics and temporal semantics. Last, we validate our approach by applying it to two real-life datasets collected by Microsoft Research Asia and Yonsei University, respectively. The results show that our approach outperforms the existing works from several aspects. [ABSTRACT FROM AUTHOR]