Trajectory data mining: A review of methods and applications

The increasing use of location-aware devices has led to an increasing availability of trajectory data. As a result, researchers devoted their efforts to developing analysis methods including different data mining methods for trajectories. However, the research in this direction has so far produced mostly isolated studies and we still lack an integrated view of problems in applications of trajectory mining that were solved, the methods used to solve them, and applications using the obtained solutions. In this paper, we first discuss generic methods of trajectory mining and the relationships between them. Then, we discuss and classify application problems that were solved using trajectory data and relate them to the generic mining methods that were used and real world applications based on them. We classify trajectory-mining application problems under major problem groups based on how they are related. This classification of problems can guide researchers in identifying new application problems. The relationships between the methods together with the association between the application problems and mining methods can help researchers in identifying gaps between methods and inspire them to develop new methods. This paper can also guide analysts in choosing a suitable method for a specific problem. The main contribution of this paper is to provide an integrated view relating applications of mining trajectory data and the methods used

Participatory assessment of potato production systems and cultivar development in Rwanda

Potato cultivars grown in Rwanda are very old, low yielding and not amenable to food processing. High yielding and late blight tolerant cultivars for this country should be evaluated at different agro-ecozones prior to releasing them to farmers, who are yet to be integrated into potato breeding. The objectives of this study were to assess farmers’ preferred traits in potato cultivars and to gather knowledge from farmers about potato clones bred in Rwanda. Four respondents per village in 36 villages each for the districts of Musanze, Burera and Nyamagabe participated in the survey, whose questionnaire was about farm size, gender balance, land allocated to potatoes and other main crops, potato “seed” sourcing, potato production constraints and most important potato attributes. Potato was rated as the most important food and cash crop. ‘Kirundo’, ‘Cruza’, ‘Mabondo’ and ‘Victoria’ were the most popular cultivars. Among them, Mabondo’ was the most resistant to the oomycete Phytophthora infestans causing late blight. Potato production in Rwanda is limited by lack of improved cultivars, high temperature, drought, acidic soil, pathogens, insects, weeds, inadequate storage of tubers as planting material, post-harvest technology, low market price of tubers at harvest, lack of access to credit, climate change, and gaps such as inadequate fertilizer and fungicide applications. The most important cultivar attributes were high tuber yield, host plant resistance and high specific gravity or dry matter

A Framework for Analysing Trajectories of Movement in a Dynamic Geographic Context

Recent advances in location-aware technologies enable the collection of trajectories of moving entities, which can be useful in different application domains such as urban planning, transportation and environment management. The analysis of these trajectories has mainly focused on discovering movement patterns. However, the usefulness of the discovered patterns depends on the possibility to interpret and understand them. Recent studies have shown that the consideration of the movement context, while analysing trajectories, has the potential to support the understanding of movement patterns. However, the integration of movement context into the analysis of trajectories is still in its infancy and most of the available work considers only a static geographic context. This thesis develops a comprehensive conceptual and methodological framework for integrating a dynamic geographic context into the analysis of trajectories. In the first step, a conceptual model relating the movement to its dynamic geographic context is developed. The thesis establishes a classification of geographic context elements and then proposes a set of qualitative relations, termed movement interactions, between the movement and the context. In the second step, the thesis proposes an analysis framework which exploits the conceptual model developed. The analysis framework is based on the process of Knowledge Discovery in Database (KDD). The thesis focuses on two steps, which correspond to the steps of the KDD process aimed at discovering and interpreting patterns. The first step applies data mining and spatial analysis methods to extract interactions from trajectories and context data. The second step quantifies the extracted interactions and explores the correlation or dependence between the quantified interactions and dynamic attributes of the movement and the context. In order to evaluate the framework developed, the thesis executes three experiments using real trajectories of vehicle movement in urban environment. Each experiment focusses on specific challenges addressed by the thesis. The first experiment focuses on the temporal dynamics of the dynamic geographic context while the second experiment focusses on its spatial dynamics. While the first two experiments involve context data in pattern discovery, the third experiment involves context data for post-processing already discovered patterns. The experiments show that the integration of context data supports not only the interpretation of movement patterns but also a deeper understanding of the movement context. Furthermore, the experiments show that context data can be integrated at the pattern discovery stage or for post-processing already discovered patterns. The choice of the integration step depends on the data being analysed and the type of patterns being mined

How they move reveals what is happening: understanding the dynamics of big events from human mobility pattern

The context in which a moving object moves contributes to the movement pattern observed. Likewise, the movement pattern reflects the properties of the movement context. In particular, big events influence human mobility depending on the dynamics of the events. However, this influence has not been explored to understand big events. In this paper, we propose a methodology for learning about big events from human mobility pattern. The methodology involves extracting and analysing the stopping, approaching, and moving-away interactions between public transportation vehicles and the geographic context. The analysis is carried out at two different temporal granularity levels to discover global and local patterns. The results of evaluating this methodology on bus trajectories demonstrate that it can discover occurrences of big events from mobility patterns, roughly estimate the event start and end time, and reveal the temporal patterns of arrival and departure of event attendees. This knowledge can be usefully applied in transportation and event planning and management