Whereas some trajectory operations can be performed in GIS environments, the temporal dimension often requires the use of additional data processing tools e. A first crucial task is enriching trajectories with descriptive variables such as speed, step length, acceleration, turning angle, or sinuosity. A second key trajectory operation is segmentation, decomposing the trajectory into self-similar segments Buchin et al.
Very often a first segmentation will separate moves from stops, that is periods when the object is not moving at all. This may be important with tracking systems that keep recording positions whilst the object is static, potentially producing pseudo-movement resulting from inaccurate measurements rather than actual movement. Comparing trajectories is a further important trajectory operation. Comparing trajectories is important for grouping or clustering objects that express similar or dissimilar movement. The catalogue of proposed approaches reaches from geometry-based line similarity measures to time series analysis to edit distance concepts borrowed from string analysis and genetics Dodge et al.
Once a suitable similarity metric has been found, conventional clustering techniques allow grouping of objects according to their movement properties.
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The key to a better understanding of movement processes very often lies in putting the trajectories in context to the underlying geography, hence studying relations between the trajectories and the enabling and constraining geographic context Nathan et al In the simplest case this may mean adding a land-use category to every observed position of a moving entity.
More complex forms of such semantic enrichment of trajectories may involve buffer operations or topological operations.
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Another challenge for CMA comes in the form of additional sensor data further characterizing mobility behavior Shamoun-Baranes et al. The most prominent such sensor is the accelerometer, nowadays-standard equipment even on mobile phones. Also many GPS trackers are complemented with accelerometers, adding further data dimensions to the mobility monitoring exercise.
Combined with speed readings accelerometer data may help qualifying different behaviors when monitoring animals or detecting travel modes when monitoring commuters. Further sensors allowing such semantic enrichment of trajectories include sensors tracking physiological parameters such as heart rate or body temperature, acoustic sensors, or light sensors. Movement patterns can refer to frequently used routes by commuters, spatially-explicit manifestations of animal behavior such as hunting excursions of feral animals, or arrangement and coordination patterns relating sets of mobile objects such as flocking, convoys, or leadership Andrienko et al.
First of all, data mining algorithms were deemed to be a natural fit, with borrowings from spatio-temporal association rule mining known from market basket analysis or time series analysis from stock exchange analysis. Then, movement data and its processing also led to significant developments in the database community, with moving object database MOD specifically being tailored for managing movement data and querying patterns.
Furthermore, the computer science discipline of computational geometry produced a wide range of movement pattern approaches focusing mostly on the geometric aspects of arrangements of points and lines representing movement. Detecting movement patterns in essence means structuring data: Be it finding spatio-temporal hot-spots of space-use, clusters of objects expressing similar movement or coordination events in groups of moving objects.
The next knowledge discovery step of attaching meaning and process understanding to those same patterns, is much harder. Typical views include maps, 3D space-time spaces, and parameter spaces typically depicting time series of derived movement parameters such as speed or acceleration. Also, the interactive visualization of previously mined movement patterns in map views — hence the combination of data mining and visualization promoted as visual analytics — works well for exploring large streams of movement data.
Visual analytics of movement: An overview of methods, tools and procedures.
Information Visualization , DOI: Basic concepts of movement data. In Mobility, data mining and privacy pp. Finding long and similar parts of trajectories. Computational Geometry , 44 9 , Analysis and visualisation of movement: Movement ecology , 3 1 , Movement Ecology , 1 1 , 3. Movement similarity assessment using symbolic representation of trajectories. International Journal of Geographical Information Science , 26 9 , Towards a taxonomy of movement patterns. Visualizations can make the structure and dependencies between elements in processes accessible The University of Konstanz, with its Institutional Strategy to promote Top-Level Research, has been receiving continuous funding since within Social networks collected by historians or sociologists typically have a large number of attributes associated with their actors, and applying Home What is Visual Analytics?
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