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TrajectoryLenses – A Set‐based Filtering and Exploration Technique for Long‐term Trajectory Data
Author(s) -
Krüger Robert,
Thom Dennis,
Wörner Michael,
Bosch Harald,
Ertl Thomas
Publication year - 2013
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12132
Subject(s) - computer science , geospatial analysis , traverse , set (abstract data type) , data set , trajectory , computer vision , matching (statistics) , artificial intelligence , term (time) , range (aeronautics) , task (project management) , filter (signal processing) , data mining , geography , cartography , statistics , physics , materials science , mathematics , quantum mechanics , astronomy , composite material , programming language , management , economics
The visual analysis of spatiotemporal movement is a challenging task. There may be millions of routes of different length and shape with different origin and destination, extending over a long time span. Furthermore there can be various correlated attributes depending on the data domain, e.g. engine measurements for mobility data or sensor data for animal tracking. Visualizing such data tends to produce cluttered and incomprehensible images that need to be accompanied by sophisticated filtering methods. We present TrajectoryLenses, an interaction technique that extends the exploration lens metaphor to support complex filter expressions and the analysis of long time periods. Analysts might be interested only in movements that occur in a given time range, traverse a certain region, or end at a given area of interest (AOI). Our lenses can be placed on an interactive map to identify such geospatial AOIs. They can be grouped with set operations to create powerful geospatial queries. For each group of lenses, users can access aggregated data for different attributes like the number of matching movements, covered time, or vehicle performance. We demonstrate the applicability of our technique on a large, real‐world dataset of electric scooter tracks spanning a 2‐year period.