A model for enriching trajectories with semantic geographical information
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Visual analytics tools for analysis of movement data
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
A conceptual view on trajectories
Data & Knowledge Engineering
Mobility, Data Mining and Privacy: Geographic Knowledge Discovery
Mobility, Data Mining and Privacy: Geographic Knowledge Discovery
A clustering-based approach for discovering interesting places in trajectories
Proceedings of the 2008 ACM symposium on Applied computing
Visually driven analysis of movement data by progressive clustering
Information Visualization
Activity-aware map: identifying human daily activity pattern using mobile phone data
HBU'10 Proceedings of the First international conference on Human behavior understanding
Identifying important places in people's lives from cellular network data
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
An event-based conceptual model for context-aware movement analysis
International Journal of Geographical Information Science
The geography of taste: analyzing cell-phone mobility and social events
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
A hybrid model and computing platform for spatio-semantic trajectories
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part I
Semantic trajectories modeling and analysis
ACM Computing Surveys (CSUR)
Visual Analytics of Movement
Thematic Patterns in Georeferenced Tweets through Space-Time Visual Analytics
Computing in Science and Engineering
Hi-index | 0.00 |
Data reflecting movements of people, such as GPS or GSM tracks, can be a source of information about mobility behaviors and activities of people. Such information is required for various kinds of spatial planning in the public and business sectors. Movement data by themselves are semantically poor. Meaningful information can be derived by means of interactive visual analysis performed by a human expert; however, this is only possible for data about a small number of people. We suggest an approach that allows scaling to large datasets reflecting movements of numerous people. It includes extracting stops, clustering them for identifying personal places of interest (POIs), and creating temporal signatures of the POIs characterizing the temporal distribution of the stops with respect to the daily and weekly time cycles and the time line. The analyst can give meanings to selected POIs based on their temporal signatures (i.e., classify them as home, work, etc.), and then POIs with similar signatures can be classified automatically. We demonstrate the possibilities for interactive visual semantic analysis by example of GSM, GPS, and Twitter data. GPS data allow inferring richer semantic information, but temporal signatures alone may be insufficient for interpreting short stops. Twitter data are similar to GSM data but additionally contain message texts, which can help in place interpretation. We plan to develop an intelligent system that learns how to classify personal places and trips while a human analyst visually analyzes and semantically annotates selected subsets of movement data.