Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Space-time density of trajectories: exploring spatio-temporal patterns in movement data
International Journal of Geographical Information Science - Geospatial Visual Analytics: Focus on Time Special Issue of the ICA Commission on GeoVisualization
Unveiling the complexity of human mobility by querying and mining massive trajectory data
The VLDB Journal — The International Journal on Very Large Data Bases
Ranking continuous nearest neighbors for uncertain trajectories
The VLDB Journal — The International Journal on Very Large Data Bases
Visualization of vessel movements
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
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Trajectories are used to represent objects' movement and spatio-temporal aggregation of trajectories is commonly used in knowledge discovery. Based on a general workflow to extract knowledge, we identify relevant factors that propagate uncertainty in moving object datasets when using aggregation. We use a probabilistic approach and propose a theoretical model to represent positional uncertainty of trajectories and their aggregation, and implement a prototype system to compute positional uncertainty in pedestrians' movement data.