Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
From GPS traces to a routable road map
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Road network reconstruction for organizing paths
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Detecting road intersections from GPS traces
GIScience'10 Proceedings of the 6th international conference on Geographic information science
Metric graph reconstruction from noisy data
Proceedings of the twenty-seventh annual symposium on Computational geometry
Incremental clustering for trajectories
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Constructing street networks from GPS trajectories
ESA'12 Proceedings of the 20th Annual European conference on Algorithms
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Analyzing and mining geo-referenced trajectory data has different aspects to researchers from different communities. For example, animal location data provides ecologists live points of contact between ecologies and the species. And by studying movements of individual animals, they have gained insight into population distributions, important resources, dispersal settings, social interaction or general patterns of how the space was used in an ecological system. Similarly, geologists and environmentalists use earthquake positional data for predicting the location of the next earthquake. Intelligent Transportation Systems and GIS communities use heuristic algorithms on vehicle trajectory data sets to construct or update digital street-maps that represent the data set. Recently, the Computational Geometry community started to give attention to the street-map construction problems as well, applying different approaches and providing quality guarantees. Although different communities use different types or aspects of the GPS data, they face one challenge in common: how to model or incorporate the impreciseness of the input data in their output. In this paper we discuss specifically the impact of spatial inaccuracy of GPS trajectory data on street-map reconstruction algorithms. In particular, we discuss approaches and challenges to associate that impreciseness with the reconstructed street-intersections.