Modeling nature: cellular automata simulations with Mathematica
Modeling nature: cellular automata simulations with Mathematica
Updating and Querying Databases that Track Mobile Units
Distributed and Parallel Databases - Special issue on mobile data management and applications
Modeling Moving Objects over Multiple Granularities
Annals of Mathematics and Artificial Intelligence
Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
Continuous probabilistic nearest-neighbor queries for uncertain trajectories
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Robust traffic merging strategies for sensor-enabled cars using time geography
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Probabilistic nearest-neighbor query on uncertain objects
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Map-based spatio-temporal interpolation in vehicle trajectory data using routing web-services
Proceedings of the 5th ACM SIGSPATIAL International Workshop on Computational Transportation Science
An opportunistic client user interface to support centralized ride share planning
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Time geography uses space---time volumes to represent the possible locations of a mobile agent over time in a x---y---t space. A volume is a qualitative representation of the fact that the agent is at a particular time t i inside of the volume's base at t i . Space---time volumes enable qualitative analysis such as potential encounters between agents. In this paper the qualitative statements of time geography will be quantified. For this purpose an agent's possible locations are modeled from a stochastic perspective. It is shown that probability is not equally distributed in a space---time volume, i.e., a quantitative analysis cannot be based simply on proportions of intersections. The actual probability distribution depends on the degree of a priori knowledge about the agent's behavior. This paper starts with the standard assumption of time geography (no further knowledge), and develops the appropriate probability distribution by three equivalent approaches. With such a model any analysis of the location of an agent, or relations between the locations of two agents, can be improved in expressiveness as well as accuracy.