Modeling Moving Objects over Multiple Granularities
Annals of Mathematics and Artificial Intelligence
Analyzing Relative Motion within Groups of Trackable Moving Point Objects
GIScience '02 Proceedings of the Second International Conference on Geographic Information Science
Dynamic collectives and their collective dynamics
COSIT'05 Proceedings of the 2005 international conference on Spatial Information Theory
A qualitative trajectory calculus and the composition of its relations
GeoS'05 Proceedings of the First international conference on GeoSpatial Semantics
Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes
GIScience '08 Proceedings of the 5th international conference on Geographic Information Science
Detecting single file movement
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Pervasive and Mobile Computing
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Points are dimensionless geometric elements often employed by geographic information systems to model a range of real-world objects such as people and vehicles. Many analytical tools have been developed for finding and validating certain patterns of static points such as clustering. In this paper, we present our preliminary efforts to develop statistical methods for analyzing patterns of moving points whose locations are tracked at equal intervals of time. Focus is placed on simple descriptive statistics—rather than more sophisticated inferential statistics—useful for detecting a tendency of two or more points to move in some coordinated fashion. A major implication of this paper is that statistical analysis complements spatial data query and modeling with respect to dynamics of sets of point-like objects in a way that potentially interrelated subsets are screened.