A datatype with increasing importance in GIS is what we call the location history\u2013a record of an entity\u2019s location in geographical space over an interval of time. This paper proposes a number of rigorously defined data structures and algorithms for analyzing and generating location histories. Stays are instances where a subject has spent some time at a single location, and destinations are clusters of stays. Using stays and destinations, we then propose two methods for modeling location histories probabilistically. Experiments show the value of these data structures, as well as the possible applications of probabilistic models of location histories.<\/p>\n","protected":false},"excerpt":{"rendered":"
A datatype with increasing importance in GIS is what we call the location history\u2013a record of an entity\u2019s location in geographical space over an interval of time. This paper proposes a number of rigorously defined data structures and algorithms for analyzing and generating location histories. 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