Patent ID: 6975939
Filing Date: 2005-12-13
Classification: G01C,H04W

Abstract:
1. A method of attaining spatial precision in mapping patterns by using data collected from uniquely identified wireless transmissions sessions, comprising: identifying a source of said routinely collected data, wherein said data includes data points describing spatial information, and wherein each said data point is time-tagged, and wherein each said time-tagged data point is uniquely attributable to one said transmission session; selecting pre-specified portions of said data points collected from a pre-specified geographic area, wherein said data points may be available from storage devices; acquiring said pre-specified portions of said data points; estimating the spatial error about each said data point, wherein said ascertaining of said spatial error may depend on the status of a wireless device or the method of obtaining location data on a position of a wireless device at a particular time of transmission; sorting said data points by said unique transmission session identifiers; calculating at least one speed, if any, to be assigned each said transmission session for whom said data points are associated, wherein said speed is calculated by dividing a distance interval between successive said data points by an associated time interval, ΔT, and wherein said speed is assigned to one of a pre-specified range of speeds, and wherein ΔT represents the time of occurrence of a second transmission of a unique transmission session minus the time of occurrence of a first transmission immediately preceding said second transmission of said unique transmission session; sorting said data according to said pre-specified ranges of speed, wherein said sorting differentiates categories of said transmission sessions; converting said representation of said at least one transmission session to weighted cells; adding said weighted cells of said at least one transmission session to at least one aggregation matrix; aggregating said weighted cells based on identifying clusters of said data points, wherein said clusters may have a linear or areal shape, or a combination thereof; converting said cell aggregate to at least one vector representation; sorting said data according to pre-specified time intervals; ascertaining at least one attribute of each said at least one vector representation; and from said at least one attribute of each said at least one vector representation, assigning a most likely attribute, wherein said most likely attributes are used to map a precise pattern.