Mapping patterns of movement based on the aggregation of spatial information contained in wireless transmissions
First Claim
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, andwherein each said data point is time-tagged, andwherein 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, Δ
T, andwherein said speed is assigned to one of a pre-specified range of speeds, andwherein Δ
T 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.
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Accused Products
Abstract
Time-tagged coordinates from session-unique transmissions of wireless devices are collected routinely and stored for later analysis. From this data, one may derive a sequence of wireless device operation from which attributes may be ascertained. Sequences are accumulated until a dense aggregate pattern (900) is formed over a geographic area. Aggregate data is sorted into ranges representing speed of movement and then converted to pixels representing cells (401) in an aggregate matrix (400). Heavily weighted values (402) are assigned to cells (401) that represent a location within a pre-specified spatial error (100) about a data point (101). Lower values are assigned to cells (401) representing paths (200), or corridors, connecting these better-identified locations. As more transmission sessions (500) are added to the matrix (400), the largest weight values (402) cluster as individual cells (401) representing a most likely path (1001). Thus precise topographic attributes may be derived based on these spatial clusters (FIG. 11A), overlapping paths connecting them (1001), or combinations (FIG. 15A) thereof.
34 Citations
27 Claims
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1. A method of attaining spatial precision in mapping patterns by using data collected from uniquely identified wireless transmissions sessions, comprising:
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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, Δ
T, andwherein said speed is assigned to one of a pre-specified range of speeds, and wherein Δ
T 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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A method of developing precise topographic maps by using data routinely collected from uniquely identified wireless transmissions sessions, comprising:
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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 uniquely identified wireless 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, Δ
T, andwherein said speed is assigned to one of a pre-specified range of speeds, and wherein Δ
T represents the time of occurrence of a second transmission of said uniquely identified wireless transmission session minus the time of occurrence of a first transmission immediately preceding said second transmission of said uniquely identified wireless transmission session;sorting said data according to said pre-specified ranges of speed, wherein said sorting differentiates categories of said uniquely identified wireless transmission sessions; converting said representation of said at least one uniquely identified wireless transmission session to weighted cells;
adding said weighted cells of said at least one uniquely identified wireless transmission session to said 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 weighted cell aggregate to at least one vector representation;
sorting said data according to at least one pre-specified time interval;
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 a collection of said most likely attributes is used to develop a precise topographic map. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27)
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Specification