Abstract: Described is a technology by which raw GPS data is processed into segments of a trip, with a predicted mode of transportation (e.g., walking, car, bus, bicycling) determined for each segment. The determined transportation modes may be used to tag the GPS data with transportation mode information, and/or dynamically used. Segments are first characterized as walk segments or non-walk segments based on velocity and/or acceleration. Features corresponding to each of those walk segments or non-walk segments are extracted, and analyzed with an inference model to determine probabilities for the possible modes of transportation for each segment. Post-processing may be used to modify the probabilities based on transitioning considerations with respect to the transportation mode of an adjacent segment. The most probable transportation mode for each segment is selected.

Abstract: Described is a technology by which co-located query patterns are mined from a data space such as a geographic search log. An overall data space (basic) approach and/or a lattice-based approach may be used when mining. The data space contains objects, each comprising associated type and location information. The location information is used to determine the distance between different two or more types of objects, e.g., pairs. The frequency of occurrence of those pairs within the data space determines whether that pairing of object types indicates a co-located pattern. Also described is partitioning the data space into regions, including for the purpose of categorizing a co-located pattern as a local pattern or a global pattern based on how that co-located pattern is distributed among the regions.

Abstract: A system logs life experiences using geographic cues. The system variously provides a comprehensive life-logging tool for recording a plurality of different types of life events. In one implementation, the system receives a user's GPS log files and multimedia content at a website. The system segments the GPS log files into geographic routes corresponding to user trips, and tags the multimedia content with geographic cues from the GPS log files. Then, the system indexes the geographic routes so that users can retrieve the geographic routes by browsing or by search techniques. The system displays animations of selected routes on a map, and displays the multimedia content at corresponding locations along the map route, as the route is replayed. The system also provides browsing and spatial and temporal techniques to search a person's travels and can provide graphical displays of the person's activity statistics.

Abstract: Described is a technology by which raw GPS data is processed into segments of a trip, with a predicted mode of transportation (e.g., walking, car, bus, bicycling) determined for each segment. The determined transportation modes may be used to tag the GPS data with transportation mode information, and/or dynamically used. Segments are first characterized as walk segments or non-walk segments based on velocity and/or acceleration. Features corresponding to each of those walk segments or non-walk segments are extracted, and analyzed with an inference model to determine probabilities for the possible modes of transportation for each segment. Post-processing may be used to modify the probabilities based on transitioning considerations with respect to the transportation mode of an adjacent segment. The most probable transportation mode for each segment is selected.

Abstract: Described is a technology by which uploaded GPS data is indexed according to spatio-temporal relationships to facilitate efficient insertion and retrieval. The indexes may be converted to significantly smaller-sized data structures when new updates to that structure are not likely. GPS data is processed into a track of spatially-partitioned segments such that each segment has a cell. Each cell has an associated temporal index (a compressed start-end tree), into which data for that cell's segments are inserted. The temporal index may include an end time index that relates each segment's end time to a matching start time index. Given query input comprising a spatial predicate and a temporal predicate, tracks may be searched for by determining which spatial candidate cells may contain matching results. For each candidate cell, the search accesses the cell's associated temporal index to find any track or tracks that correspond to the temporal predicate.

Abstract: Described is a technology by which raw GPS data is processed into segments of a trip, with a predicted mode of transportation (e.g., walking, car, bus, bicycling) determined for each segment. The determined transportation modes may be used to tag the GPS data with transportation mode information, and/or dynamically used. Segments are first characterized as walk segments or non-walk segments based on velocity and/or acceleration. Features corresponding to each of those walk segments or non-walk segments are extracted, and analyzed with an inference model to determine probabilities for the possible modes of transportation for each segment. Post-processing may be used to modify the probabilities based on transitioning considerations with respect to the transportation mode of an adjacent segment. The most probable transportation mode for each segment is selected.

Abstract: Described is a technology by which raw GPS data is processed into segments of a trip, with a predicted mode of transportation (e.g., walking, car, bus, bicycling) determined for each segment. The determined transportation modes may be used to tag the GPS data with transportation mode information, and/or dynamically used. Segments are first characterized as walk segments or non-walk segments based on velocity and/or acceleration. Features corresponding to each of those walk segments or non-walk segments are extracted, and analyzed with an inference model to determine probabilities for the possible modes of transportation for each segment. Post-processing may be used to modify the probabilities based on transitioning considerations with respect to the transportation mode of an adjacent segment. The most probable transportation mode for each segment is selected.

Abstract: Described is a technology by which co-located query patterns are mined from a data space such as a geographic search log. An overall data space (basic) approach and/or a lattice-based approach may be used when mining. The data space contains objects, each comprising associated type and location information. The location information is used to determine the distance between different two or more types of objects, e.g., pairs. The frequency of occurrence of those pairs within the data space determines whether that pairing of object types indicates a co-located pattern. Also described is partitioning the data space into regions, including for the purpose of categorizing a co-located pattern as a local pattern or a global pattern based on how that co-located pattern is distributed among the regions.

Abstract: Described is a technology by which uploaded GPS data is indexed according to spatio-temporal relationships to facilitate efficient insertion and retrieval. The indexes may be converted to significantly smaller-sized data structures when new updates to that structure are not likely. GPS data is processed into a track of spatially-partitioned segments such that each segment has a cell. Each cell has an associated temporal index (a compressed start-end tree), into which data for that cell's segments are inserted. The temporal index may include an end time index that relates each segment's end time to a matching start time index. Given query input comprising a spatial predicate and a temporal predicate, tracks may be searched for by determining which spatial candidate cells may contain matching results. For each candidate cell, the search accesses the cell's associated temporal index to find any track or tracks that correspond to the temporal predicate.

Abstract: Described is a technology by which raw GPS data is processed into segments of a trip, with a predicted mode of transportation (e.g., walking, car, bus, bicycling) determined for each segment. The determined transportation modes may be used to tag the GPS data with transportation mode information, and/or dynamically used. Segments are first characterized as walk segments or non-walk segments based on velocity and/or acceleration. Features corresponding to each of those walk segments or non-walk segments are extracted, and analyzed with an inference model to determine probabilities for the possible modes of transportation for each segment. Post-processing may be used to modify the probabilities based on transitioning considerations with respect to the transportation mode of an adjacent segment. The most probable transportation mode for each segment is selected.

Abstract: A system for logging life experiences using geographic cues. The system variously provides a comprehensive life-logging tool for recording each life event; a vacation album for revisiting and reliving vacation routes and associated photos; an information service for finding popular routes and locations; a statistical tool for analyzing metrics of a person's life; and a personal website service for sharing personal information. In one implementation, the system receives a user's GPS log files and multimedia content at a website. The system segments the GPS log files into geographic routes corresponding to user trips, and tags the multimedia content with geographic cues from the GPS log files. Then, the system indexes the geographic routes so that users can retrieve the geographic routes by browsing or by search techniques. The system displays animations of selected routes on a map, and displays the multimedia content at corresponding locations along the map route, as the route is replayed.