Abstract: Georeferenced transaction data is harvested (“crowd-sourced”) from client devices and sent to a network-based map service. The map service performs cluster analysis on location data points in the harvested data, resulting in one or more clusters representing local densities of transaction occurrences. Data vectors including supplemental data are obtained from one or more vendors. Location data points included in the data vectors are compared to center coordinates of the one or more clusters and the closest matching cluster/vector pair provides a mapping to POI data in a POI database. The mapped POI data is updated with the supplemental data. In some implementations, transaction timestamps in the harvested data are used to estimate the business hours of a business POI.

Abstract: This application relates to travel time estimation. The travel time estimation can be performed by a travel time service of a computing device, and based on a calendar entry stored in the computing device. The travel time service can calculate the travel time estimation by using a location associated with a calendar entry and a location of the user prior to a time associated with the calendar entry. The travel time service can use the travel time estimation to alert the user of when to leave for the calendar event and when the user is going to be late for the event. Additionally, when the user is predicted to be late for the calendar event, the user can be prompted to notify the invitees of the calendar event that the user will be late.

Abstract: A method for providing navigation instructions on a device is described. As the device traverses a navigated route according to a first mode of transportation, the method displays a first turn-by-turn navigation presentation defined for the first mode. Based on data gathered by the device, the method determines that the device is navigating the route according to a second mode of transportation. The method automatically displays a second, different turn-by-turn navigation presentation defined for the second mode.

Abstract: Some embodiments provide a mapping application with novel navigation and/or search tools. In some embodiments, the mapping application formulates predictions about future destinations of a device that executes the mapping application, and provides dynamic notifications regarding these predicted destinations. For instance, when a particular destination is a likely destination (e.g., most likely destination) of the device, the mapping application in some embodiments presents a notification regarding the particular destination (e.g., plays an animation that presents the notification). This notification in some embodiments provides some information about (1) the predicted destination (e.g., a name and/or address for the predicted destination) and (2) a route to this predicted destination (e.g., an estimated time of arrival, distance, and/or amount of ETD for the predicted destination).

Abstract: A method for providing navigation instructions on a device is described. As the device traverses a navigated route according to a first mode of transportation, the method displays a first turn-by-turn navigation presentation defined for the first mode. Based on data gathered by the device, the method determines that the device is navigating the route according to a second mode of transportation. The method automatically displays a second, different turn-by-turn navigation presentation defined for the second mode.

Abstract: A method for providing navigation instructions on a device is described. As the device traverses a navigated route according to a first mode of transportation, the method displays a first turn-by-turn navigation presentation defined for the first mode. Based on data gathered by the device, the method determines that the device is navigating the route according to a second mode of transportation. The method automatically displays a second, different turn-by-turn navigation presentation defined for the second mode.

Abstract: For a device that runs a mapping application, a method for providing maneuver indicators along a route of a map. The maneuver indicators are arrows that identify the direction and orientation of a maneuver. A maneuver arrow may be selected and displayed differently from unselected maneuver arrows. Maneuver arrows may be selected automatically based on a user's current location. The mapping application transitions between maneuver arrows and provides an animation for the transition. Complex maneuvers may be indicated by multiple arrows, providing a more detailed guidance for a user of the mapping application.

Abstract: A method of providing a sequence of turn-by-turn navigation instructions on a device traversing a route is provided. Each turn-by-turn navigation instruction is associated with a location on the route. As the device traverses along the route, the method displays a turn-by-turn navigation instruction associated with a current location of the device. The method receives a touch input through a touch input interface of the device while displaying a first turn-by-turn navigation instruction and a first map region that displays the current location and a first location associated with the first turn-by-turn navigation instruction. In response to receiving the touch input, the method displays a second turn-by-turn navigation instruction and a second map region that displays a second location associated with the second turn-by-turn navigation instruction. Without receiving additional input, the method automatically returns to the display of the first turn-by-turn navigation instruction and the first map region.

Abstract: Some embodiments of the invention provide a mobile device with a novel route prediction engine that (1) can formulate predictions about current or future destinations and/or routes to such destinations for the device's user, and (2) can relay information to the user about these predictions. In some embodiments, this engine includes a machine-learning engine that facilitates the formulation of predicted future destinations and/or future routes to destinations based on stored, user-specific data. The user-specific data is different in different embodiments. In some embodiments, the stored, user-specific data includes data about any combination of the following (1) previous destinations traveled to by the user, (2) previous routes taken by the user, (3) locations of calendared events in the user's calendar, (4) locations of events for which the user has electronic tickets, and (5) addresses parsed from recent e-mails and/or messages sent to the user.

Abstract: Some embodiments of the invention provide a novel prediction engine that (1) can formulate predictions about current or future destinations and/or routes to such destinations for a user, and (2) can relay information to the user about these predictions. In some embodiments, this engine includes a machine-learning engine that facilitates the formulation of predicted future destinations and/or future routes to destinations based on stored, user-specific data. The user-specific data is different in different embodiments. In some embodiments, the stored, user-specific data includes data about any combination of the following: (1) previous destinations traveled to by the user, (2) previous routes taken by the user, (3) locations of calendared events in the user's calendar, (4) locations of events for which the user has electronic tickets, and (5) addresses parsed from recent e-mails and/or messages sent to the user.

Abstract: Some embodiments of the invention provide a novel prediction engine that (1) can formulate predictions about current or future destinations and/or routes to such destinations for a user, and (2) can relay information to the user about these predictions. In some embodiments, this engine includes a machine-learning engine that facilitates the formulation of predicted future destinations and/or future routes to destinations based on stored, user-specific data. The user-specific data is different in different embodiments. In some embodiments, the stored, user-specific data includes data about any combination of the following: (1) previous destinations traveled to by the user, (2) previous routes taken by the user, (3) locations of calendared events in the user's calendar, (4) locations of events for which the user has electronic tickets, and (5) addresses parsed from recent e-mails and/or messages sent to the user.