Abstract: The disclosure relates to a system and method for facilitating geospatial processing and analysis of datasets and providing a visualization of geospatial analytics data over a network. The platform utilizes a discrete global grid system to partition an area (e.g., a map, a city, a state, a country, a continent, the world, etc.) into a set of discrete grid cells. The set of grid cells can be subdivided into finer resolutions of grids. In examples, the platform generates and employs the use of hex tiles for the grid cells.

Abstract: The present application relates to apparatus, systems, and methods for grouping data records based on entities referenced by the data records. The disclosed grouping mechanism can include determining a pair-wise similarity between a large number of data records, and clustering a subset of the data records based on their pair-wise similarity.

Abstract: Examples of the present disclosure describe systems and methods for venue detection. In aspects, a mobile device comprising a set of sensors may collect and store sensor data from in response to a detected movement event or user interaction data. The sensor data may be used to generate a set of candidate venues corresponding to the location of the mobile device. The candidate venues may be provided to a venue detection system. The venue detection system may process the candidate venues to generate a set of features. The set of features may be applied to, and/or used to generate, one or more probabilistic models. The probabilistic models may generate confidence metrics for each of the candidate venues. In some aspects, the top ‘N’ venues may be selected from the set. The top ‘N’ venues may then be presented to the user and/or used to effect one or more actions.

Abstract: Systems and methods are described herein for determining user exposures to content, such as content presented by physical objects (e.g., advertisements on billboards). The systems and methods may determine a probability (an “exposure probability”) that a user has viewed or consumed content, information, or other visual media presented by a physical object, such as a billboard, vehicle, sign, or other structure, and provide the determined probability to various attribution systems, such as systems that attribute user engagements (e.g., store visits, app downloads, website visits, product purchases, and so on) to earlier content exposures.

Abstract: The present disclosure relates to apparatus, systems, and methods for providing a location information analytics mechanism. The location information analytics mechanism is configured to analyze location information to extract contextual information (e.g., profile) about a mobile device or a user of a mobile device, collectively referred to as a target entity. The location information analytics mechanism can include analyzing location data points associated with a target entity to determine features associated with the target entity, and using the features to predict attributes associated with the target entity. The set of predicted attributes can form a profile of the target entity.

Abstract: A system and method for attributing in-store visits to exposure to advertisement (“ad”) impressions associated with an ad campaign are disclosed. The system gathers impression data and uses that data to identify users who were exposed to the ad impressions. The system then uses location data, activity information and in some instances beacon data points reported by mobile devices of the impression users to determine if the impression users visited a target place during a conversion window. Based on the impression users who were exposed to the ad impressions, the system establishes a control group of users who were not exposed to the ad impressions to perform quasi-experimental analyses to assess whether the ad impressions had any impact on changing the impression users' physical in-store visitation behavior.

Abstract: A targeting system based on a predictive targeting model based on observed behavioral data including visit data, user profile and/or survey data, and geographic features associated with a geographic region. The predictive targeting model analyzes the observed behavioral data and the geographic features data to predict conversion rates for every cell in a square grid of predefined size on the geographic region. The conversion rate of a cell indicates a likelihood that any random user in that cell will perform a targeted behavior.

Abstract: The present disclosure relates to apparatus, systems, and methods for analyzing characteristics of entities of interest. In particular, the present disclosure provides a mechanism for analyzing information about entities of interest and for rating or scoring the entities of interest based on the analyzed information. The rating or the score of an entity of interest can sometimes be referred to as a placerank value of an entity of interest.

Abstract: The disclosed apparatus, systems, and methods relate to a location query mechanism that can efficiently determine whether a target entity is located within a region of interest (ROI). At a high level, the location query mechanism can be configured to represent a ROI using one or more polygons. The location query mechanism can, in turn, divide (e.g., Jesse Hate) the one or more polygons into sub-polygons. Subsequently, the location query mechanism can use the sub-polygons to build an index system that can efficiently determine whether a particular location is within any of the sub-polygons. Therefore, when a computing device queries whether a particular location is within the region of interest, the location query mechanism can use the index system to determine whether the particular location is within any of the sub-polygons.

Abstract: In non-limiting examples, passive information is used to verify the location of a client computing device and to create segment categories for directed information using location-based services and/or applications. In aspects, passive information is received from a client computing device. The client computing device is detected as being located at a predicted venue using the passive information. It may be determined whether directed information associated with the predicted venue has been displayed at an earlier time on a predetermined portion of the client computing device. In response to determining directed information associated with the predicted venue has been displayed on the predetermined portion of the client computing device, a statistical model may be applied to calculate a confidence score that the client computing device is located at the predicted venue. When the confidence score meets a predetermined threshold, a directed information conversion record may be stored in a data store.

Abstract: Non-limiting examples of the present disclosure describe analysis of venue data and prediction of trendiness of venues based analyzing the venue data. As an example, one or more new venues are determined. The one or more new venues are determined by identification of a venue that has venue data existing for a period of time less than or equal to a predetermined time threshold. The venue data associated with the one or more new venues is evaluated. A predicted popularity for the one or more new venues is generated based on evaluation of the venue data. The generated predicted popularity may be provided to a processing device. In some examples, a ranked list of the one or more new venues is generated. The ranked list may display the one or more venues in a ranked order according to the generated predicted popularity. Other examples are also described.

Abstract: In non-limiting examples of the present disclosure, taste data is generated and usable within one or more applications. A taste is one or more elements that describe an entity. Information of an application may be processed to extract entity data that corresponds to a plurality of candidates to be designated as tastes (taste data). The candidates for the tastes are curated. In examples, the curating comprises filtering the candidates for tastes to remove extracted candidates. Extraction rules for managing structured taste data may be applied in the filtering. A status of a remaining candidate may be determined as approved or rejected based on processing of received user feedback. Taste data may be generated for an approved candidate. The generating of the taste data may comprise assigning parameters that include a descriptor type and a recommendation type. Taste data may be used within an application.

Abstract: In non-limiting examples, passive information is used to verify the location of a client computing device and to create segment categories for directed information using location-based services and/or applications. In aspects, passive information is received from a client computing device. The client computing device is detected as being located at a predicted venue using the passive information. It may be determined whether directed information associated with the predicted venue has been displayed at an earlier time on a predetermined portion of the client computing device. In response to determining directed information associated with the predicted venue has been displayed on the predetermined portion of the client computing device, a statistical model may be applied to calculate a confidence score that the client computing device is located at the predicted venue. When the confidence score meets a predetermined threshold, a directed information conversion record may be stored in a data store.

Abstract: In non-limiting examples, wireless scan data collected from a processing device is directly mapped to venues. Wireless scan data is received from a processing device where the wireless scan data comprises at least a first identifier of a wireless connection. Venues associated with the first identifier are identified from a mapping of wireless identifiers to venues based on active validation data associated with particular venues including historical wireless scan data of the particular venues. The plurality of venues are ranked according to a likelihood that the processing device is located at a particular venue based on evaluating the active validation data including the historical wireless scan data from the mapping. A predicted venue that the processing device is located at is determined based on the ranking of the plurality of venues. Other examples are also described.

Abstract: A mobile application is provided that provides intelligent recommendations based on the knowledge of where the user has been, and what venues the user would like to visit. Further, such an application may be capable of determining where people in a user's social network have been and what venue locations these related users would like to visit. Also, in another implementation, the application may be capable of determining where people with similar taste have been, and where they would like to go. Some or all of this information may be used by a mobile application that provides recommendations to a user. For instance, in one implementation, a user having a mobile device such as a cell phone wishes to locate a venue based on one or more parameters, and some or all of this information may be used to order to rank recommendations with the interface.

Abstract: A mobile application is provided that provides intelligent recommendations based on the knowledge of where the user has been, and what venues the user would like to visit. Further, such an application may be capable of determining where people in a user's social network have been and what venue locations these related users would like to visit. Also, in another implementation, the application may be capable of determining where people with similar tastes have been, and where they would like to go. Some or all of this information may be used by a mobile application that provides recommendations to a user. For instance, in one implementation, a user having a mobile device such as a cell phone wishes to locate a venue based on one or more parameters, and some or all of this information may be used to order or rank recommendations within the interface.

Abstract: A mobile application is provided that provides intelligent recommendations based on the knowledge of where the user has been, and what venues the user would like to visit. Further, such an application may be capable of determining where people in a user's social network have been and what venue locations these related users would like to visit. Also, in another implementation, the application may be capable of determining where people with similar taste have been, and where they would like to go. Some or all of this information may be used by a mobile application that provides recommendations to a user. For instance, in one implementation, a user having a mobile device such as a cell phone wishes to locate a venue based on one or more parameters, and some or all of this information may be used to order to rank recommendations with the interface.

Abstract: A mobile application is provided that provides intelligent recommendations based on the knowledge of where the user has been, and what venues the user would like to visit. Further, such an application may be capable of determining where people in a user's social network have been and what venue locations these related users would like to visit. Also, in another implementation, the application may be capable of determining where people with similar tastes have been, and where they would like to go. Some or all of this information may be used by a mobile application that provides recommendations to a user. For instance, in one implementation, a user having a mobile device such as a cell phone wishes to locate a venue based on one or more parameters, and some or all of this information may be used to order or rank recommendations within the interface.

Abstract: A mobile application is provided that provides intelligent recommendations based on the knowledge of where the user has been, and what venues the user would like to visit. Further, such an application may be capable of determining where people in a user's social network have been and what venue locations these related users would like to visit. Also, in another implementation, the application may be capable of determining where people with similar tastes have been, and where they would like to go. Some or all of this information may be used by a mobile application that provides recommendations to a user. For instance, in one implementation, a user having a mobile device such as a cell phone wishes to locate a venue based on one or more parameters, and some or all of this information may be used to order or rank recommendations within the interface.