Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices. The delivery of data to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases which is not currently linked to individual mobile devices. Some embodiments allow advertisers to better target their ads to relevant target audience with greater accuracy.

Techniques described herein relate to analyzing location-based transaction systems, based on data from multiple client devices detected and received via multiple communication networks, and providing customized data to client devices associated with particular locations and/or users of the transaction system. The characteristics of a transaction system and/or specific client locations of the transaction system may be determined, based on data received from multiple client devices. A back-end system may receive data from one or more digital kiosk systems associated with particular locations in a transaction system network, various mobile computing devices of users, and client computers within the transaction system. One or more back-end servers may analyze the data to determine various characteristics of the transaction system and/or characteristics of one or more particular locations in the transaction system network.

A system utilizes a plurality of neural networks to assess a score indicating relative risk of whether water supply for a selected parcel of land or other geographic area will be sufficient for water management according to regulatory requirements and/or future intended uses of the land.

Embodiments are described for transforming data relatively homogeneous data segments for training or otherwise configuring automated modeling systems. For example, a computing system selects, from standardized data, geographic data objects associated with a completed transaction value greater than the threshold number. The computing system clusters these geographic data objects and combines completed transactions associated with the selected geographic data objects into segments. The computing system performs a similarity analysis for pairs of the geographic data objects by comparing numerically valued attributes of the geographic data objects. The computing system also selects, from the standardized data, remaining completed transactions that are not grouped into segments and combines, based on the similarity analysis and the threshold number, these property transactions into segments such that each completed transaction has been assigned to one or more of the relatively homogeneous segments. The computing system trains an automated modeling algorithm with the relatively homogeneous segments.

Computer-based systems and methods for discovering neighborhood clusters in a geographic region, where the clusters have a mix of venues and are determined based on venue check-in data. The mix of venues for the clusters may be based on the social similarity between pairs of venues; or emblematic of certain neighborhood typologies; or emblematic of temporal check-in pattern types; or combinations thereof. The neighborhood clusters that are so discovered through venue-check in data could be used for many commercial and civic purposes.

Embodiments provide computer apparatuses, computer systems and computer-executable methods for recommending a commercial item or entity to a consumer based on geographic zone data. The method includes receiving a first predetermined geographic zone, a first importance score associated with a consumer for the first predetermined geographic zone, and a second importance score associated with a commercial item or entity for the first predetermined geographic zone. The method also includes programmatically generating an overlap score based on the first and second importance scores, and programmatically generating a relevancy score based on the overlap score, the relevancy score indicating a probability that the commercial item or entity is of relevance to the consumer. The method further includes, based on the relevancy score, transmitting instructions to a computing device associated with the consumer to cause the computing device to render a representation of the commercial item or entity.

Systems and methods for facilitating effective sales are provided. A database contains location and potential revenue data for a plurality of sales targets. A graphical user interface (GUI) is generated at an electronic display for a mobile device. The GUI includes a map with selectable icons representing sales targets, displayed at their respective locations, within a distance of the location of the mobile device.

A system for ranking geospatial entities is described. In one embodiment, the system comprises an interface for receiving ranking data about a plurality of geospatial entities and an entity ranking module. The module uses a ranking mechanism to generate place ranks for the geospatial entities based on the ranking data. Ranked entity data generated by the entity ranking module is stored in a database. The entity ranking module may be configured to evaluate a plurality of diverse attributes to determine a total score for a geospatial entity. The entity ranking module may be configured to organize ranked entity data into placemark layers.

A computer-implemented method for power grid anomaly detection using a convolutional neural network (CNN) trained to detect anomalies in electricity demand data and electricity supply data includes receiving (i) electricity demand data comprising time series measurements of consumption of electricity by a plurality of consumers, and (ii) electricity supply data comprising time series measurements of availability of electricity by one or more producers. An input matrix is generated that comprises the electricity demand data and the electricity supply data. The CNN is applied to the input matrix to yield a probability of anomaly in the electricity demand data and the electricity supply data. If the probability of anomaly is above a threshold value, an alert message is generated for one or more system operators.

Embodiments of vehicle data systems for use in distributed computer network are disclosed. Particular embodiments may determine and enhance vehicle data from various data sources distributed across the computer network, and utilize the enhanced vehicle data in the determination of normalization metrics that account for geography and population density or spatial behavioral patterns. Embodiments may utilize these normalization metrics to assign zone labels to geographic areas and present representations of the geographic areas based on the normalization metrics across the distributed computer network.