Abstract: Various embodiments determine relevance of place data by determining whether a place record is relevant based on a set of features associated with the place record. For a given place record, a set of features may be generated based on values of one or more attributes included in the given place record. A given place record may be processed by at least one machine learning model, such as a classifier, which receives as input a set of features of the given place record and outputs a prediction score indicating the certainty or probability that the given place record is associated with, or belongs to, a particular class. The certainty/probability of association between a given place record and a particular class can assist some embodiments in determining (e.g., predicting) whether the given place record is relevant or non-relevant for an intended use, such as a software application for a ride service.

Abstract: Systems and methods for improving accuracy of geographic position data are provided. A networked system mines ticket data from content of the ticket. Based on the ticket data, a determination is made that the ticket indicates an issue with a trip involving a point of interest (POI). The networked system extracts trip data from a trip log corresponding to the trip involving the POI, and identifies, from a data storage, stored attributes of the POI. The networked system analyzes the ticket data, trip data, and attributes to determine a workflow to improve accuracy of the POI, whereby the analyzing comprises determining a priority level to verify accuracy of the POI. The workflow is triggered based on the priority level to verify accuracy of the POI.

Abstract: Various embodiments determine accuracy of place data by determining a context for a place record (that is included in the place data) and determining accuracy of the place record based on a set of criteria associated with the context determined for the place record. For some embodiments, the set of criteria is used in place of, or in conjunction with, another set of fixed criteria for determining accuracy of the place record. Context for the given place record may be determined based on a set of features for the given place record, and the set of features may be generated (e.g., derived or extracted) based on values of one or more attributes (e.g., record fields or fields) included in the given place record. For some embodiments, a quality of a place record is determined based on at least the determination of an accuracy of the place record.

Abstract: Systems and methods for detecting and verifying closed places (e.g., claims no longer in business) from trip data are provided. A networked system accesses trip data associated with the POI. The networked system processes the trip data to generate at least two time buckets based on timestamps from the trip data associated with the POI, and calculates trip counts associated with the POI for each of the time buckets. Using a machine learning algorithm and based on the at least two time buckets, the networked system determines that the trip counts show a decline over time that indicates that the POI is likely closed. In response to the determining, the networked system updates a database to indicate the POI is closed.

Abstract: Systems and methods for improving attribute data for a point of interest (POI) are provided. A networked system accesses trip data associated with the POI. The networked system generates, using a processor-implemented clustering algorithm, a first spatial cluster and a second spatial cluster using coordinates corresponding to the POI indicated in the trip data. A centroid for the first spatial cluster and a centroid for the second spatial cluster are identified by the networked system. The networked system determines that a difference in distance between the centroid for the first spatial cluster and the centroid for the second spatial cluster meets or transgresses a centroid distance threshold. In response to the determining, a database is updated to indicate a new attribute for the POI, the new attribute corresponds to an attribute associated with either the first spatial cluster or the second spatial cluster.

Abstract: An authoritative candidate is selected for determining a location of a point of interest (POI). Source data including name, address, and location for POIs is received from multiple data sources. The received data is normalized for ease of comparison, and coordinates for each candidate are compared to coordinates of other candidates to determine which candidate if any is an authoritative location for the POI. The candidate locations are compared using two models a metric-based scoring system and a machine learning model that may utilize a gradient boosted decision tree. The authoritative candidate can be used to render digital maps that include the POI. In addition, the authoritative candidate's location can be used to provide vehicle route guidance to the POI.

Abstract: An authoritative candidate is selected for determining a location of a point of interest (POI). Source data including name and location for POIs is received from multiple data sources. The received data is normalized for ease of comparison, and coordinates for each candidate are compared to coordinates of other candidates to determine which candidates lie outside of a consensus set of candidates. A consensus set of candidates are those that are each located on a same side of a road segment. Candidates that are not part of the consensus set are eliminated from consideration. Further criteria are then applied to the consensus set of candidates. The authoritative candidate can be used to render digital maps that include the POI. In addition, the authoritative candidate's location can be used to provide vehicle route guidance to the POI.