Inventory quantity prediction for geospatial ads with trigger parameters

A computing device for providing an impression metric for locations associated with at least one organization on a map display, the computing device comprising:

• one or more processors;
• and
• a non-transitory computer-readable memory coupled to the one or more processors and storing instructions thereon that, when executed by the one or more processors, cause the computing device to:
• randomly select a set of geographic locations within one or more geographic regions within a geographic area;
• for each geographic location in the set of the geographic locations, determine a number of users displaying map data while meeting impression eligibility requirements for the geographic location based at least in part on location data determined by global positioning system (GPS) sensors in the users' client devices;
• generate a statistical model of impression data based on the number of users displaying map data while meeting the impression eligibility requirements for each geographic location in the set to estimate a number of users who will be displaying map data while meeting the impression eligibility requirements for one or more dispersed geographic locations within a predetermined time interval;
• obtain a request for impression data for displaying map content indicative of one or more locations associated with an organization over a particular time interval;
• apply data associated with the request including the one or more organization locations to the statistical model to estimate an impression metric for the one or more organization locations during the particular time interval, the impression metric indicative of a number of users who will be displaying map data while meeting the impression eligibility requirements for the one or more organization locations;
• provide an indicator of the impression metric for display to a user within the organization;
• and
• update the statistical model based on an additional determination of the number of users displaying map data while meeting the impression eligibility requirements for each geographic location to train the statistical model for subsequent use.

A non-transitory computer-readable medium storing instructions for providing an impression metric that, when executed by one or more processors in a computing device, cause the one or more processors to:

• randomly select a set of geographic locations within one or more geographic regions within a geographic area;
• for each geographic location in the set of the geographic locations, determine a number of users displaying map data while meeting impression eligibility requirements for the geographic location based at least in part on location data determined by global positioning system (GPS) sensors in the users' client devices;
• generate a statistical model of impression data based on the number of users displaying map data while meeting the impression eligibility requirements for each geographic location in the set to estimate a number of users who will be displaying map data while meeting the impression eligibility requirements for one or more dispersed geographic locations within a predetermined time interval;
• obtain a request for impression data for displaying map content indicative of one or more locations associated with an organization over a particular time interval;
• apply data associated with the request including the one or more organization locations to the statistical model to estimate an impression metric for the one or more organization locations during the particular time interval, the impression metric indicative of a number of users who will be displaying map data while meeting the impression eligibility requirements for the one or more organization locations;
• provide an indicator of the impression metric for display to a user within the organization;
• and
• update the statistical model based on an additional determination of the number of users displaying map data while meeting the impression eligibility requirements for each geographic location to train the statistical model for subsequent use.