TECHNICAL FIELD

This disclosure relates generally to processing of sensor data, and, in particular, to inference of logistical relationships from device location data.

BACKGROUND

The ability to infer logistical relationships between areas of interest may be a valuable resource in many scenarios. For example, determining who the intermediaries are in a supply chain may assist a company in determining areas of risk, such as areas where disruption may halt production for the company. As another example, determining intermediaries in the supply chain may assist the company in identifying upstream suppliers who may be conducting business in an unsustainable manner.

Multiple methods may be used to determine direct and/or indirect relationships between suppliers and customers in a supply chain. A simple approach may simply be to gather input from suppliers along the chain. For example, a company may ask its suppliers for data via a software system. However, this approach has many drawbacks, such as relying on suppliers to input any data and relying on suppliers that do input data to input accurate data. Additionally, there can exist a long delay in receiving data from suppliers and high costs due to the dependence on manual workflows.

Existing technology is insufficient to automate determination of these relationships, because this technology does not enable users to define areas of interest. Thus, available data, such as mobile device location of users, is not able to be parsed using existing tools to provide granular enough information to determine these relationships.

SUMMARY

While sensor data, such as mobile device location data is tracked in the aggregate, the data is not broken down in a way that provides meaningful activity data for micro-locations. For example, the aggregate data is not useful in helping to identify relationships between different user-defined locations (e.g., a manufacturer and a supplier). Technical improvements are disclosed herein, including new uses of clustering algorithms that enable aggregate data input into a system to be applied to user-defined AOIs to find related AOIs. Disclosed by way of example embodiments are a method, system and/or computer program products (e.g., a non-transitory computer-readable storage media that stores instructions executable by one or more processors) for combining sensor data from multiple devices to determine suppliers and/or customers in a supply chain and to quantify a logistical relationship between any two in the supply chain without any input from the individual suppliers or the individual customers. The sensor data comprises geolocation data records provided by location enabled devices (e.g., mobile devices) to a mobile device location data provider. Each geolocation data record includes at least coordinates and a timestamp at which the coordinates are recorded. A plurality of geolocation data records for a plurality of mobile devices are received by an AOI logistical relationship analyzer system. The AOI logistical relationship analyzer system may also receive a request to track data of an area of interest (AOI). The AOI logistical relationship analyzer system determines which mobile devices of the plurality visited the AOI based on the geolocation data records for each mobile device. In one example, a mobile device is determined to have visited the AOI when two or more consecutive coordinates of the device's geolocation data records intersect with an AOI geographic area. For each mobile device determined to have visited the AOI, the AOI logistical relationship analyzer system determines a path of the mobile device. The path includes a prior location of the mobile device and a next location of the mobile device. The AOI logistical relationship analyzer system determines a path by gathering a subset of the geolocation records of the mobile device with timestamps taking place during a time interval. The time interval includes an amount of time before the visit to the AOI, during the visit to the AOI, and after the visit to the AOI.

After the path for each mobile device determined to have visited the AOI is determined, the AOI logistical relationship analyzer system determines which geolocation data records of the path are stationary geolocation data records. Stationary geolocation data records are determined by analyzing a velocity of each geolocation data record. Records with a velocity of substantially zero are considered stationary. The AOI logistical relationship analyzer system clusters the stationary geolocation data records into one or more groups. Each group includes geolocation data records with coordinates within a threshold distance of each other. The AOI logistical relationship analyzer system determines a geographic area for each group. Each geographic area may be identified as an additional unique AOI. The additional unique AOI(s) may be classified as a source AOI or a destination AOI based on the timestamps of the stationary geolocation data records. The AOI logistical relationship analyzer system may aggregate the one or more groups of stationary geolocation data records and the geographic areas to determine a number of mobile devices that visited each geographic area. The number of mobile devices that visited any two geographic areas may be used by the AOI logistical relationship analyzer system to quantify the logistical relationship between the two.

In one example embodiment, the AOI logistical relationship analyzer system receives a request to track data of an area of interest (AOI). The AOI logistical relationship analyzer system further receives a plurality of geolocation data records corresponding to a plurality of mobile devices, each geolocation data record including coordinates and a timestamp at which the coordinates are recorded. The AOI logistical relationship analyzer system further determines which mobile devices of the plurality of mobile devices have visited the AOI. For each mobile device determined to have visited the AOI, the AOI logistical relationship analyzer system determines a path of the mobile device. The path includes a prior location of the mobile device and a next location of the mobile device. The AOI logistical relationship analyzer system further transmits for display a report including the path for each mobile device determined to have visited the AOI. The path indicates an identification of additional AOIs based on the prior location and the next location.

