Crowd-Sourced Location Verification via Moderation and Feedback
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Solution Overview
Problem
Existing systems for obtaining crowd-sourced location information often lack data for specific categories of points of interest, such as restrooms and ATMs, and may contain unverified or inaccurate information, which hampers their utility in map services and other applications.
Innovation Solution
A system and method that utilize mobile user devices to collect and verify location tags, including categories and device locations, by determining proximity, user feedback, and moderation to create and maintain a points of interest layer, incorporating image recognition and reputation management to ensure data accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If crowd-sourced location information is collected from mobile user devices, then the quantity and diversity of points of interest data is improved, but the accuracy and reliability of the data deteriorates due to unverified or incorrect information
Solution Approach 1:
The system implements feedback mechanisms where users can provide feedback on location tags (marking them as incorrect), and moderators can review and verify location information. This feedback loop allows the system to learn from user experiences and correct inaccuracies, thereby improving the reliability of crowd-sourced location data while maintaining the quantity of data collected.
Solution Approach 2:
The system enables users to self-verify location information by allowing them to mark location tags as incorrect, and provides automated verification processes where the system itself can confirm the validity of location data. This self-service approach empowers users to maintain data quality while continuing to contribute large quantities of location information.
2Reliability
If location tags are verified through moderation and user feedback, then the reliability of location information is improved, but the time required to process and verify data increases
Solution Approach 1:
The system applies partial verification by automatically verifying location tags that meet certain criteria (such as multiple consistent reports or high confidence scores) without requiring full manual moderation. This partial action approach allows the system to quickly verify a portion of the data while reserving full moderation for only those location tags that require human review, thereby reducing overall verification time while maintaining reliability.
Solution Approach 2:
The system performs preliminary verification actions by automatically filtering and pre-processing location tags before they reach the moderation queue. Automated systems can initially assess the validity of location data based on predefined rules and user reputation scores, preparing only the most questionable entries for human moderator review. This preliminary action significantly reduces the time required for complete verification.
3Quantity of substance
If the system stores and processes large amounts of location tag data, then the completeness of points of interest information is improved, but the device complexity and processing requirements increase
Solution Approach 1:
The system segments the location data processing into distinct modules: data collection from mobile devices, automated verification processing, manual moderation processing, and data storage. This segmentation allows each component to handle specific tasks independently, making the overall system more manageable and less complex while still processing large volumes of location data to maintain completeness.
Solution Approach 2:
The system introduces intermediary processing layers between data collection and final storage, including automated verification systems and moderation queues. These intermediaries act as buffers that process and filter data before it enters the final database, reducing the immediate processing burden on the main system while still ensuring data completeness through multi-stage validation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy and completeness of crowd-sourced location information, providing reliable data for map services and other applications while incentivizing users for contributions and maintaining data quality through reputation systems.
Implementation Method 1
determining that a distance between the first device location and the second device location is less than a distance threshold
Implementation Method 2
performing optical character recognition on the location image to obtain location information associated with the first point of interest
Data Source
AI summary
Systems, methods, computer programs, and user interfaces are provided to receive location tags from a plurality of user devices (a location tag including a location category and a location corresponding to a geographic location of a user device from which the location tag is received), identify a first set of location tags comprising a given location category, determine whether a cluster location exists for locations of the set of location tags, generate a moderation request for a location tag of the set of location tags in response to determining that a cluster location does not exists for locations of the set of location tags, receive moderator approval of the location tag, and store a geographic location corresponding to the location tag as a point of interest associated with the given location category in response to receiving moderator approval of the location tag.