Mobile Positioning Dynamics in an Image-Based Hybrid Geocrowdsourcing System



Williams, Toby J

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Geocrowdsourced data (GcD), also known as volunteered geographic information, has proven to be an invaluable resource to the geospatial science community. From a United States National Security perspective, GcD has become a force-multiplier for the Department of Defense aiding in nuclear counterproliferation efforts; at a humanitarian level it was used to aid first responders reaching earthquake victims in Haiti. Despite the potential benefits, research has shown GcD to be unreliable unless moderated with quality assessment methods applied to the data. However, circumstances may prevent moderation and new quality assessment methods must be designed. This research demonstrates a correlation between the number of GcD contributors and the level of positional accuracy of information contributed to the George Mason University Geocrowdsourcing Testbed (GMU-GcT). A mobile-phone, imagebased data contribution tool from the GMU-GcT was developed and distributed to student volunteers at GMU who provided information regarding pre-defined locations on campus. Findings showed that the positional accuracy characteristics of the data contributions to the GMU-GcT improved with added contributors, reaching a level comparable to previously-studied accuracy threshholds reached with a significantly more detailed and heavily moderated data contribution workflow. Undermoderated reports from single contributors averaged 8.55m in positional error. With an increasing number of contributors, positional error of reports for the same item dropped to 3.89m (n=20). The most common positional error threshold for geocrowdsourced data, referred to in previous work as the Haklay distance (approximately 6.0 meters) was reached with two contributors, and after four contributors, the positional error rate stayed fairly constant. This research demonstrated that a fully moderated crowdsourced data contribution process, used in previous incarnations of the GMU-GcT, is unnecessary for producing data with adequate fitness-for-use, including common routing and obstacle avoidance algorithms.



Geocrowdsouring, GMU-GcT, Mobile Positioning, Positional Accuracy, Quality Assessment, VGI