Abstract
Abstract. Transitory obstacles – random, short-lived and unpredictable objects – are difficult to capture in any traditional mapping system, yet they have significant negative impacts on the accessibility of mobility- and visually-impaired individuals. These transitory obstacles include sidewalk obstructions, construction detours, and poor surface conditions. To identify these obstacles and assist the navigation of mobility- and visually- impaired individuals, crowdsourced mapping applications have been developed to harvest and analyze the volunteered obstacles reports from local students, faculty, staff, and residents. In this paper, we introduce a training program designed and implemented for recruiting and motivating contributors to participate in our geocrowdsourced accessibility system, and explore the quality of geocrowdsourced data with a comparative analysis methodology.
Highlights
Mapping dynamic geographic phenomena is often difficult, due to the requirements for frequent updates and changes that occur over time
The following sections of this paper present an overview of the GMU Geocrowdsourcing Testbed and its moderation and quality assessment program, the training and obstacle characterization studies, a short discussion of user motivations, a summary of positional accuracy characterization, and conclusions and future work
To improve the quality of information contributed to the GMUGcT, Paez (2014) conducted a thorough review of training strategies in map-based social applications and geocrowdsourcing and found the most effective methods of training to be those that were embedded within the data contribution tools, such as those embedded within Google Map Maker and OpenStreetMap
Summary
Mapping dynamic geographic phenomena is often difficult, due to the requirements for frequent updates and changes that occur over time. For individuals with vision or mobility impairments, changes to the pedestrian corridors (even temporary ones) are very difficult, due to the necessity for rerouting, inconvenience, and the increased risks associated with safety hazards Mapping these areas with high-frequency coverage is essential. Rice et al (2014) review the work of Paez (2014) and provide additional insight into the use of training systems and quality assessment of geocrowdsourced data. VGS can be considered as a dynamic version of VGI It is focused more on actions between users who offer or use services. The work presented here uses data contributed by the public (VGI) and enhances the active use of this data through active map-based routing and other services (VGS). The following sections of this paper present an overview of the GMU Geocrowdsourcing Testbed and its moderation and quality assessment program, the training and obstacle characterization studies, a short discussion of user motivations, a summary of positional accuracy characterization, and conclusions and future work
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
