Mining collective local knowledge from Google MyMaps.

The emerging popularity of location-aware devices and location-based services has generated a growing archive of digital traces of people's activities and opinions in physical space. In this study, we leverage geo-referenced user-generated content from Google MyMaps to discover collective local knowledge and understand the differing perceptions of urban space. Working with the large collection of publicly available, annotation-rich MyMaps data, we propose a highly parallelizable approach in order to merge identical places, discover landmarks, and recommend places. Additionally, we conduct interviews with New York City residents/visitors to validate the quantitative findings.