Mining collective local knowledge from Google MyMaps

Authors:
Shaomei Wu;Shenwei Liu;Dan Cosley;Michael Macy
Affiliations:
Cornell University, Ithaca, NY, USA;Cornell University, Ithaca, NY, USA;Cornell University, Ithaca, NY, USA;Cornell University, Ithaca, NY, USA
Venue:
Proceedings of the 20th international conference companion on World wide web
Year:
2011

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Abstract

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.