Spatial variation in search engine queries
Proceedings of the 17th international conference on World Wide Web
Analysis of geographic queries in a search engine log
Proceedings of the first international workshop on Location and the web
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
On the "localness" of user-generated content
Proceedings of the 2010 ACM conference on Computer supported cooperative work
Find me if you can: improving geographical prediction with social and spatial proximity
Proceedings of the 19th international conference on World wide web
Distance matters: geo-social metrics for online social networks
WOSN'10 Proceedings of the 3rd conference on Online social networks
Location recommendation for location-based social networks
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Exploiting geographical influence for collaborative point-of-interest recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A geographic study of tie strength in social media
Proceedings of the 20th ACM international conference on Information and knowledge management
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Understanding the spatial preference of mobile and web users is of great significance to creating and improving location-based recommendation systems, travel planners, search engines, and other emerging mobile applications. However, traditional sources of spatial preference -- which reflect the patterns of geo-spatial interest of large numbers of users -- have typically been expensive to collect, proprietary, and unavailable for widespread use. In this paper, we investigate the viability of new publicly-available geospatial information to capture spatial preference. Concretely, we compare a set of 35 million publicly shared check-ins voluntarily generated by users of a popular location sharing service with a set of over 400 million private query logs recorded by a commercial hotel search engine. Although generated by users with fundamentally different intentions, we find common conclusions may be drawn from both data sources -- (i) that the relative geo-spatial "footprint" of different locations is surprisingly consistent across both; (ii) that methods to identify significant locations results in similar conclusions; and (iii) that similar performance may be achieved for automatically identifying groups of related locations. These results indicate the viability of publicly shared location information to complement (and replace, in some cases), privately held location information.