Semantic Place Descriptors for Classification and Map Discovery
This work addresses the challenge of map discovery and classification for visitors in urban environments, though it appears incremental as it applies existing methods to new data.
The authors tackled the problem of representing complex urban structures by using mobile phone traces and geo-tagged Twitter messages to automatically annotate city maps via kernel density estimation, showing that usage information can strongly predict semantic place categories.
Urban environments develop complex, non-obvious structures that are often hard to represent in the form of maps or guides. Finding the right place to go often requires intimate familiarity with the location in question and cannot easily be deduced by visitors. In this work, we exploit large-scale samples of usage information, in the form of mobile phone traces and geo-tagged Twitter messages in order to automatically explore and annotate city maps via kernel density estimation. Our experiments are based on one year's worth of mobile phone activity collected by Nokia's Mobile Data Challenge (MDC). We show that usage information can be a strong predictor of semantic place categories, allowing us to automatically annotate maps based on the behavior of the local user base.