Inferring Urban Land Use Using Large-Scale Social Media Check-in Data

Emerging location-based services in social media tools such as Foursquare and Twitter are providing an unprecedented amount of public-generated data on human movements and activities. This novel data source contains valuable information (e.g., geo-location, time and date, type of places) on human activities. While the data is tremendously beneficial in modeling human activity patterns, it is also greatly useful in inferring planning related variables such as a city's land use characteristics. This paper provides a comprehensive investigation on the possibility and validity of utilizing large-scale social media check-in data to infer land use types by applying the state-of-art data mining techniques. Two inference approaches are proposed and tested in this paper: the unsupervised clustering method and supervised learning method. The land use inference is conducted in a uniform grid level of 200 by 200 m. The methods are applied to a case study of New York City. The validation result confirms that the two approaches effectively infer different land use types given sufficient check-in data. The encouraging result demonstrates the potential of using social media check-in data in urban land use inference, and also reveals the hidden linkage between the human activity pattern and the underlying urban land use pattern.