Location Contexts of User Check-Ins to Model Urban Geo Life-Style Patterns

May Location Contexts of User Check-Ins to Model Urban Geo Life-Style Patterns Samiul Hasan 0 1 Satish V. Ukkusuri 0 1 0 1 Land and Water Flagship, CSIRO , Melbourne, Victoria , Australia , 2 School of Civil Engineering, Purdue University , West Lafayette, Indiana , USA 1 Academic Editor: Zi-Ke Zhang, Hangzhou Normal University , CHINA Geo-location data from social media offers us information, in new ways, to understand people's attitudes and interests through their activity choices. In this paper, we explore the idea of inferring individual life-style patterns from activity-location choices revealed in social media. We present a model to understand life-style patterns using the contextual information (e. g. location categories) of user check-ins. Probabilistic topic models are developed to infer individual geo life-style patterns from two perspectives: i) to characterize the patterns of user interests to different types of places and ii) to characterize the patterns of user visits to different neighborhoods. The method is applied to a dataset of Foursquare check-ins of the users from New York City. The co-existence of several location contexts and the corresponding probabilities in a given pattern provide useful information about user interests and choices. It is found that geo life-style patterns have similar items-either nearby neighborhoods or similar location categories. The semantic and geographic proximity of the items in a pattern reflects the hidden regularity in user preferences and location choice behavior. - Funding: This work was funded by the National Science Foundation Project No. 1017933 Dynamic Flow Equilibria in Vehicular Traffic and Data The recent introduction of the location-based services in smartphone-based social media applications allows people to share their activity related choices in their virtual social networks (e.g., Facebook Places, Foursquare). Such sharing occurs at the level of specific geo-location and time of where and when an individual participates in an activity. This vast amount of geo-location data offers us, in new ways, peoples attitudes and interests through their activity-location choices over a large number of users and over multiple months/years that was unimaginable before. In addition to location and timing, this data reveals our interests towards specific brands or types of locations for different activity purposes (e.g., Walmart, Target, Whole foods for grocery shopping). From this new information, we can understand human mobility behavior in a better way. We can infer users life-style choices from their activity-location choice patterns. However, while this new data is available, there are currently limited methodologies to infer life-style choices to characterize individual activity patterns important for understanding behavior. In this paper, using activity participation data shared in social media, we develop Communication Networks, for which the authors are grateful. However, the authors are solely responsible for the contents of the research work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. probabilistic models inferring individual interests to activity-locations and thereby obtain the life-style patterns of people living in an urban area. The linkage between life-style and activity participation has been well recognized in activity-based travel behavior analysis [14] in the domain of transportation science. Individuals daily activity and travel patterns with longer-term choices of residential location, work place location and vehicle ownership are inter-related with each other and jointly define their lifestyle patterns [5]. Hence the concept of life-style choice provides a useful framework to study urban human activity and travel behaviors. However, in the literature, there is no general consensus on a definition of life-style based on individual activity patterns. Life-style definitions vary from conceptual to operational ones. Most definitions belong to one of the two broader perspectives: a) life-style as behavioral patterns such as activity and time use patterns and b) life-style as a behavioral orientation [4]. The former approach views life-style as changing as an individual adapts to the surrounding environment while the later approach views life-style as an orientation which the individual wants to maintain by changing his or her actions. Changes in individual values, attitudes, and preferences may affect, in the long term, the individuals life-style as orientation. Empirical studies have found evidence of the linkages between individual life-style choices and activity-travel behaviors. These behaviors include short-term choices, such as activity types and frequencies, travel distances, mode of travel [57], and more long-term choices, such as where to live and work [5] and what type of car to buy [8]. As location-based datasets from social media are becoming increasingly available, there is potential to characterize and infer user preferences from these datasets. Researchers have already been using these datasets to gather interesting insights on different aspects related to human mobility and activity choices. Studies from social science, computer science, and transportation science have used innovative ways to extract meaningful patterns with diverse applications. These studies include activity recognition [9], discovering mobility and activity choice behavior [1016], classifying activity choice patterns [17], estimating urban travel demand and traffic flow [1820], predicting next place to check-in [21], modeling the influence of friendship on mobility patterns [22], and detecting neighborhood boundaries [23]. With large sample size covering a long period (e.g., for a year) and providing locations and timings of individual activity participation, location-based datasets have further potential for activity-based modeling. However, a major challenge of using these datasets for activity behavior modeling is that individuals are recognized by only the identification numbers without any detailed information on socio-economic characteristics (e.g., income, age, race etc.). These attributes of the social media users are difficult to obtain. Without the socio-demographic attributes of the users, it is difficult to explain or to gain deeper insights from the observed patterns. We need to cluster the population based on a set of generic characteristics and correlate patterns with those characteristics providing a behavioral underpinning to different patterns observed from social media data. In this paper, we introduce the life-style concept giving a richer medium to understand the higher dimensions of activity patterns observed in social media. We develop a clustering approach based on probabilistic topic models [24] to understand individ (...truncated)