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Location contexts of user check-ins to model urban geo life-style patterns.

Hasan S, Ukkusuri SV - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Land and Water Flagship, CSIRO, Melbourne, Victoria, Australia.

ABSTRACT
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.

No MeSH data available.


Components of the proposed approach to enrich the location contexts of user check-ins and use it for modeling geo life-style patterns.
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pone.0124819.g002: Components of the proposed approach to enrich the location contexts of user check-ins and use it for modeling geo life-style patterns.

Mentions: Fig 2 illustrates the components of our modeling approach to infer life-style patterns from the rich semantics of user check-ins. At first user check-ins are collected. The check-ins contain the names and addresses of the activity-locations that the user has visited. In the next stage, additional contextual information is added to the check-in observations. For example, based on the name of a place, its category can be extracted from a database. In this study, we collect the categories of the activity-locations from Foursquare venue category database. Similarly, from the address of a location we can find the neighborhood of the location. In the next stage, location contexts of user check-ins can be modeled to classify user life-style patterns.


Location contexts of user check-ins to model urban geo life-style patterns.

Hasan S, Ukkusuri SV - PLoS ONE (2015)

Components of the proposed approach to enrich the location contexts of user check-ins and use it for modeling geo life-style patterns.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4430213&req=5

pone.0124819.g002: Components of the proposed approach to enrich the location contexts of user check-ins and use it for modeling geo life-style patterns.
Mentions: Fig 2 illustrates the components of our modeling approach to infer life-style patterns from the rich semantics of user check-ins. At first user check-ins are collected. The check-ins contain the names and addresses of the activity-locations that the user has visited. In the next stage, additional contextual information is added to the check-in observations. For example, based on the name of a place, its category can be extracted from a database. In this study, we collect the categories of the activity-locations from Foursquare venue category database. Similarly, from the address of a location we can find the neighborhood of the location. In the next stage, location contexts of user check-ins can be modeled to classify user life-style patterns.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Land and Water Flagship, CSIRO, Melbourne, Victoria, Australia.

ABSTRACT
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.

No MeSH data available.