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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.


Geo context patterns in New York City.(A)Pattern 6 (B) Pattern 8 (C)Pattern 21
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pone.0124819.g006: Geo context patterns in New York City.(A)Pattern 6 (B) Pattern 8 (C)Pattern 21

Mentions: Table 4 presents a few of the patterns estimated and the probability of the top 10 neighborhoods for each pattern reported. To understand the spatial meaning of the patterns, we present few patterns in a map of New York City (see Fig 6). Each pattern reveals the predominant neighborhoods based on the check-in information from the data. One of the major findings from these patterns are the geographic proximity among the neighborhoods in each pattern. Even though we have not used the information on the spatial proximity of the neighborhoods as a priori knowledge, the hidden structure is revealed by the patterns of activity-location choices of the users. This means that individuals prefer nearby locations for different activity purposes. Using the pattern proportions for each user, we can infer his or her local neighborhood. Such information might be crucial to reconstruct a user’s profile.


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

Hasan S, Ukkusuri SV - PLoS ONE (2015)

Geo context patterns in New York City.(A)Pattern 6 (B) Pattern 8 (C)Pattern 21
© Copyright Policy
Related In: Results  -  Collection

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

pone.0124819.g006: Geo context patterns in New York City.(A)Pattern 6 (B) Pattern 8 (C)Pattern 21
Mentions: Table 4 presents a few of the patterns estimated and the probability of the top 10 neighborhoods for each pattern reported. To understand the spatial meaning of the patterns, we present few patterns in a map of New York City (see Fig 6). Each pattern reveals the predominant neighborhoods based on the check-in information from the data. One of the major findings from these patterns are the geographic proximity among the neighborhoods in each pattern. Even though we have not used the information on the spatial proximity of the neighborhoods as a priori knowledge, the hidden structure is revealed by the patterns of activity-location choices of the users. This means that individuals prefer nearby locations for different activity purposes. Using the pattern proportions for each user, we can infer his or her local neighborhood. Such information might be crucial to reconstruct a user’s profile.

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.