Location Contexts of User Check-Ins to Model Urban Geo Life-Style Patterns
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 prese...
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| Vydáno v: | PloS one Ročník 10; číslo 5; s. e0124819 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Public Library of Science
13.05.2015
Public Library of Science (PLoS) |
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| ISSN: | 1932-6203, 1932-6203 |
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| 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. |
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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Hasan, Samiul Ukkusuri, Satish V. |
| AuthorAffiliation | 1 Land and Water Flagship, CSIRO, Melbourne, Victoria, Australia Hangzhou Normal University, CHINA 2 School of Civil Engineering, Purdue University, West Lafayette, Indiana, USA |
| AuthorAffiliation_xml | – name: 1 Land and Water Flagship, CSIRO, Melbourne, Victoria, Australia – name: Hangzhou Normal University, CHINA – name: 2 School of Civil Engineering, Purdue University, West Lafayette, Indiana, USA |
| Author_xml | – sequence: 1 givenname: Samiul surname: Hasan fullname: Hasan, Samiul – sequence: 2 givenname: Satish V. surname: Ukkusuri fullname: Ukkusuri, Satish V. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25970430$$D View this record in MEDLINE/PubMed |
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| Copyright | COPYRIGHT 2015 Public Library of Science 2015 Hasan, Ukkusuri. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2015 Hasan, Ukkusuri 2015 Hasan, Ukkusuri |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceived and designed the experiments: SH SVU. Performed the experiments: SH. Analyzed the data: SH. Contributed reagents/materials/analysis tools: SH SVU. Wrote the paper: SH SVU. Competing Interests: The authors have declared that no competing interests exist. |
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| SubjectTerms | Analysis Behavior Choice Behavior Coexistence Computer Simulation Datasets Digital media Friendship Geographic Information Systems Humans International conferences Knowledge management Life Style Location based services Models, Statistical Neighborhoods New York City Participation Place preferences Role models Smartphones Social Media Social networks Social research Sociodemographics Studies Urban Population |
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| Title | Location Contexts of User Check-Ins to Model Urban Geo Life-Style Patterns |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/25970430 https://www.proquest.com/docview/1680707029 https://www.proquest.com/docview/1681262017 https://pubmed.ncbi.nlm.nih.gov/PMC4430213 https://doaj.org/article/244bf029fcba490791cb647c79df79e9 http://dx.doi.org/10.1371/journal.pone.0124819 |
| Volume | 10 |
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