Location based social network（LBSN）is prevailing rapidly in China with increase and adoption of smart devices which provide a wide range of opportunities to observe and analyze the human activities behavior towards the use of LBSN.In LBSN,users socialize with each other by sharing their current location（also referred as "geolocation"）in the form of a tweet（also referred as "check-in"）,which contains（but not limited to）text,audio,video etc.,and allows users to disclose the places they visit and a... 

【文章來源】：上海大學(xué)上海市 211工程院校

【文章頁數(shù)】：165 頁

【學(xué)位級別】：博士

【文章目錄】：
Abstract
Chapter 1 Introduction
    1.1 Literature Review
        1.1.1 Check-in Behavior
        1.1.2 Activities Behavior
    1.2 Objectives of the Thesis
    1.3 Thesis and its Substantiation
        1.3.1 Chapters and Contributions
Chapter 2 Big Data, from Volume to Value
    2.1 Introduction
    2.2 Big Data Analytics
    2.3 The Characteristics of Big Data
    2.4 Benefits from Big Data
Chapter 3 We're all Connected: The Rise of the LBSN
    3.1 Introduction
    3.2 Social Media Analytics
    3.3 Overview and History of Location-Based Services （LBS）
    3.4 Overview of Location-Based Social Network（LBSN）
    3.5 The Rise of Location Based Social Network
        3.5.1 The web and online social networks
        3.5.2 Geography and online social networks
        3.5.3 The importance of places for the study of human movement
    3.6 Services of LBSN
    3.7 LBSN Check-in Behavior
        3.7.1 Motivation to Engage in Check-in Behavior
    3.8 LBSN Activities Behavior
Chapter 4 Study Area and Data Source
    4.1 Study Area
    4.2 Data Source
        4.2.1 Check-in Data
            4.2.1.1 Weibo Statistics
        4.2.2 POI Data
    4.3 Activities Behavior Analytics Framework
Chapter 5 Mapping it out: Density Variations and Activities Distribution
    5.1 Introduction
    5.2 Mathematical Formulation
    5.3 Results
        5.3.1 Density Variations and Check-ins Distribution
        5.3.2 Density Variations and Activities Distribution
    5.4 Conclusion
Chapter 6 Spatial Regression Analysis for Modeling Relationships
    6.1 Introduction
    6.2 Mathematical Formulation
    6.3 Results
    6.4 Conclusion
Chapter 7 Spatiotemporal Distribution Patterns of Activities
    7.1 Introduction
    7.2 Mathematical Formulation
        7.2.1 Gravity Centre Analysis
        7.2.2 Standard Deviational Ellipse（SDE） Analysis
    7.3 Results
    7.4 Conclusion
Chapter 8 Concluding Remarks
    8.1 Conclusion
    8.2 Possible Future Work
Reference
Research Outputs and Activities
Acknowledgements


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