Spatiotemporal Analysis to Observe Activities Behavior and C
发布时间：2021-02-27 18:11
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...
【文章来源】：上海大学上海市 211工程院校
【文章页数】：165 页
【学位级别】：博士
【文章目录】：
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
【参考文献】：
期刊论文
[1]基于多源LBS数据的职住平衡对比研究——以北京城区为例[J]. 赵鹏军,曹毓书. 北京大学学报(自然科学版). 2018(06)
[2]基于居民行为周期特征的城市空间研究[J]. 钟炜菁,王德. 地理科学进展. 2018(08)
[3]基于数字足迹的风景名胜区旅游者时空结构特征研究——以赴张家界景区的旅游者为例[J]. 贺小荣,李宗幸,李启明,陈祖龙,折宇君. 湖南师范大学自然科学学报. 2018(01)
[4]基于微博语义分析的重庆主城区风貌感知评价[J]. 易峥,李继珍,冷炳荣,陈敏. 地理科学进展. 2017(09)
[5]开发区居住空间特征及其形成机制——对北京经济技术开发区的调查[J]. 冯健,项怡之. 地理科学进展. 2017(01)
[6]2000-2010年广州市居住空间结构演变及机制分析[J]. 周春山,罗仁泽,代丹丹. 地理研究. 2015(06)
[7]立足统筹,面向转型的用地规划技术规章——《城市用地分类与规划建设用地标准（GB50137-2011）》阐释[J]. 王凯,张菁,徐泽,徐颖. 城市规划. 2012(04)
本文编号：3054610
【文章来源】：上海大学上海市 211工程院校
【文章页数】：165 页
【学位级别】：博士
【文章目录】：
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
【参考文献】：
期刊论文
[1]基于多源LBS数据的职住平衡对比研究——以北京城区为例[J]. 赵鹏军,曹毓书. 北京大学学报(自然科学版). 2018(06)
[2]基于居民行为周期特征的城市空间研究[J]. 钟炜菁,王德. 地理科学进展. 2018(08)
[3]基于数字足迹的风景名胜区旅游者时空结构特征研究——以赴张家界景区的旅游者为例[J]. 贺小荣,李宗幸,李启明,陈祖龙,折宇君. 湖南师范大学自然科学学报. 2018(01)
[4]基于微博语义分析的重庆主城区风貌感知评价[J]. 易峥,李继珍,冷炳荣,陈敏. 地理科学进展. 2017(09)
[5]开发区居住空间特征及其形成机制——对北京经济技术开发区的调查[J]. 冯健,项怡之. 地理科学进展. 2017(01)
[6]2000-2010年广州市居住空间结构演变及机制分析[J]. 周春山,罗仁泽,代丹丹. 地理研究. 2015(06)
[7]立足统筹,面向转型的用地规划技术规章——《城市用地分类与规划建设用地标准（GB50137-2011）》阐释[J]. 王凯,张菁,徐泽,徐颖. 城市规划. 2012(04)
本文编号：3054610
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