Human dynamics research in smart and connected communities / Shih-Lung Shaw, Daniel Sui, editors.
This book addresses how accelerating advances in information and communication technology, mobile technology, and location-aware technology have fundamentally changed the ways how social, political, economic and transportation systems work in today's globally connected world. It delivers on man...
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Full Text (via Springer) |
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| Other Authors: | , |
| Format: | eBook |
| Language: | English |
| Published: |
Cham, Switzerland :
Springer,
[2018]
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| Series: | Human dynamics in smart cities.
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| Subjects: |
Table of Contents:
- Intro; Contents; Editors and Contributors; 1 Introduction: Human Dynamics in Perspective; 1.1 Introduction; 1.2 Human Dynamics in Perspective; 1.3 Overview of the Chapters in This Volume; References; 2 Utilizing Geo-located Sensors and Social Media for Studying Population Dynamics and Land Classification; 2.1 Introduction; 2.2 Geo-located Human Activity Data Collection and Management; 2.2.1 Neogeography; 2.2.2 Volunteered Geographic Information; 2.2.3 Social Media; 2.2.3.1 Twitter; 2.2.3.2 Facebook; 2.2.3.3 Limitations of Social Media; 2.2.3.4 Application to Human Dynamics Research.
- 2.3 Improving Land Use Inference by Factorizing Mobile Phone Call Activity Matrix2.3.1 Result and Analysis; 2.3.2 Validation; 2.4 Understanding Special Events Population; 2.4.1 Methods and Results; 2.4.2 Discussion; 2.5 Facility Popularity Assessment from Fine Grained Twitter Analysis; 2.5.1 Basic Description; 2.5.2 Twitter Training Data; 2.5.3 Text Classification Algorithm and Approach; 2.5.3.1 Data Sanitation and Cleaning; 2.5.4 Machine Learning Classification; 2.5.5 Results and Analysis; 2.5.5.1 Training Classifiers; 2.5.5.2 Evaluation Metrics; 2.5.5.3 Results; 2.5.5.4 Accuracy.
- 2.5.5.5 Precision2.6 Summary; Acknowledgements; References; 3 Uncovering the Relationships Between Phone Communication Activities and Spatiotemporal Distribution of Mobile Phone Users; 3.1 Introduction; 3.2 Related Work; 3.3 Study Area and Mobile Phone Data Set; 3.4 Research Design; 3.4.1 Defining Indicators of Aggregate Cellphone Usage; 3.4.2 Deriving the Spatiotemporal Distribution of Mobile Phone Users; 3.4.3 Correlation and Regression Analysis; 3.5 Results and Discussion; 3.5.1 Correlation Between the Number of Phone Users and the Two Cellphone Usage Indicators.
- 3.5.2 Comparison of Regression Models3.5.3 Cross Validation; 3.5.4 Spatiotemporal Patterns of Residuals; 3.6 Conclusion; Acknowledgements; References; 4 Spatio-Temporal-Network Visualization for Exploring Human Movements and Interactions in Physical and Virtual Spaces; 4.1 Introduction; 4.2 An Integrated Spatio-temporal-Network Framework; 4.3 Case Study; 4.4 Vision for Quantitative Analytical Metrics; 4.5 Conclusions; References; 5 Modeling Mobility and Dynamics of Scheduled Space-Time Activitiesâ#x80;#x94;An RDF Approach; 5.1 Introduction; 5.2 Related Work.
- 5.2.1 Geospatial Technologies and Human Mobility5.2.2 Semantic Data Models and Geospatial Research; 5.3 A Semantic Data Model for Space-Time Activity; 5.3.1 Integrate Spatial and Temporal Dimension of Space-Time Activity; 5.3.2 Thematic Role of Participants of Space-Time Activities; 5.3.3 Individual Trajectory and Space-Time Activity; 5.3.4 Scheduled Space-Time Activities; 5.4 Prototype Implementation and a Use Case for Scheduled Movements; 5.5 Retrieving Campus Movement Dynamics from RDF Knowledgebase; 5.5.1 Representing and Visualizing Trajectories for Individual Students.