Mobility patterns, big data and transport analytics : tools and applications for modeling

Front Cover -- Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling -- Copyright -- Dedication -- Contents -- Contributors -- About the Editors -- Chapter 1: Big Data and Transport Analytics: An Introduction -- 1. Introduction -- 2. Book Structure -- Special Acknowledgments -- References -- Further Reading -- Part I: Methodological -- Chapter 2: Machine Learning Fundamentals -- 1. Introduction -- 2. A Little Bit of History -- 3. Deep Neural Networks and Optimization -- 4. Bayesian Models -- 5. Basics of Machine Learning Experiments -- 6. Concluding Remarks -- References -- Further Reading -- Chapter 3: Using Semantic Signatures for Social Sensing in Urban Environments -- 1. Introduction -- 2. Spatial Signatures -- 2.1. Spatial Point Pattern -- 2.2. Spatial Autocorrelations -- 2.3. Spatial Interactions With Other Geographic Features -- 2.4. Place-Based Statistics -- 3. Temporal Signatures -- 4. Thematic Signatures -- 5. Examples -- 5.1. Comparing Place Types -- 5.1.1. Comparison Using Spatial Signatures -- 5.1.2. Comparison Using Temporal Signatures -- 5.2. Coreference Resolution Across Gazetteers -- 5.3. Geoprivacy -- 5.4. Temporally Enhanced Geolocation -- 5.5. Regional Variation -- 5.6. Extraction of Urban Functional Regions -- 6. Summary -- References -- Chapter 4: Geographic Space as a Living Structure for Predicting Human Activities Using Big Data -- 1. Introduction -- 2. Living Structure and the Topological Representation -- 3. Data and Data Processing -- 4. Prediction of Tweet Locations Through Living Structure -- 4.1. Correlations at the Scale of Thiessen Polygons -- 4.2. Correlations at the Scale of Natural Cities -- 4.3. Degrees of Wholeness or Life or Beauty -- 5. Implications on the Topological Representation and Living Structure -- 6. Conclusion -- Acknowledgments -- References.