Urban Informatics.

• Superior document: The Urban Book Series
• Author: Shi, Wenzhong.
• TeilnehmendeR: Goodchild, Michael F.; Batty, Michael.; Kwan, Mei-Po.; Zhang, Anshu.
• Place / Publishing House: Singapore : : Springer Singapore Pte. Limited,, 2021.; ©2021.
• Year of Publication: 2021
• Edition: 1st ed.
• Language: English
• Series: The Urban Book Series
• Physical Description: 1 online resource (928 pages)

Table of Contents

Intro -- Acknowledgements -- Contents -- About the Editors -- 1 Overall Introduction -- 1.1 Defining Urban Informatics -- 1.2 The Background: The Origins of Urban Informatics -- 1.3 Structure of the Book -- 1.4 Retrospective and Prospective -- References -- Part IDimensions of Urban Science -- 2 Introduction to Urban Science -- 3 Defining Urban Science -- 3.1 A Science of Cities -- 3.2 City Systems and Systems of Cities -- 3.3 Urban Growth: Urbanization from the Bottom Up -- 3.4 Scale and Size, Networks, and Flows -- 3.5 The Development of Operational Urban Models -- 3.6 Future Directions in Urban Informatics -- References -- 4 Street View Imaging for Automated Assessments of Urban Infrastructure and Services -- 4.1 Introduction -- 4.2 Data Collection and Object Localization -- 4.3 Deriving Urban Functions from Object Statistics -- 4.4 Discussion -- References -- 5 Urban Human Dynamics -- 5.1 Introduction -- 5.2 Urban Dynamics -- 5.2.1 Cellular Automata for Urban Dynamics Research -- 5.2.2 Other Urban Dynamics Approaches -- 5.3 Human Dynamics -- 5.3.1 Effects of Information and Communications Technologies on Human Dynamics -- 5.3.2 Time Geography -- 5.3.3 Big Data and Space-Time GIS for Human Dynamics Research -- 5.3.4 Some Other Examples Human Dynamics Studies -- 5.4 Urban Human Dynamics and Urban Informatics -- References -- 6 Geosmartness for Personalized and Sustainable Future Urban Mobility -- 6.1 Introduction -- 6.2 Geosmartness -- 6.3 Analyzing Urban-Mobility Patterns -- 6.3.1 Data -- 6.3.2 Computational Methods for Large-Scale Spatio-temporal Mobility-Pattern Analysis -- 6.3.3 Studies -- 6.3.4 SBB Green Class (Multi-modal and Energy-Efficient Mobility) -- 6.4 Behavioral Change and Sustainable Mobility -- 6.4.1 Motivation -- 6.4.2 Detecting and Supporting Behavioral Change -- 6.4.3 Studies -- 6.4.4 GoEco! -- 6.5 Mobile Decision Making.

6.5.1 Mobile Eye-Tracking and Gaze-Based Interaction -- 6.5.2 Personalized Gaze-Based Decision Support -- 6.6 Conclusions and Future Work -- References -- 7 Urban Metabolism -- 7.1 Introduction -- 7.2 History of Urban Metabolism -- 7.3 Methods of Urban Metabolism -- 7.3.1 Bottom-Up Methods -- 7.3.2 Top-Down Methods -- 7.3.3 Hybrid Methods -- 7.4 A Case Study: The Metabolism of Singapore -- 7.5 Urban Metabolism Applications, Challenges, and Opportunities -- 7.6 Conclusions -- References -- 8 Spatial Economics, Urban Informatics, and Transport Accessibility -- 8.1 Introduction -- 8.2 Intellectual Context -- 8.3 Econometric Models -- 8.3.1 Isotropic Versus Hierarchical Market Linkages for Economic Mass (EM) Computation -- 8.3.2 Control Variables -- 8.3.3 Representing Spatial Spillover Effects -- 8.4 Data -- 8.5 Model Test Results -- 8.6 Discussions -- 8.7 Conclusions -- References -- 9 Conceptualizing the City of the Information Age -- 9.1 Introduction -- 9.1.1 Urban Complexity in the Age of Information and Communication Technologies -- 9.1.2 A Different Kind of City -- 9.1.3 The Smart City -- 9.1.4 Urban Informatics -- 9.2 Urban Research and Planning, Yesterday, and Tomorrow -- 9.2.1 The City as Place -- 9.2.2 The City as Node on a Network -- 9.2.3 Planning the City -- 9.3 Speculations -- 9.3.1 The Robotic Era? -- 9.3.2 The City's Epistemic Planes -- 9.4 Conclusion -- References -- Part IIUrban Systems and Applications -- 10 Introduction to Urban Systems and Applications -- 11 Characterizing Urban Mobility Patterns: A Case Study of Mexico City -- 11.1 Introduction -- 11.2 Data Collection of POIs -- 11.2.1 Parsing Algorithm -- 11.3 Spatial Distribution of POIs -- 11.3.1 Extended Radiation Model for Human Mobility -- 11.3.2 Results -- 11.4 Analyzing Human Mobility by Mode of Transportation -- 11.4.1 Detected Mobility Groups -- 11.5 Conclusions.

References -- 12 Laboratories for Research on Freight Systems and Planning -- 12.1 Introduction -- 12.2 Future Mobility Sensing, a Behavioral Laboratory -- 12.2.1 Background -- 12.2.2 FMS Architecture -- 12.2.3 Applications -- 12.3 SimMobility, a Simulation Laboratory -- 12.3.1 Background -- 12.3.2 SimMobility Architecture -- 12.3.3 Applications -- 12.4 Demonstrations -- 12.4.1 Freight-Vehicle Route-Choice Model -- 12.4.2 Quantification of Model Performance -- 12.4.3 Replication of Specific Freight and Non-Freight-Vehicle Tours -- 12.5 Concluding Remarks -- References -- 13 Urban Risks and Resilience -- 13.1 Introduction -- 13.2 Risks, Exposure, and Vulnerability -- 13.3 Urban Resilience and Capacities -- 13.3.1 The Definitional Quagmire -- 13.3.2 Objects of Analysis -- 13.4 Measurement and Assessment Informatics -- 13.5 Science Informs Practice and Practice Informs Science -- 13.6 Moving Forward -- References -- 14 Urban Crime and Security -- 14.1 Introduction -- 14.2 Urban Crime -- 14.2.1 Historical Roots in Understanding Urban Crime: An Environmental Perspective -- 14.2.2 Theoretical Concepts in Environmental Criminology -- 14.3 Urban Security -- 14.3.1 Fear of Crime in Urban Areas -- 14.3.2 Implementation of Crime Prevention -- 14.4 Latest Tools in Urban Crime Analysis and Security -- 14.4.1 Crime Hotspot Mapping: From Retrospective Analysis to Prediction -- 14.4.2 Advanced Police Patrolling Strategies -- 14.5 Intelligent Data-Driven Policing -- 14.6 Summary -- References -- 15 Urban Governance -- 15.1 Transparency and City Open Data -- 15.1.1 Open Data Platforms -- 15.1.2 Open Data and Accountability -- 15.1.3 Why Are Goals Important? -- 15.1.4 Dashboards and Performance Indicators -- 15.2 Algorithmic Decision-Making -- 15.2.1 Positioning Algorithms -- 15.2.2 Challenges for Operationalizing Algorithms -- 15.3 Conclusion -- References.

16 Urban Pollution -- 16.1 Monitoring Air Quality in Urban Areas -- 16.2 Remote Sensing of the Urban Heat Island -- 16.2.1 Spatial Resolution of Satellite Sensors Related to Scales of Urban Climate -- 16.2.2 Relationship Between Surface Temperature and Air Temperature -- 16.2.3 Time of Imaging in Relation to Heat Island Maximum -- 16.2.4 Anisotropy of the Satellite View -- 16.2.5 The Need for Emissivity and Atmospheric Correction -- 16.3 Monitoring Water Quality Along Urban Coastlines -- References -- 17 Urban Health and Wellbeing -- 17.1 Smart Cities and Health -- 17.2 Data -- 17.2.1 Big Data -- 17.2.2 Individual and Population Data -- 17.2.3 Environmental Data -- 17.3 Methods and Techniques -- 17.4 BERTHA Studies -- 17.4.1 AirGIS -- 17.4.2 Personalized Tracking and Sensing -- 17.4.3 Personalized Air-Pollution Sensors -- 17.4.4 Mental Health -- 17.4.5 Physical Activity -- 17.4.6 Danish Blood-Donor Study -- 17.5 Privacy -- 17.6 Conclusions -- References -- 18 Urban Energy Systems: Research at Oak Ridge National Laboratory -- 18.1 Introduction -- 18.2 Population and Land Use -- 18.2.1 Big Data and GeoAI to Create Population and Land-Use Data -- 18.2.2 Estimating Urban Electricity Use in Data-Poor Regions -- 18.2.3 Estimating Household-Level Energy Consumption -- 18.3 Sustainable Mobility -- 18.3.1 Human Interactions with Transportation Systems -- 18.3.2 Emerging Options for Freight Delivery for Businesses -- 18.4 Energy-Water Nexus -- 18.5 Urban Resiliency -- 18.5.1 Renewable Energy-Infrastructure Assessment -- 18.5.2 Optimizing Energy and Safety Through Precision De-icing -- 18.6 Situational Awareness of National Energy Infrastructure -- 18.7 Conclusion -- References -- Part IIIUrban Sensing -- 19 Introduction to Urban Sensing -- 20 Optical Remote Sensing -- 20.1 Introduction -- 20.2 History of Optical Remote Sensing.

20.3 Latest Developments in Optical Remote Sensing -- 20.3.1 Introduction to Representative Optical Satellite Sensors -- 20.4 Processing of Remote Sensing Satellite Images -- 20.4.1 Image Pre-processing -- 20.4.2 Image Processing -- 20.4.3 Image Post-Processing -- 20.5 Applications of Optical Remote Sensing -- 20.5.1 Land-Use and Land-Cover Mapping -- 20.5.2 Urban Vegetation Phenology -- 20.5.3 Urban Heat Island Mapping -- 20.5.4 Rock Outcrops Identification -- 20.6 Summary -- References -- 21 Urban Sensing with Spaceborne Interferometric Synthetic Aperture Radar -- 21.1 Synthetic Aperture Radar -- 21.2 Interferometric Synthetic Aperture Radar -- 21.3 Multi-temporal InSAR (MTInSAR) -- 21.4 Applications in Urban Areas -- 21.4.1 Construction of Fine Resolution DEM -- 21.4.2 Subsidence Measurement -- 21.4.3 Monitoring Stability of Infrastructures -- 21.5 Summary -- References -- 22 Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics -- 22.1 Introduction -- 22.2 Detection of Urban Objects with ALS and Co-registered Imagery -- 22.2.1 General Strategy -- 22.2.2 Feature Derivation -- 22.2.3 AdaBoost Classification -- 22.3 Detection of Urban Traffic Dynamics with ALS Data -- 22.3.1 Artifacts Effect of Vehicle Motion in ALS Data -- 22.3.2 Detection of Moving Vehicles -- 22.3.3 Concept for Vehicle Velocity Estimation with ALS Data -- 22.4 Experiments and Results -- 22.4.1 Detection of Urban Objects with ALS Data Associated with Aerial Imagery -- 22.4.2 Accuracy Prediction for Vehicle Velocity Estimation Using ALS Aata -- 22.5 Summary -- References -- 23 Photogrammetry for 3D Mapping in Urban Areas -- 23.1 Introduction -- 23.2 Fundamentals of Photogrammetry -- 23.2.1 Image Orientation -- 23.2.2 Bundle Adjustment -- 23.2.3 Image Matching -- 23.3 Advances in Photogrammetry for 3D Mapping in Urban Areas.

23.3.1 Structure from Motion and Multi-view Stereo.