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Mathematics of Planet Earth : Protecting Our Planet, Learning from the Past, Safeguarding for the Future / Hans G. Kaper, Fred S. Roberts editors
| Mathematics of Planet Earth : Protecting Our Planet, Learning from the Past, Safeguarding for the Future / Hans G. Kaper, Fred S. Roberts editors |
| Pubbl/distr/stampa | Cham, : Springer, 2019 |
| Descrizione fisica | xix, 374 p. : ill. ; 24 cm |
| Soggetto topico |
90Bxx - Operations research and management science [MSC 2020]
60Gxx - Stochastic processes [MSC 2020] 92Bxx - Mathematical biology in general [MSC 2020] 86-XX - Geophysics [MSC 2020] 92Dxx - Genetics and population dynamics [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] 62Pxx - Applications of statistics [MSC 2020] 91Dxx - Mathematical sociology (including anthropology) [MSC 2020] 68T09 - Computational aspects of data analysis and big data [MSC 2020] |
| Soggetto non controllato |
Carbon Cycle
Climate change impacts Data reduction Data-driven modeling Dynamic optimization Earth's Climate System Ecosystem services Glacial cycles Gravitational potential Machine learning Mathematical Ecology Mathematical modeling Risk management Risk-structured model Sea level Urban computing |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0127016 |
| Cham, : Springer, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Mathematics of Planet Earth : Protecting Our Planet, Learning from the Past, Safeguarding for the Future / Hans G. Kaper, Fred S. Roberts editors
| Mathematics of Planet Earth : Protecting Our Planet, Learning from the Past, Safeguarding for the Future / Hans G. Kaper, Fred S. Roberts editors |
| Pubbl/distr/stampa | Cham, : Springer, 2019 |
| Descrizione fisica | xix, 374 p. : ill. ; 24 cm |
| Soggetto topico |
60Gxx - Stochastic processes [MSC 2020]
62Pxx - Applications of statistics [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] 68T09 - Computational aspects of data analysis and big data [MSC 2020] 86-XX - Geophysics [MSC 2020] 90Bxx - Operations research and management science [MSC 2020] 91Dxx - Mathematical sociology (including anthropology) [MSC 2020] 92Bxx - Mathematical biology in general [MSC 2020] 92Dxx - Genetics and population dynamics [MSC 2020] |
| Soggetto non controllato |
Carbon Cycle
Climate change impacts Data reduction Data-driven modeling Dynamic optimization Earth's Climate System Ecosystem services Glacial cycles Gravitational potential Machine learning Mathematical Ecology Mathematical modeling Risk management Risk-structured model Sea level Urban computing |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00127016 |
| Cham, : Springer, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Urban Informatics
| Urban Informatics |
| Autore | Shi Wenzhong |
| Pubbl/distr/stampa | Springer Nature, 2021 |
| Descrizione fisica | 1 online resource (928 pages) |
| Disciplina | 307.76 |
| Altri autori (Persone) |
GoodchildMichael F
BattyMichael KwanMei-Po ZhangAnshu |
| Collana | The Urban Book |
| Soggetto topico |
Human geography
Computer networking & communications Information technology: general issues Geography |
| Soggetto non controllato |
Human Geography
Information Systems and Communication Service Computer Applications Geography, general Urban Geography and Urbanism Database Management System Geographical Information System Urban informatics Urban science GIS Urban computing Sensing Big data Smart cities Spatial data infrastructure Big data analytics Data-driven geography Open access Computer networking & communications Information technology: general issues Geography |
| ISBN | 981-15-8983-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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. |
| Record Nr. | UNINA-9910473454103321 |
Shi Wenzhong
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| Springer Nature, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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