Data Analytics for Smart Cities.
Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- 1: Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition Assessment -- 1.1 Introduction -- 1.2 Smartphone-Driven Assessment of Airport Pave...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Alavi, Amir [verfasserIn] Buttlar, William G. [mitwirkender] |
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| Format: |
E-Book |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
Milton: Auerbach Publications ; 2018 ©2019 |
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| Schlagwörter: |
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| Anmerkung: |
Description based on publisher supplied metadata and other sources |
|---|---|
| Umfang: |
1 online resource (255 pages) |
| Reihe: |
Data Analytics Applications Ser. |
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| Links: | |
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| ISBN: |
978-0-429-78663-1 |
| Katalog-ID: |
1039849717 |
|---|
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| 245 | 1 | 0 | |a Data Analytics for Smart Cities. |
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| 264 | 4 | |c ©2019 | |
| 300 | |a 1 online resource (255 pages) | ||
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| 490 | 0 | |a Data Analytics Applications Ser. | |
| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 520 | |a Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- 1: Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition Assessment -- 1.1 Introduction -- 1.2 Smartphone-Driven Assessment of Airport Pavement Condition -- 1.2.1 Description of Smartphone Application -- 1.2.2 Smartphone Characteristics -- 1.3 Case Study of Missouri Airports -- 1.3.1 Calibration Study -- 1.3.2 Missouri Airport Smartphone Data Collection Methodology -- 1.3.3 Missouri Airport Smartphone Data Collection Results for Each Airport -- 1.3.4 Discussion -- 1.4 Prediction of PCI Based on Smartphone-Measured IRI -- 1.4.1 Machine Learning Method -- 1.4.2 GEP-Based Formulation of PCI -- 1.5 Conclusions -- Acknowledgments -- References -- 2: Global Satellite Observations for Smart Cities -- 2.1 Introduction -- 2.2 Overview of NASA Satellite-Based Global Data Products for Smart Cities -- 2.2.1 Satellite-Based Data Products at the GES DISC -- 2.2.1.1 Multi-Satellite and Multi-Sensor Merged Global Precipitation Products -- 2.2.1.2 Global and Regional Land Data Assimilation Products -- 2.2.1.3 Modern-Era Retrospective Analysis for Research and Applications (MERRA) Products -- 2.3 Data Services -- 2.3.1 Point-and-Click Online Tools -- 2.3.1.1 NASA's Worldview -- 2.3.1.2 NASA GES DISC Giovanni -- 2.3.2 Data Rod Services -- 2.3.3 Other Web Data Services -- 2.4 Examples -- 2.4.1 The Pearl River Delta -- 2.4.1.1 Typhoon Nida Rainfall -- 2.4.1.2 Atmospheric Composition Preliminary Analysis -- 2.4.2 Estimation of Hurricane Contribution to Annual Precipitation in Maryland -- 2.4.2.1 Data and Methods -- 2.4.2.2 Preliminary Results -- 2.5 Summary and Future Plans -- Acknowledgments -- References -- 3: Advancing Smart and Resilient Cities with Big Spatial Disaster Data -- 3.1 Introduction | ||
| 520 | |a 3.2 The Role of Spatial Data in Coastal Resilience Applications -- 3.2.1 Disaster Management Cycle -- 3.2.2 Data Acquisition -- 3.2.3 Challenges and Opportunities -- 3.3 A Hurricane Sandy Inspired Big Data Framework for Coastal Resilience Investigations with Heterogeneous Spatial Data -- 3.3.1 Geospatial Response to Hurricane Sandy -- 3.3.2 Data Analytic Framework -- 3.3.3 Anatomy of Big Spatial Disaster Data -- 3.3.3.1 Volume -- 3.3.3.2 Data Structure -- 3.3.3.3 Spatial Completeness -- 3.3.3.4 Veracity -- 3.3.3.5 Velocity -- 3.3.4 Decomposition of Processing Tasks -- 3.3.4.1 Digital Elevation Models -- 3.3.4.2 Feature Extraction -- 3.3.4.3 Change Detection -- 3.3.4.4 Core Operation Categories -- 3.3.5 Identify the Uncertainty Associated with Big Data Acquisition and Processing -- 3.3.6 Computing with Big Data Infrastructure -- 3.3.7 Connecting Data Processing with Decision-Making Models -- 3.3.8 Future Improvement -- 3.4 Conclusion -- References -- 4: Smart City Portrayal -- 4.1 Introduction -- 4.2 Background and Related Work -- 4.2.1 Point Representation -- 4.2.2 Geographic Generalization -- 4.2.3 Heatmap -- 4.2.4 Circular Plot -- 4.2.5 Schematic Map -- 4.3 Point Representation: DESIGN STUDY -- 4.3.1 Concept and Formalization -- 4.3.2 Color-Coding -- 4.3.3 Implementation -- 4.3.4 Graphical Error -- 4.4 Map Transformations -- 4.4.1 Map Morphing -- 4.4.1.1 Metro Map Format -- 4.4.1.2 Station to Station Transition -- 4.4.1.3 Connection to Connection Transition -- 4.4.2 Splitting Overlapped Segments -- 4.4.3 Reactive Circle Scaling -- 4.5 Interactive Application -- 4.5.1 General Overview -- 4.5.2 Circular Diagram -- 4.6 Critical Discussion -- 4.7 Conclusions -- Acknowledgments -- References -- 5: Smart Bike-Sharing Systems for Smart Cities -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Bike Availability Prediction | ||
| 520 | |a 5.2.2 Using Clustering to Explore Data Trends -- 5.2.3 Rebalancing -- 5.3 Data Set -- 5.4 Bike Prediction Models and Results -- 5.4.1 Univariate Models -- 5.4.2 Multivariate Models -- 5.5 Supervised Clustering -- 5.5.1 Proposed Algorithm -- 5.5.2 Model Order Selection -- 5.5.3 Day of the Week -- 5.5.4 Hour of the Day -- 5.6 Rebalancing -- 5.6.1 Problem Statement -- 5.7 Proposed Algorithm -- 5.7.1 Tour Construction Using the Deferred Acceptance Algorithm -- 5.7.2 Tour Improvement Using 2-Opt Local Search Algorithm -- 5.7.3 Tour Construction Example -- 5.8 Results -- 5.8.1 San Francesco Bay Area Instances -- 5.9 Conclusions -- Acknowledgments -- References -- 6: Indirect Monitoring of Critical Transport Infrastructure: Data Analytics and Signal Processing -- 6.1 Introduction -- 6.2 Indirect Monitoring of Transport Infrastructure -- 6.3 Road Pavement Applications -- 6.3.1 Foundation Quality -- 6.3.2 Road Cracking and Surface Roughness -- 6.3.3 Pavement Stiffness -- 6.4 Railway Track Applications -- 6.5 Bridge Applications -- 6.6 Vehicle Management -- 6.7 Data Analytics -- 6.7.1 Internet of Things -- 6.7.2 Data Analytics Challenges -- 6.8 Conclusion -- Acknowledgment -- References -- 7: Big Data Exploration to Examine Aggressive Driving Behavior in the Era of Smart Cities -- 7.1 Introduction -- 7.2 Data Description -- 7.3 Methodology -- 7.3.1 Data Partitioning -- 7.3.2 Data Extraction -- 7.3.3 Knowledge Discovery -- 7.3.3.1 K-Means Method -- 7.3.3.2 Scenario Development -- 7.3.3.3 Variable Selection -- 7.4 Results -- 7.4.1 Cluster Determination -- 7.4.2 Hot Spot Identification -- 7.4.3 Impact of Trip Travel Time -- 7.5 Conclusions -- References -- 8: Exploratory Analysis of Run-Off-Road Crash Patterns -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Method and Data -- 8.3.1 Multiple Correspondence Analysis -- 8.3.1.1 Cloud of Individuals | ||
| 520 | |a 8.3.1.2 Cloud of Categories -- 8.3.2 Data Collection -- 8.4 Results and Discussions -- 8.5 Conclusion -- References -- 9: Predicting Traffic Safety Risk Factors Using an Ensemble Classifier -- 9.1 Introduction -- 9.2 Problem Statement -- 9.3 Method -- 9.4 Classification of Event Data -- 9.4.1 Multinomial Logistic Regression (MLR) -- 9.4.2 Random Forest (RF) -- 9.4.3 Method Selection -- 9.4.4 Multivariate Time Series Random Forest (MTS-RF) -- 9.5 Data -- 9.6 Results and Discussions -- 9.7 Conclusions and Recommendations -- 9.8 Practical Applications -- Disclaimer -- References -- 10: Architecture Design of Internet of Things-Enabled Cloud Platform for Managing the Production of Prefabricated Public Houses -- 10.1 Introduction -- 10.1.1 Housing Concerns in Shenzhen and Advantages of Prefabricated Public Houses -- 10.1.2 Existing Technical Challenges -- 10.1.3 Internet of Things-Enabled Cloud Platform -- 10.2 Research Background -- 10.2.1 Massive Production of Prefabricated Public Houses in Shenzhen, Advantages of Prefabrication and Policies to Promote Prefabrication -- 10.2.2 Technical Challenges of Managing Massive Production of Prefabricated Public Houses -- 10.3 Architecture Design for Internet of Things-Enabled Cloud Platform -- 10.3.1 Processes and Function Requirements -- 10.3.2 Service-Oriented Architecture Design -- 10.3.3 Gateway Operation System and Quick Response Code for Defining Intelligent Building Elements -- 10.3.4 Beidou and GIS Integrated Technologies for Positioning Intelligent Building Elements -- 10.3.5 Dynamic nD BIM for Visualizing the Whole Processes of Prefabrication Construction Processes -- 10.3.6 Data Source Management Service through Big Data Analytics Systems -- 10.4 Benefits to Stakeholders Involved in Massive Production of Prefabricated Public Houses -- 10.4.1 Benefits and Marketing Opportunities for Clients | ||
| 520 | |a 10.4.2 Benefits and Marketing Opportunities for Contractors -- 10.4.3 Benefits and Marketing Opportunities for Prefabrication Manufacturers -- 10.4.4 Benefits and Marketing Opportunities for Third-Party Logistics Firms -- 10.4.5 Benefits and Marketing Opportunities for IT Vendors -- 10.5 Discussion and Future Research -- References -- Index | ||
| 650 | 4 | |a Big data | |
| 650 | 4 | |a Quantitative research | |
| 650 | 4 | |a Smart cities | |
| 650 | 4 | |a Electronic books | |
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9780429786631 : electronic bk. 978-0-429-78663-1 9781138308770 (DE-627)1039849717 (DE-576)520223527 (DE-599)GBV1039849717 (EBP)038198258 (EBL)EBL5566778 (EBR)ebr11626386 (EBC)EBC5566778 DE-627 eng DE-627 rda eng 307.760285 Alavi, Amir verfasserin aut Data Analytics for Smart Cities. Milton Auerbach Publications 2018 ©2019 1 online resource (255 pages) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data Analytics Applications Ser. Description based on publisher supplied metadata and other sources Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- 1: Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition Assessment -- 1.1 Introduction -- 1.2 Smartphone-Driven Assessment of Airport Pavement Condition -- 1.2.1 Description of Smartphone Application -- 1.2.2 Smartphone Characteristics -- 1.3 Case Study of Missouri Airports -- 1.3.1 Calibration Study -- 1.3.2 Missouri Airport Smartphone Data Collection Methodology -- 1.3.3 Missouri Airport Smartphone Data Collection Results for Each Airport -- 1.3.4 Discussion -- 1.4 Prediction of PCI Based on Smartphone-Measured IRI -- 1.4.1 Machine Learning Method -- 1.4.2 GEP-Based Formulation of PCI -- 1.5 Conclusions -- Acknowledgments -- References -- 2: Global Satellite Observations for Smart Cities -- 2.1 Introduction -- 2.2 Overview of NASA Satellite-Based Global Data Products for Smart Cities -- 2.2.1 Satellite-Based Data Products at the GES DISC -- 2.2.1.1 Multi-Satellite and Multi-Sensor Merged Global Precipitation Products -- 2.2.1.2 Global and Regional Land Data Assimilation Products -- 2.2.1.3 Modern-Era Retrospective Analysis for Research and Applications (MERRA) Products -- 2.3 Data Services -- 2.3.1 Point-and-Click Online Tools -- 2.3.1.1 NASA's Worldview -- 2.3.1.2 NASA GES DISC Giovanni -- 2.3.2 Data Rod Services -- 2.3.3 Other Web Data Services -- 2.4 Examples -- 2.4.1 The Pearl River Delta -- 2.4.1.1 Typhoon Nida Rainfall -- 2.4.1.2 Atmospheric Composition Preliminary Analysis -- 2.4.2 Estimation of Hurricane Contribution to Annual Precipitation in Maryland -- 2.4.2.1 Data and Methods -- 2.4.2.2 Preliminary Results -- 2.5 Summary and Future Plans -- Acknowledgments -- References -- 3: Advancing Smart and Resilient Cities with Big Spatial Disaster Data -- 3.1 Introduction 3.2 The Role of Spatial Data in Coastal Resilience Applications -- 3.2.1 Disaster Management Cycle -- 3.2.2 Data Acquisition -- 3.2.3 Challenges and Opportunities -- 3.3 A Hurricane Sandy Inspired Big Data Framework for Coastal Resilience Investigations with Heterogeneous Spatial Data -- 3.3.1 Geospatial Response to Hurricane Sandy -- 3.3.2 Data Analytic Framework -- 3.3.3 Anatomy of Big Spatial Disaster Data -- 3.3.3.1 Volume -- 3.3.3.2 Data Structure -- 3.3.3.3 Spatial Completeness -- 3.3.3.4 Veracity -- 3.3.3.5 Velocity -- 3.3.4 Decomposition of Processing Tasks -- 3.3.4.1 Digital Elevation Models -- 3.3.4.2 Feature Extraction -- 3.3.4.3 Change Detection -- 3.3.4.4 Core Operation Categories -- 3.3.5 Identify the Uncertainty Associated with Big Data Acquisition and Processing -- 3.3.6 Computing with Big Data Infrastructure -- 3.3.7 Connecting Data Processing with Decision-Making Models -- 3.3.8 Future Improvement -- 3.4 Conclusion -- References -- 4: Smart City Portrayal -- 4.1 Introduction -- 4.2 Background and Related Work -- 4.2.1 Point Representation -- 4.2.2 Geographic Generalization -- 4.2.3 Heatmap -- 4.2.4 Circular Plot -- 4.2.5 Schematic Map -- 4.3 Point Representation: DESIGN STUDY -- 4.3.1 Concept and Formalization -- 4.3.2 Color-Coding -- 4.3.3 Implementation -- 4.3.4 Graphical Error -- 4.4 Map Transformations -- 4.4.1 Map Morphing -- 4.4.1.1 Metro Map Format -- 4.4.1.2 Station to Station Transition -- 4.4.1.3 Connection to Connection Transition -- 4.4.2 Splitting Overlapped Segments -- 4.4.3 Reactive Circle Scaling -- 4.5 Interactive Application -- 4.5.1 General Overview -- 4.5.2 Circular Diagram -- 4.6 Critical Discussion -- 4.7 Conclusions -- Acknowledgments -- References -- 5: Smart Bike-Sharing Systems for Smart Cities -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Bike Availability Prediction 5.2.2 Using Clustering to Explore Data Trends -- 5.2.3 Rebalancing -- 5.3 Data Set -- 5.4 Bike Prediction Models and Results -- 5.4.1 Univariate Models -- 5.4.2 Multivariate Models -- 5.5 Supervised Clustering -- 5.5.1 Proposed Algorithm -- 5.5.2 Model Order Selection -- 5.5.3 Day of the Week -- 5.5.4 Hour of the Day -- 5.6 Rebalancing -- 5.6.1 Problem Statement -- 5.7 Proposed Algorithm -- 5.7.1 Tour Construction Using the Deferred Acceptance Algorithm -- 5.7.2 Tour Improvement Using 2-Opt Local Search Algorithm -- 5.7.3 Tour Construction Example -- 5.8 Results -- 5.8.1 San Francesco Bay Area Instances -- 5.9 Conclusions -- Acknowledgments -- References -- 6: Indirect Monitoring of Critical Transport Infrastructure: Data Analytics and Signal Processing -- 6.1 Introduction -- 6.2 Indirect Monitoring of Transport Infrastructure -- 6.3 Road Pavement Applications -- 6.3.1 Foundation Quality -- 6.3.2 Road Cracking and Surface Roughness -- 6.3.3 Pavement Stiffness -- 6.4 Railway Track Applications -- 6.5 Bridge Applications -- 6.6 Vehicle Management -- 6.7 Data Analytics -- 6.7.1 Internet of Things -- 6.7.2 Data Analytics Challenges -- 6.8 Conclusion -- Acknowledgment -- References -- 7: Big Data Exploration to Examine Aggressive Driving Behavior in the Era of Smart Cities -- 7.1 Introduction -- 7.2 Data Description -- 7.3 Methodology -- 7.3.1 Data Partitioning -- 7.3.2 Data Extraction -- 7.3.3 Knowledge Discovery -- 7.3.3.1 K-Means Method -- 7.3.3.2 Scenario Development -- 7.3.3.3 Variable Selection -- 7.4 Results -- 7.4.1 Cluster Determination -- 7.4.2 Hot Spot Identification -- 7.4.3 Impact of Trip Travel Time -- 7.5 Conclusions -- References -- 8: Exploratory Analysis of Run-Off-Road Crash Patterns -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Method and Data -- 8.3.1 Multiple Correspondence Analysis -- 8.3.1.1 Cloud of Individuals 8.3.1.2 Cloud of Categories -- 8.3.2 Data Collection -- 8.4 Results and Discussions -- 8.5 Conclusion -- References -- 9: Predicting Traffic Safety Risk Factors Using an Ensemble Classifier -- 9.1 Introduction -- 9.2 Problem Statement -- 9.3 Method -- 9.4 Classification of Event Data -- 9.4.1 Multinomial Logistic Regression (MLR) -- 9.4.2 Random Forest (RF) -- 9.4.3 Method Selection -- 9.4.4 Multivariate Time Series Random Forest (MTS-RF) -- 9.5 Data -- 9.6 Results and Discussions -- 9.7 Conclusions and Recommendations -- 9.8 Practical Applications -- Disclaimer -- References -- 10: Architecture Design of Internet of Things-Enabled Cloud Platform for Managing the Production of Prefabricated Public Houses -- 10.1 Introduction -- 10.1.1 Housing Concerns in Shenzhen and Advantages of Prefabricated Public Houses -- 10.1.2 Existing Technical Challenges -- 10.1.3 Internet of Things-Enabled Cloud Platform -- 10.2 Research Background -- 10.2.1 Massive Production of Prefabricated Public Houses in Shenzhen, Advantages of Prefabrication and Policies to Promote Prefabrication -- 10.2.2 Technical Challenges of Managing Massive Production of Prefabricated Public Houses -- 10.3 Architecture Design for Internet of Things-Enabled Cloud Platform -- 10.3.1 Processes and Function Requirements -- 10.3.2 Service-Oriented Architecture Design -- 10.3.3 Gateway Operation System and Quick Response Code for Defining Intelligent Building Elements -- 10.3.4 Beidou and GIS Integrated Technologies for Positioning Intelligent Building Elements -- 10.3.5 Dynamic nD BIM for Visualizing the Whole Processes of Prefabrication Construction Processes -- 10.3.6 Data Source Management Service through Big Data Analytics Systems -- 10.4 Benefits to Stakeholders Involved in Massive Production of Prefabricated Public Houses -- 10.4.1 Benefits and Marketing Opportunities for Clients 10.4.2 Benefits and Marketing Opportunities for Contractors -- 10.4.3 Benefits and Marketing Opportunities for Prefabrication Manufacturers -- 10.4.4 Benefits and Marketing Opportunities for Third-Party Logistics Firms -- 10.4.5 Benefits and Marketing Opportunities for IT Vendors -- 10.5 Discussion and Future Research -- References -- Index Big data Quantitative research Smart cities Electronic books Buttlar, William G. mitwirkender (DE-588)1161009493 (DE-627)1024416461 (DE-576)506300730 ctb 9781138308770 Erscheint auch als Druck-Ausgabe 9781138308770 https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=5566778 X:EBC Verlag lizenzpflichtig Volltext https://www.gbv.de/dms/bowker/toc/9781138308770.pdf DE-601 pdf/application 2020-01-19 Aggregator Inhaltsverzeichnis ZDB-30-PQE GBV_ILN_30 ISIL_DE-104 SYSFLAG_1 GBV_KXP GBV_ILN_206 ISIL_DE-Brg3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2021 ISIL_DE-289 SWB 4170rda BO 045F 307.760285 30 01 0104 1831714302 EBL-UBCL Campusweiter Zugriff. - Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 11-12-18 206 01 3350 1831602504 00 --%%-- Online-Ressource g --%%-- OLR-EBL If you are a ThHF affiliate and the E-Book is not fully accessible, please send us a purchase or short time loan request. All others: Inter-library loans and guest access on campus premises is not possible. zh 10-12-18 370 01 4370 3976600172 olr-dda ebc Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 09-09-21 2021 01 DE-289 3844332650 00 --%%-- --%%-- --%%-- n l01 28-01-21 30 01 0104 https://ebookcentral.proquest.com/lib/tuclausthal-ebooks/detail.action?docID=5566778 206 01 3350 Full Text only for ThHF affiliates https://thh-friedensau.idm.oclc.org/login?url=http://ebookcentral.proquest.com/lib/thhfriedensau/detail.action?docID=5566778 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von auβerhalb nur für HCU-Angehörige möglich https://ebookcentral.proquest.com/lib/hcuhamburg-ebooks/detail.action?docID=5566778 2021 01 DE-289 https://ebookcentral.proquest.com/lib/kiz-uniulm/detail.action?docID=5566778 30 01 0104 EBL-UBCL 206 01 3350 OLR-EBL 370 01 4370 olr-dda ebc |
| spelling |
9780429786631 : electronic bk. 978-0-429-78663-1 9781138308770 (DE-627)1039849717 (DE-576)520223527 (DE-599)GBV1039849717 (EBP)038198258 (EBL)EBL5566778 (EBR)ebr11626386 (EBC)EBC5566778 DE-627 eng DE-627 rda eng 307.760285 Alavi, Amir verfasserin aut Data Analytics for Smart Cities. Milton Auerbach Publications 2018 ©2019 1 online resource (255 pages) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data Analytics Applications Ser. Description based on publisher supplied metadata and other sources Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- 1: Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition Assessment -- 1.1 Introduction -- 1.2 Smartphone-Driven Assessment of Airport Pavement Condition -- 1.2.1 Description of Smartphone Application -- 1.2.2 Smartphone Characteristics -- 1.3 Case Study of Missouri Airports -- 1.3.1 Calibration Study -- 1.3.2 Missouri Airport Smartphone Data Collection Methodology -- 1.3.3 Missouri Airport Smartphone Data Collection Results for Each Airport -- 1.3.4 Discussion -- 1.4 Prediction of PCI Based on Smartphone-Measured IRI -- 1.4.1 Machine Learning Method -- 1.4.2 GEP-Based Formulation of PCI -- 1.5 Conclusions -- Acknowledgments -- References -- 2: Global Satellite Observations for Smart Cities -- 2.1 Introduction -- 2.2 Overview of NASA Satellite-Based Global Data Products for Smart Cities -- 2.2.1 Satellite-Based Data Products at the GES DISC -- 2.2.1.1 Multi-Satellite and Multi-Sensor Merged Global Precipitation Products -- 2.2.1.2 Global and Regional Land Data Assimilation Products -- 2.2.1.3 Modern-Era Retrospective Analysis for Research and Applications (MERRA) Products -- 2.3 Data Services -- 2.3.1 Point-and-Click Online Tools -- 2.3.1.1 NASA's Worldview -- 2.3.1.2 NASA GES DISC Giovanni -- 2.3.2 Data Rod Services -- 2.3.3 Other Web Data Services -- 2.4 Examples -- 2.4.1 The Pearl River Delta -- 2.4.1.1 Typhoon Nida Rainfall -- 2.4.1.2 Atmospheric Composition Preliminary Analysis -- 2.4.2 Estimation of Hurricane Contribution to Annual Precipitation in Maryland -- 2.4.2.1 Data and Methods -- 2.4.2.2 Preliminary Results -- 2.5 Summary and Future Plans -- Acknowledgments -- References -- 3: Advancing Smart and Resilient Cities with Big Spatial Disaster Data -- 3.1 Introduction 3.2 The Role of Spatial Data in Coastal Resilience Applications -- 3.2.1 Disaster Management Cycle -- 3.2.2 Data Acquisition -- 3.2.3 Challenges and Opportunities -- 3.3 A Hurricane Sandy Inspired Big Data Framework for Coastal Resilience Investigations with Heterogeneous Spatial Data -- 3.3.1 Geospatial Response to Hurricane Sandy -- 3.3.2 Data Analytic Framework -- 3.3.3 Anatomy of Big Spatial Disaster Data -- 3.3.3.1 Volume -- 3.3.3.2 Data Structure -- 3.3.3.3 Spatial Completeness -- 3.3.3.4 Veracity -- 3.3.3.5 Velocity -- 3.3.4 Decomposition of Processing Tasks -- 3.3.4.1 Digital Elevation Models -- 3.3.4.2 Feature Extraction -- 3.3.4.3 Change Detection -- 3.3.4.4 Core Operation Categories -- 3.3.5 Identify the Uncertainty Associated with Big Data Acquisition and Processing -- 3.3.6 Computing with Big Data Infrastructure -- 3.3.7 Connecting Data Processing with Decision-Making Models -- 3.3.8 Future Improvement -- 3.4 Conclusion -- References -- 4: Smart City Portrayal -- 4.1 Introduction -- 4.2 Background and Related Work -- 4.2.1 Point Representation -- 4.2.2 Geographic Generalization -- 4.2.3 Heatmap -- 4.2.4 Circular Plot -- 4.2.5 Schematic Map -- 4.3 Point Representation: DESIGN STUDY -- 4.3.1 Concept and Formalization -- 4.3.2 Color-Coding -- 4.3.3 Implementation -- 4.3.4 Graphical Error -- 4.4 Map Transformations -- 4.4.1 Map Morphing -- 4.4.1.1 Metro Map Format -- 4.4.1.2 Station to Station Transition -- 4.4.1.3 Connection to Connection Transition -- 4.4.2 Splitting Overlapped Segments -- 4.4.3 Reactive Circle Scaling -- 4.5 Interactive Application -- 4.5.1 General Overview -- 4.5.2 Circular Diagram -- 4.6 Critical Discussion -- 4.7 Conclusions -- Acknowledgments -- References -- 5: Smart Bike-Sharing Systems for Smart Cities -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Bike Availability Prediction 5.2.2 Using Clustering to Explore Data Trends -- 5.2.3 Rebalancing -- 5.3 Data Set -- 5.4 Bike Prediction Models and Results -- 5.4.1 Univariate Models -- 5.4.2 Multivariate Models -- 5.5 Supervised Clustering -- 5.5.1 Proposed Algorithm -- 5.5.2 Model Order Selection -- 5.5.3 Day of the Week -- 5.5.4 Hour of the Day -- 5.6 Rebalancing -- 5.6.1 Problem Statement -- 5.7 Proposed Algorithm -- 5.7.1 Tour Construction Using the Deferred Acceptance Algorithm -- 5.7.2 Tour Improvement Using 2-Opt Local Search Algorithm -- 5.7.3 Tour Construction Example -- 5.8 Results -- 5.8.1 San Francesco Bay Area Instances -- 5.9 Conclusions -- Acknowledgments -- References -- 6: Indirect Monitoring of Critical Transport Infrastructure: Data Analytics and Signal Processing -- 6.1 Introduction -- 6.2 Indirect Monitoring of Transport Infrastructure -- 6.3 Road Pavement Applications -- 6.3.1 Foundation Quality -- 6.3.2 Road Cracking and Surface Roughness -- 6.3.3 Pavement Stiffness -- 6.4 Railway Track Applications -- 6.5 Bridge Applications -- 6.6 Vehicle Management -- 6.7 Data Analytics -- 6.7.1 Internet of Things -- 6.7.2 Data Analytics Challenges -- 6.8 Conclusion -- Acknowledgment -- References -- 7: Big Data Exploration to Examine Aggressive Driving Behavior in the Era of Smart Cities -- 7.1 Introduction -- 7.2 Data Description -- 7.3 Methodology -- 7.3.1 Data Partitioning -- 7.3.2 Data Extraction -- 7.3.3 Knowledge Discovery -- 7.3.3.1 K-Means Method -- 7.3.3.2 Scenario Development -- 7.3.3.3 Variable Selection -- 7.4 Results -- 7.4.1 Cluster Determination -- 7.4.2 Hot Spot Identification -- 7.4.3 Impact of Trip Travel Time -- 7.5 Conclusions -- References -- 8: Exploratory Analysis of Run-Off-Road Crash Patterns -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Method and Data -- 8.3.1 Multiple Correspondence Analysis -- 8.3.1.1 Cloud of Individuals 8.3.1.2 Cloud of Categories -- 8.3.2 Data Collection -- 8.4 Results and Discussions -- 8.5 Conclusion -- References -- 9: Predicting Traffic Safety Risk Factors Using an Ensemble Classifier -- 9.1 Introduction -- 9.2 Problem Statement -- 9.3 Method -- 9.4 Classification of Event Data -- 9.4.1 Multinomial Logistic Regression (MLR) -- 9.4.2 Random Forest (RF) -- 9.4.3 Method Selection -- 9.4.4 Multivariate Time Series Random Forest (MTS-RF) -- 9.5 Data -- 9.6 Results and Discussions -- 9.7 Conclusions and Recommendations -- 9.8 Practical Applications -- Disclaimer -- References -- 10: Architecture Design of Internet of Things-Enabled Cloud Platform for Managing the Production of Prefabricated Public Houses -- 10.1 Introduction -- 10.1.1 Housing Concerns in Shenzhen and Advantages of Prefabricated Public Houses -- 10.1.2 Existing Technical Challenges -- 10.1.3 Internet of Things-Enabled Cloud Platform -- 10.2 Research Background -- 10.2.1 Massive Production of Prefabricated Public Houses in Shenzhen, Advantages of Prefabrication and Policies to Promote Prefabrication -- 10.2.2 Technical Challenges of Managing Massive Production of Prefabricated Public Houses -- 10.3 Architecture Design for Internet of Things-Enabled Cloud Platform -- 10.3.1 Processes and Function Requirements -- 10.3.2 Service-Oriented Architecture Design -- 10.3.3 Gateway Operation System and Quick Response Code for Defining Intelligent Building Elements -- 10.3.4 Beidou and GIS Integrated Technologies for Positioning Intelligent Building Elements -- 10.3.5 Dynamic nD BIM for Visualizing the Whole Processes of Prefabrication Construction Processes -- 10.3.6 Data Source Management Service through Big Data Analytics Systems -- 10.4 Benefits to Stakeholders Involved in Massive Production of Prefabricated Public Houses -- 10.4.1 Benefits and Marketing Opportunities for Clients 10.4.2 Benefits and Marketing Opportunities for Contractors -- 10.4.3 Benefits and Marketing Opportunities for Prefabrication Manufacturers -- 10.4.4 Benefits and Marketing Opportunities for Third-Party Logistics Firms -- 10.4.5 Benefits and Marketing Opportunities for IT Vendors -- 10.5 Discussion and Future Research -- References -- Index Big data Quantitative research Smart cities Electronic books Buttlar, William G. mitwirkender (DE-588)1161009493 (DE-627)1024416461 (DE-576)506300730 ctb 9781138308770 Erscheint auch als Druck-Ausgabe 9781138308770 https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=5566778 X:EBC Verlag lizenzpflichtig Volltext https://www.gbv.de/dms/bowker/toc/9781138308770.pdf DE-601 pdf/application 2020-01-19 Aggregator Inhaltsverzeichnis ZDB-30-PQE GBV_ILN_30 ISIL_DE-104 SYSFLAG_1 GBV_KXP GBV_ILN_206 ISIL_DE-Brg3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2021 ISIL_DE-289 SWB 4170rda BO 045F 307.760285 30 01 0104 1831714302 EBL-UBCL Campusweiter Zugriff. - Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 11-12-18 206 01 3350 1831602504 00 --%%-- Online-Ressource g --%%-- OLR-EBL If you are a ThHF affiliate and the E-Book is not fully accessible, please send us a purchase or short time loan request. All others: Inter-library loans and guest access on campus premises is not possible. zh 10-12-18 370 01 4370 3976600172 olr-dda ebc Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 09-09-21 2021 01 DE-289 3844332650 00 --%%-- --%%-- --%%-- n l01 28-01-21 30 01 0104 https://ebookcentral.proquest.com/lib/tuclausthal-ebooks/detail.action?docID=5566778 206 01 3350 Full Text only for ThHF affiliates https://thh-friedensau.idm.oclc.org/login?url=http://ebookcentral.proquest.com/lib/thhfriedensau/detail.action?docID=5566778 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von auβerhalb nur für HCU-Angehörige möglich https://ebookcentral.proquest.com/lib/hcuhamburg-ebooks/detail.action?docID=5566778 2021 01 DE-289 https://ebookcentral.proquest.com/lib/kiz-uniulm/detail.action?docID=5566778 30 01 0104 EBL-UBCL 206 01 3350 OLR-EBL 370 01 4370 olr-dda ebc |
| allfields_unstemmed |
9780429786631 : electronic bk. 978-0-429-78663-1 9781138308770 (DE-627)1039849717 (DE-576)520223527 (DE-599)GBV1039849717 (EBP)038198258 (EBL)EBL5566778 (EBR)ebr11626386 (EBC)EBC5566778 DE-627 eng DE-627 rda eng 307.760285 Alavi, Amir verfasserin aut Data Analytics for Smart Cities. Milton Auerbach Publications 2018 ©2019 1 online resource (255 pages) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data Analytics Applications Ser. Description based on publisher supplied metadata and other sources Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- 1: Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition Assessment -- 1.1 Introduction -- 1.2 Smartphone-Driven Assessment of Airport Pavement Condition -- 1.2.1 Description of Smartphone Application -- 1.2.2 Smartphone Characteristics -- 1.3 Case Study of Missouri Airports -- 1.3.1 Calibration Study -- 1.3.2 Missouri Airport Smartphone Data Collection Methodology -- 1.3.3 Missouri Airport Smartphone Data Collection Results for Each Airport -- 1.3.4 Discussion -- 1.4 Prediction of PCI Based on Smartphone-Measured IRI -- 1.4.1 Machine Learning Method -- 1.4.2 GEP-Based Formulation of PCI -- 1.5 Conclusions -- Acknowledgments -- References -- 2: Global Satellite Observations for Smart Cities -- 2.1 Introduction -- 2.2 Overview of NASA Satellite-Based Global Data Products for Smart Cities -- 2.2.1 Satellite-Based Data Products at the GES DISC -- 2.2.1.1 Multi-Satellite and Multi-Sensor Merged Global Precipitation Products -- 2.2.1.2 Global and Regional Land Data Assimilation Products -- 2.2.1.3 Modern-Era Retrospective Analysis for Research and Applications (MERRA) Products -- 2.3 Data Services -- 2.3.1 Point-and-Click Online Tools -- 2.3.1.1 NASA's Worldview -- 2.3.1.2 NASA GES DISC Giovanni -- 2.3.2 Data Rod Services -- 2.3.3 Other Web Data Services -- 2.4 Examples -- 2.4.1 The Pearl River Delta -- 2.4.1.1 Typhoon Nida Rainfall -- 2.4.1.2 Atmospheric Composition Preliminary Analysis -- 2.4.2 Estimation of Hurricane Contribution to Annual Precipitation in Maryland -- 2.4.2.1 Data and Methods -- 2.4.2.2 Preliminary Results -- 2.5 Summary and Future Plans -- Acknowledgments -- References -- 3: Advancing Smart and Resilient Cities with Big Spatial Disaster Data -- 3.1 Introduction 3.2 The Role of Spatial Data in Coastal Resilience Applications -- 3.2.1 Disaster Management Cycle -- 3.2.2 Data Acquisition -- 3.2.3 Challenges and Opportunities -- 3.3 A Hurricane Sandy Inspired Big Data Framework for Coastal Resilience Investigations with Heterogeneous Spatial Data -- 3.3.1 Geospatial Response to Hurricane Sandy -- 3.3.2 Data Analytic Framework -- 3.3.3 Anatomy of Big Spatial Disaster Data -- 3.3.3.1 Volume -- 3.3.3.2 Data Structure -- 3.3.3.3 Spatial Completeness -- 3.3.3.4 Veracity -- 3.3.3.5 Velocity -- 3.3.4 Decomposition of Processing Tasks -- 3.3.4.1 Digital Elevation Models -- 3.3.4.2 Feature Extraction -- 3.3.4.3 Change Detection -- 3.3.4.4 Core Operation Categories -- 3.3.5 Identify the Uncertainty Associated with Big Data Acquisition and Processing -- 3.3.6 Computing with Big Data Infrastructure -- 3.3.7 Connecting Data Processing with Decision-Making Models -- 3.3.8 Future Improvement -- 3.4 Conclusion -- References -- 4: Smart City Portrayal -- 4.1 Introduction -- 4.2 Background and Related Work -- 4.2.1 Point Representation -- 4.2.2 Geographic Generalization -- 4.2.3 Heatmap -- 4.2.4 Circular Plot -- 4.2.5 Schematic Map -- 4.3 Point Representation: DESIGN STUDY -- 4.3.1 Concept and Formalization -- 4.3.2 Color-Coding -- 4.3.3 Implementation -- 4.3.4 Graphical Error -- 4.4 Map Transformations -- 4.4.1 Map Morphing -- 4.4.1.1 Metro Map Format -- 4.4.1.2 Station to Station Transition -- 4.4.1.3 Connection to Connection Transition -- 4.4.2 Splitting Overlapped Segments -- 4.4.3 Reactive Circle Scaling -- 4.5 Interactive Application -- 4.5.1 General Overview -- 4.5.2 Circular Diagram -- 4.6 Critical Discussion -- 4.7 Conclusions -- Acknowledgments -- References -- 5: Smart Bike-Sharing Systems for Smart Cities -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Bike Availability Prediction 5.2.2 Using Clustering to Explore Data Trends -- 5.2.3 Rebalancing -- 5.3 Data Set -- 5.4 Bike Prediction Models and Results -- 5.4.1 Univariate Models -- 5.4.2 Multivariate Models -- 5.5 Supervised Clustering -- 5.5.1 Proposed Algorithm -- 5.5.2 Model Order Selection -- 5.5.3 Day of the Week -- 5.5.4 Hour of the Day -- 5.6 Rebalancing -- 5.6.1 Problem Statement -- 5.7 Proposed Algorithm -- 5.7.1 Tour Construction Using the Deferred Acceptance Algorithm -- 5.7.2 Tour Improvement Using 2-Opt Local Search Algorithm -- 5.7.3 Tour Construction Example -- 5.8 Results -- 5.8.1 San Francesco Bay Area Instances -- 5.9 Conclusions -- Acknowledgments -- References -- 6: Indirect Monitoring of Critical Transport Infrastructure: Data Analytics and Signal Processing -- 6.1 Introduction -- 6.2 Indirect Monitoring of Transport Infrastructure -- 6.3 Road Pavement Applications -- 6.3.1 Foundation Quality -- 6.3.2 Road Cracking and Surface Roughness -- 6.3.3 Pavement Stiffness -- 6.4 Railway Track Applications -- 6.5 Bridge Applications -- 6.6 Vehicle Management -- 6.7 Data Analytics -- 6.7.1 Internet of Things -- 6.7.2 Data Analytics Challenges -- 6.8 Conclusion -- Acknowledgment -- References -- 7: Big Data Exploration to Examine Aggressive Driving Behavior in the Era of Smart Cities -- 7.1 Introduction -- 7.2 Data Description -- 7.3 Methodology -- 7.3.1 Data Partitioning -- 7.3.2 Data Extraction -- 7.3.3 Knowledge Discovery -- 7.3.3.1 K-Means Method -- 7.3.3.2 Scenario Development -- 7.3.3.3 Variable Selection -- 7.4 Results -- 7.4.1 Cluster Determination -- 7.4.2 Hot Spot Identification -- 7.4.3 Impact of Trip Travel Time -- 7.5 Conclusions -- References -- 8: Exploratory Analysis of Run-Off-Road Crash Patterns -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Method and Data -- 8.3.1 Multiple Correspondence Analysis -- 8.3.1.1 Cloud of Individuals 8.3.1.2 Cloud of Categories -- 8.3.2 Data Collection -- 8.4 Results and Discussions -- 8.5 Conclusion -- References -- 9: Predicting Traffic Safety Risk Factors Using an Ensemble Classifier -- 9.1 Introduction -- 9.2 Problem Statement -- 9.3 Method -- 9.4 Classification of Event Data -- 9.4.1 Multinomial Logistic Regression (MLR) -- 9.4.2 Random Forest (RF) -- 9.4.3 Method Selection -- 9.4.4 Multivariate Time Series Random Forest (MTS-RF) -- 9.5 Data -- 9.6 Results and Discussions -- 9.7 Conclusions and Recommendations -- 9.8 Practical Applications -- Disclaimer -- References -- 10: Architecture Design of Internet of Things-Enabled Cloud Platform for Managing the Production of Prefabricated Public Houses -- 10.1 Introduction -- 10.1.1 Housing Concerns in Shenzhen and Advantages of Prefabricated Public Houses -- 10.1.2 Existing Technical Challenges -- 10.1.3 Internet of Things-Enabled Cloud Platform -- 10.2 Research Background -- 10.2.1 Massive Production of Prefabricated Public Houses in Shenzhen, Advantages of Prefabrication and Policies to Promote Prefabrication -- 10.2.2 Technical Challenges of Managing Massive Production of Prefabricated Public Houses -- 10.3 Architecture Design for Internet of Things-Enabled Cloud Platform -- 10.3.1 Processes and Function Requirements -- 10.3.2 Service-Oriented Architecture Design -- 10.3.3 Gateway Operation System and Quick Response Code for Defining Intelligent Building Elements -- 10.3.4 Beidou and GIS Integrated Technologies for Positioning Intelligent Building Elements -- 10.3.5 Dynamic nD BIM for Visualizing the Whole Processes of Prefabrication Construction Processes -- 10.3.6 Data Source Management Service through Big Data Analytics Systems -- 10.4 Benefits to Stakeholders Involved in Massive Production of Prefabricated Public Houses -- 10.4.1 Benefits and Marketing Opportunities for Clients 10.4.2 Benefits and Marketing Opportunities for Contractors -- 10.4.3 Benefits and Marketing Opportunities for Prefabrication Manufacturers -- 10.4.4 Benefits and Marketing Opportunities for Third-Party Logistics Firms -- 10.4.5 Benefits and Marketing Opportunities for IT Vendors -- 10.5 Discussion and Future Research -- References -- Index Big data Quantitative research Smart cities Electronic books Buttlar, William G. mitwirkender (DE-588)1161009493 (DE-627)1024416461 (DE-576)506300730 ctb 9781138308770 Erscheint auch als Druck-Ausgabe 9781138308770 https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=5566778 X:EBC Verlag lizenzpflichtig Volltext https://www.gbv.de/dms/bowker/toc/9781138308770.pdf DE-601 pdf/application 2020-01-19 Aggregator Inhaltsverzeichnis ZDB-30-PQE GBV_ILN_30 ISIL_DE-104 SYSFLAG_1 GBV_KXP GBV_ILN_206 ISIL_DE-Brg3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2021 ISIL_DE-289 SWB 4170rda BO 045F 307.760285 30 01 0104 1831714302 EBL-UBCL Campusweiter Zugriff. - Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 11-12-18 206 01 3350 1831602504 00 --%%-- Online-Ressource g --%%-- OLR-EBL If you are a ThHF affiliate and the E-Book is not fully accessible, please send us a purchase or short time loan request. All others: Inter-library loans and guest access on campus premises is not possible. zh 10-12-18 370 01 4370 3976600172 olr-dda ebc Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 09-09-21 2021 01 DE-289 3844332650 00 --%%-- --%%-- --%%-- n l01 28-01-21 30 01 0104 https://ebookcentral.proquest.com/lib/tuclausthal-ebooks/detail.action?docID=5566778 206 01 3350 Full Text only for ThHF affiliates https://thh-friedensau.idm.oclc.org/login?url=http://ebookcentral.proquest.com/lib/thhfriedensau/detail.action?docID=5566778 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von auβerhalb nur für HCU-Angehörige möglich https://ebookcentral.proquest.com/lib/hcuhamburg-ebooks/detail.action?docID=5566778 2021 01 DE-289 https://ebookcentral.proquest.com/lib/kiz-uniulm/detail.action?docID=5566778 30 01 0104 EBL-UBCL 206 01 3350 OLR-EBL 370 01 4370 olr-dda ebc |
| allfieldsGer |
9780429786631 : electronic bk. 978-0-429-78663-1 9781138308770 (DE-627)1039849717 (DE-576)520223527 (DE-599)GBV1039849717 (EBP)038198258 (EBL)EBL5566778 (EBR)ebr11626386 (EBC)EBC5566778 DE-627 eng DE-627 rda eng 307.760285 Alavi, Amir verfasserin aut Data Analytics for Smart Cities. Milton Auerbach Publications 2018 ©2019 1 online resource (255 pages) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data Analytics Applications Ser. Description based on publisher supplied metadata and other sources Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- 1: Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition Assessment -- 1.1 Introduction -- 1.2 Smartphone-Driven Assessment of Airport Pavement Condition -- 1.2.1 Description of Smartphone Application -- 1.2.2 Smartphone Characteristics -- 1.3 Case Study of Missouri Airports -- 1.3.1 Calibration Study -- 1.3.2 Missouri Airport Smartphone Data Collection Methodology -- 1.3.3 Missouri Airport Smartphone Data Collection Results for Each Airport -- 1.3.4 Discussion -- 1.4 Prediction of PCI Based on Smartphone-Measured IRI -- 1.4.1 Machine Learning Method -- 1.4.2 GEP-Based Formulation of PCI -- 1.5 Conclusions -- Acknowledgments -- References -- 2: Global Satellite Observations for Smart Cities -- 2.1 Introduction -- 2.2 Overview of NASA Satellite-Based Global Data Products for Smart Cities -- 2.2.1 Satellite-Based Data Products at the GES DISC -- 2.2.1.1 Multi-Satellite and Multi-Sensor Merged Global Precipitation Products -- 2.2.1.2 Global and Regional Land Data Assimilation Products -- 2.2.1.3 Modern-Era Retrospective Analysis for Research and Applications (MERRA) Products -- 2.3 Data Services -- 2.3.1 Point-and-Click Online Tools -- 2.3.1.1 NASA's Worldview -- 2.3.1.2 NASA GES DISC Giovanni -- 2.3.2 Data Rod Services -- 2.3.3 Other Web Data Services -- 2.4 Examples -- 2.4.1 The Pearl River Delta -- 2.4.1.1 Typhoon Nida Rainfall -- 2.4.1.2 Atmospheric Composition Preliminary Analysis -- 2.4.2 Estimation of Hurricane Contribution to Annual Precipitation in Maryland -- 2.4.2.1 Data and Methods -- 2.4.2.2 Preliminary Results -- 2.5 Summary and Future Plans -- Acknowledgments -- References -- 3: Advancing Smart and Resilient Cities with Big Spatial Disaster Data -- 3.1 Introduction 3.2 The Role of Spatial Data in Coastal Resilience Applications -- 3.2.1 Disaster Management Cycle -- 3.2.2 Data Acquisition -- 3.2.3 Challenges and Opportunities -- 3.3 A Hurricane Sandy Inspired Big Data Framework for Coastal Resilience Investigations with Heterogeneous Spatial Data -- 3.3.1 Geospatial Response to Hurricane Sandy -- 3.3.2 Data Analytic Framework -- 3.3.3 Anatomy of Big Spatial Disaster Data -- 3.3.3.1 Volume -- 3.3.3.2 Data Structure -- 3.3.3.3 Spatial Completeness -- 3.3.3.4 Veracity -- 3.3.3.5 Velocity -- 3.3.4 Decomposition of Processing Tasks -- 3.3.4.1 Digital Elevation Models -- 3.3.4.2 Feature Extraction -- 3.3.4.3 Change Detection -- 3.3.4.4 Core Operation Categories -- 3.3.5 Identify the Uncertainty Associated with Big Data Acquisition and Processing -- 3.3.6 Computing with Big Data Infrastructure -- 3.3.7 Connecting Data Processing with Decision-Making Models -- 3.3.8 Future Improvement -- 3.4 Conclusion -- References -- 4: Smart City Portrayal -- 4.1 Introduction -- 4.2 Background and Related Work -- 4.2.1 Point Representation -- 4.2.2 Geographic Generalization -- 4.2.3 Heatmap -- 4.2.4 Circular Plot -- 4.2.5 Schematic Map -- 4.3 Point Representation: DESIGN STUDY -- 4.3.1 Concept and Formalization -- 4.3.2 Color-Coding -- 4.3.3 Implementation -- 4.3.4 Graphical Error -- 4.4 Map Transformations -- 4.4.1 Map Morphing -- 4.4.1.1 Metro Map Format -- 4.4.1.2 Station to Station Transition -- 4.4.1.3 Connection to Connection Transition -- 4.4.2 Splitting Overlapped Segments -- 4.4.3 Reactive Circle Scaling -- 4.5 Interactive Application -- 4.5.1 General Overview -- 4.5.2 Circular Diagram -- 4.6 Critical Discussion -- 4.7 Conclusions -- Acknowledgments -- References -- 5: Smart Bike-Sharing Systems for Smart Cities -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Bike Availability Prediction 5.2.2 Using Clustering to Explore Data Trends -- 5.2.3 Rebalancing -- 5.3 Data Set -- 5.4 Bike Prediction Models and Results -- 5.4.1 Univariate Models -- 5.4.2 Multivariate Models -- 5.5 Supervised Clustering -- 5.5.1 Proposed Algorithm -- 5.5.2 Model Order Selection -- 5.5.3 Day of the Week -- 5.5.4 Hour of the Day -- 5.6 Rebalancing -- 5.6.1 Problem Statement -- 5.7 Proposed Algorithm -- 5.7.1 Tour Construction Using the Deferred Acceptance Algorithm -- 5.7.2 Tour Improvement Using 2-Opt Local Search Algorithm -- 5.7.3 Tour Construction Example -- 5.8 Results -- 5.8.1 San Francesco Bay Area Instances -- 5.9 Conclusions -- Acknowledgments -- References -- 6: Indirect Monitoring of Critical Transport Infrastructure: Data Analytics and Signal Processing -- 6.1 Introduction -- 6.2 Indirect Monitoring of Transport Infrastructure -- 6.3 Road Pavement Applications -- 6.3.1 Foundation Quality -- 6.3.2 Road Cracking and Surface Roughness -- 6.3.3 Pavement Stiffness -- 6.4 Railway Track Applications -- 6.5 Bridge Applications -- 6.6 Vehicle Management -- 6.7 Data Analytics -- 6.7.1 Internet of Things -- 6.7.2 Data Analytics Challenges -- 6.8 Conclusion -- Acknowledgment -- References -- 7: Big Data Exploration to Examine Aggressive Driving Behavior in the Era of Smart Cities -- 7.1 Introduction -- 7.2 Data Description -- 7.3 Methodology -- 7.3.1 Data Partitioning -- 7.3.2 Data Extraction -- 7.3.3 Knowledge Discovery -- 7.3.3.1 K-Means Method -- 7.3.3.2 Scenario Development -- 7.3.3.3 Variable Selection -- 7.4 Results -- 7.4.1 Cluster Determination -- 7.4.2 Hot Spot Identification -- 7.4.3 Impact of Trip Travel Time -- 7.5 Conclusions -- References -- 8: Exploratory Analysis of Run-Off-Road Crash Patterns -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Method and Data -- 8.3.1 Multiple Correspondence Analysis -- 8.3.1.1 Cloud of Individuals 8.3.1.2 Cloud of Categories -- 8.3.2 Data Collection -- 8.4 Results and Discussions -- 8.5 Conclusion -- References -- 9: Predicting Traffic Safety Risk Factors Using an Ensemble Classifier -- 9.1 Introduction -- 9.2 Problem Statement -- 9.3 Method -- 9.4 Classification of Event Data -- 9.4.1 Multinomial Logistic Regression (MLR) -- 9.4.2 Random Forest (RF) -- 9.4.3 Method Selection -- 9.4.4 Multivariate Time Series Random Forest (MTS-RF) -- 9.5 Data -- 9.6 Results and Discussions -- 9.7 Conclusions and Recommendations -- 9.8 Practical Applications -- Disclaimer -- References -- 10: Architecture Design of Internet of Things-Enabled Cloud Platform for Managing the Production of Prefabricated Public Houses -- 10.1 Introduction -- 10.1.1 Housing Concerns in Shenzhen and Advantages of Prefabricated Public Houses -- 10.1.2 Existing Technical Challenges -- 10.1.3 Internet of Things-Enabled Cloud Platform -- 10.2 Research Background -- 10.2.1 Massive Production of Prefabricated Public Houses in Shenzhen, Advantages of Prefabrication and Policies to Promote Prefabrication -- 10.2.2 Technical Challenges of Managing Massive Production of Prefabricated Public Houses -- 10.3 Architecture Design for Internet of Things-Enabled Cloud Platform -- 10.3.1 Processes and Function Requirements -- 10.3.2 Service-Oriented Architecture Design -- 10.3.3 Gateway Operation System and Quick Response Code for Defining Intelligent Building Elements -- 10.3.4 Beidou and GIS Integrated Technologies for Positioning Intelligent Building Elements -- 10.3.5 Dynamic nD BIM for Visualizing the Whole Processes of Prefabrication Construction Processes -- 10.3.6 Data Source Management Service through Big Data Analytics Systems -- 10.4 Benefits to Stakeholders Involved in Massive Production of Prefabricated Public Houses -- 10.4.1 Benefits and Marketing Opportunities for Clients 10.4.2 Benefits and Marketing Opportunities for Contractors -- 10.4.3 Benefits and Marketing Opportunities for Prefabrication Manufacturers -- 10.4.4 Benefits and Marketing Opportunities for Third-Party Logistics Firms -- 10.4.5 Benefits and Marketing Opportunities for IT Vendors -- 10.5 Discussion and Future Research -- References -- Index Big data Quantitative research Smart cities Electronic books Buttlar, William G. mitwirkender (DE-588)1161009493 (DE-627)1024416461 (DE-576)506300730 ctb 9781138308770 Erscheint auch als Druck-Ausgabe 9781138308770 https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=5566778 X:EBC Verlag lizenzpflichtig Volltext https://www.gbv.de/dms/bowker/toc/9781138308770.pdf DE-601 pdf/application 2020-01-19 Aggregator Inhaltsverzeichnis ZDB-30-PQE GBV_ILN_30 ISIL_DE-104 SYSFLAG_1 GBV_KXP GBV_ILN_206 ISIL_DE-Brg3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2021 ISIL_DE-289 SWB 4170rda BO 045F 307.760285 30 01 0104 1831714302 EBL-UBCL Campusweiter Zugriff. - Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 11-12-18 206 01 3350 1831602504 00 --%%-- Online-Ressource g --%%-- OLR-EBL If you are a ThHF affiliate and the E-Book is not fully accessible, please send us a purchase or short time loan request. All others: Inter-library loans and guest access on campus premises is not possible. zh 10-12-18 370 01 4370 3976600172 olr-dda ebc Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 09-09-21 2021 01 DE-289 3844332650 00 --%%-- --%%-- --%%-- n l01 28-01-21 30 01 0104 https://ebookcentral.proquest.com/lib/tuclausthal-ebooks/detail.action?docID=5566778 206 01 3350 Full Text only for ThHF affiliates https://thh-friedensau.idm.oclc.org/login?url=http://ebookcentral.proquest.com/lib/thhfriedensau/detail.action?docID=5566778 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von auβerhalb nur für HCU-Angehörige möglich https://ebookcentral.proquest.com/lib/hcuhamburg-ebooks/detail.action?docID=5566778 2021 01 DE-289 https://ebookcentral.proquest.com/lib/kiz-uniulm/detail.action?docID=5566778 30 01 0104 EBL-UBCL 206 01 3350 OLR-EBL 370 01 4370 olr-dda ebc |
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9780429786631 : electronic bk. 978-0-429-78663-1 9781138308770 (DE-627)1039849717 (DE-576)520223527 (DE-599)GBV1039849717 (EBP)038198258 (EBL)EBL5566778 (EBR)ebr11626386 (EBC)EBC5566778 DE-627 eng DE-627 rda eng 307.760285 Alavi, Amir verfasserin aut Data Analytics for Smart Cities. Milton Auerbach Publications 2018 ©2019 1 online resource (255 pages) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data Analytics Applications Ser. Description based on publisher supplied metadata and other sources Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- 1: Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition Assessment -- 1.1 Introduction -- 1.2 Smartphone-Driven Assessment of Airport Pavement Condition -- 1.2.1 Description of Smartphone Application -- 1.2.2 Smartphone Characteristics -- 1.3 Case Study of Missouri Airports -- 1.3.1 Calibration Study -- 1.3.2 Missouri Airport Smartphone Data Collection Methodology -- 1.3.3 Missouri Airport Smartphone Data Collection Results for Each Airport -- 1.3.4 Discussion -- 1.4 Prediction of PCI Based on Smartphone-Measured IRI -- 1.4.1 Machine Learning Method -- 1.4.2 GEP-Based Formulation of PCI -- 1.5 Conclusions -- Acknowledgments -- References -- 2: Global Satellite Observations for Smart Cities -- 2.1 Introduction -- 2.2 Overview of NASA Satellite-Based Global Data Products for Smart Cities -- 2.2.1 Satellite-Based Data Products at the GES DISC -- 2.2.1.1 Multi-Satellite and Multi-Sensor Merged Global Precipitation Products -- 2.2.1.2 Global and Regional Land Data Assimilation Products -- 2.2.1.3 Modern-Era Retrospective Analysis for Research and Applications (MERRA) Products -- 2.3 Data Services -- 2.3.1 Point-and-Click Online Tools -- 2.3.1.1 NASA's Worldview -- 2.3.1.2 NASA GES DISC Giovanni -- 2.3.2 Data Rod Services -- 2.3.3 Other Web Data Services -- 2.4 Examples -- 2.4.1 The Pearl River Delta -- 2.4.1.1 Typhoon Nida Rainfall -- 2.4.1.2 Atmospheric Composition Preliminary Analysis -- 2.4.2 Estimation of Hurricane Contribution to Annual Precipitation in Maryland -- 2.4.2.1 Data and Methods -- 2.4.2.2 Preliminary Results -- 2.5 Summary and Future Plans -- Acknowledgments -- References -- 3: Advancing Smart and Resilient Cities with Big Spatial Disaster Data -- 3.1 Introduction 3.2 The Role of Spatial Data in Coastal Resilience Applications -- 3.2.1 Disaster Management Cycle -- 3.2.2 Data Acquisition -- 3.2.3 Challenges and Opportunities -- 3.3 A Hurricane Sandy Inspired Big Data Framework for Coastal Resilience Investigations with Heterogeneous Spatial Data -- 3.3.1 Geospatial Response to Hurricane Sandy -- 3.3.2 Data Analytic Framework -- 3.3.3 Anatomy of Big Spatial Disaster Data -- 3.3.3.1 Volume -- 3.3.3.2 Data Structure -- 3.3.3.3 Spatial Completeness -- 3.3.3.4 Veracity -- 3.3.3.5 Velocity -- 3.3.4 Decomposition of Processing Tasks -- 3.3.4.1 Digital Elevation Models -- 3.3.4.2 Feature Extraction -- 3.3.4.3 Change Detection -- 3.3.4.4 Core Operation Categories -- 3.3.5 Identify the Uncertainty Associated with Big Data Acquisition and Processing -- 3.3.6 Computing with Big Data Infrastructure -- 3.3.7 Connecting Data Processing with Decision-Making Models -- 3.3.8 Future Improvement -- 3.4 Conclusion -- References -- 4: Smart City Portrayal -- 4.1 Introduction -- 4.2 Background and Related Work -- 4.2.1 Point Representation -- 4.2.2 Geographic Generalization -- 4.2.3 Heatmap -- 4.2.4 Circular Plot -- 4.2.5 Schematic Map -- 4.3 Point Representation: DESIGN STUDY -- 4.3.1 Concept and Formalization -- 4.3.2 Color-Coding -- 4.3.3 Implementation -- 4.3.4 Graphical Error -- 4.4 Map Transformations -- 4.4.1 Map Morphing -- 4.4.1.1 Metro Map Format -- 4.4.1.2 Station to Station Transition -- 4.4.1.3 Connection to Connection Transition -- 4.4.2 Splitting Overlapped Segments -- 4.4.3 Reactive Circle Scaling -- 4.5 Interactive Application -- 4.5.1 General Overview -- 4.5.2 Circular Diagram -- 4.6 Critical Discussion -- 4.7 Conclusions -- Acknowledgments -- References -- 5: Smart Bike-Sharing Systems for Smart Cities -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Bike Availability Prediction 5.2.2 Using Clustering to Explore Data Trends -- 5.2.3 Rebalancing -- 5.3 Data Set -- 5.4 Bike Prediction Models and Results -- 5.4.1 Univariate Models -- 5.4.2 Multivariate Models -- 5.5 Supervised Clustering -- 5.5.1 Proposed Algorithm -- 5.5.2 Model Order Selection -- 5.5.3 Day of the Week -- 5.5.4 Hour of the Day -- 5.6 Rebalancing -- 5.6.1 Problem Statement -- 5.7 Proposed Algorithm -- 5.7.1 Tour Construction Using the Deferred Acceptance Algorithm -- 5.7.2 Tour Improvement Using 2-Opt Local Search Algorithm -- 5.7.3 Tour Construction Example -- 5.8 Results -- 5.8.1 San Francesco Bay Area Instances -- 5.9 Conclusions -- Acknowledgments -- References -- 6: Indirect Monitoring of Critical Transport Infrastructure: Data Analytics and Signal Processing -- 6.1 Introduction -- 6.2 Indirect Monitoring of Transport Infrastructure -- 6.3 Road Pavement Applications -- 6.3.1 Foundation Quality -- 6.3.2 Road Cracking and Surface Roughness -- 6.3.3 Pavement Stiffness -- 6.4 Railway Track Applications -- 6.5 Bridge Applications -- 6.6 Vehicle Management -- 6.7 Data Analytics -- 6.7.1 Internet of Things -- 6.7.2 Data Analytics Challenges -- 6.8 Conclusion -- Acknowledgment -- References -- 7: Big Data Exploration to Examine Aggressive Driving Behavior in the Era of Smart Cities -- 7.1 Introduction -- 7.2 Data Description -- 7.3 Methodology -- 7.3.1 Data Partitioning -- 7.3.2 Data Extraction -- 7.3.3 Knowledge Discovery -- 7.3.3.1 K-Means Method -- 7.3.3.2 Scenario Development -- 7.3.3.3 Variable Selection -- 7.4 Results -- 7.4.1 Cluster Determination -- 7.4.2 Hot Spot Identification -- 7.4.3 Impact of Trip Travel Time -- 7.5 Conclusions -- References -- 8: Exploratory Analysis of Run-Off-Road Crash Patterns -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Method and Data -- 8.3.1 Multiple Correspondence Analysis -- 8.3.1.1 Cloud of Individuals 8.3.1.2 Cloud of Categories -- 8.3.2 Data Collection -- 8.4 Results and Discussions -- 8.5 Conclusion -- References -- 9: Predicting Traffic Safety Risk Factors Using an Ensemble Classifier -- 9.1 Introduction -- 9.2 Problem Statement -- 9.3 Method -- 9.4 Classification of Event Data -- 9.4.1 Multinomial Logistic Regression (MLR) -- 9.4.2 Random Forest (RF) -- 9.4.3 Method Selection -- 9.4.4 Multivariate Time Series Random Forest (MTS-RF) -- 9.5 Data -- 9.6 Results and Discussions -- 9.7 Conclusions and Recommendations -- 9.8 Practical Applications -- Disclaimer -- References -- 10: Architecture Design of Internet of Things-Enabled Cloud Platform for Managing the Production of Prefabricated Public Houses -- 10.1 Introduction -- 10.1.1 Housing Concerns in Shenzhen and Advantages of Prefabricated Public Houses -- 10.1.2 Existing Technical Challenges -- 10.1.3 Internet of Things-Enabled Cloud Platform -- 10.2 Research Background -- 10.2.1 Massive Production of Prefabricated Public Houses in Shenzhen, Advantages of Prefabrication and Policies to Promote Prefabrication -- 10.2.2 Technical Challenges of Managing Massive Production of Prefabricated Public Houses -- 10.3 Architecture Design for Internet of Things-Enabled Cloud Platform -- 10.3.1 Processes and Function Requirements -- 10.3.2 Service-Oriented Architecture Design -- 10.3.3 Gateway Operation System and Quick Response Code for Defining Intelligent Building Elements -- 10.3.4 Beidou and GIS Integrated Technologies for Positioning Intelligent Building Elements -- 10.3.5 Dynamic nD BIM for Visualizing the Whole Processes of Prefabrication Construction Processes -- 10.3.6 Data Source Management Service through Big Data Analytics Systems -- 10.4 Benefits to Stakeholders Involved in Massive Production of Prefabricated Public Houses -- 10.4.1 Benefits and Marketing Opportunities for Clients 10.4.2 Benefits and Marketing Opportunities for Contractors -- 10.4.3 Benefits and Marketing Opportunities for Prefabrication Manufacturers -- 10.4.4 Benefits and Marketing Opportunities for Third-Party Logistics Firms -- 10.4.5 Benefits and Marketing Opportunities for IT Vendors -- 10.5 Discussion and Future Research -- References -- Index Big data Quantitative research Smart cities Electronic books Buttlar, William G. mitwirkender (DE-588)1161009493 (DE-627)1024416461 (DE-576)506300730 ctb 9781138308770 Erscheint auch als Druck-Ausgabe 9781138308770 https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=5566778 X:EBC Verlag lizenzpflichtig Volltext https://www.gbv.de/dms/bowker/toc/9781138308770.pdf DE-601 pdf/application 2020-01-19 Aggregator Inhaltsverzeichnis ZDB-30-PQE GBV_ILN_30 ISIL_DE-104 SYSFLAG_1 GBV_KXP GBV_ILN_206 ISIL_DE-Brg3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2021 ISIL_DE-289 SWB 4170rda BO 045F 307.760285 30 01 0104 1831714302 EBL-UBCL Campusweiter Zugriff. - Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 11-12-18 206 01 3350 1831602504 00 --%%-- Online-Ressource g --%%-- OLR-EBL If you are a ThHF affiliate and the E-Book is not fully accessible, please send us a purchase or short time loan request. All others: Inter-library loans and guest access on campus premises is not possible. zh 10-12-18 370 01 4370 3976600172 olr-dda ebc Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. 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Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- 1: Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition Assessment -- 1.1 Introduction -- 1.2 Smartphone-Driven Assessment of Airport Pavement Condition -- 1.2.1 Description of Smartphone Application -- 1.2.2 Smartphone Characteristics -- 1.3 Case Study of Missouri Airports -- 1.3.1 Calibration Study -- 1.3.2 Missouri Airport Smartphone Data Collection Methodology -- 1.3.3 Missouri Airport Smartphone Data Collection Results for Each Airport -- 1.3.4 Discussion -- 1.4 Prediction of PCI Based on Smartphone-Measured IRI -- 1.4.1 Machine Learning Method -- 1.4.2 GEP-Based Formulation of PCI -- 1.5 Conclusions -- Acknowledgments -- References -- 2: Global Satellite Observations for Smart Cities -- 2.1 Introduction -- 2.2 Overview of NASA Satellite-Based Global Data Products for Smart Cities -- 2.2.1 Satellite-Based Data Products at the GES DISC -- 2.2.1.1 Multi-Satellite and Multi-Sensor Merged Global Precipitation Products -- 2.2.1.2 Global and Regional Land Data Assimilation Products -- 2.2.1.3 Modern-Era Retrospective Analysis for Research and Applications (MERRA) Products -- 2.3 Data Services -- 2.3.1 Point-and-Click Online Tools -- 2.3.1.1 NASA's Worldview -- 2.3.1.2 NASA GES DISC Giovanni -- 2.3.2 Data Rod Services -- 2.3.3 Other Web Data Services -- 2.4 Examples -- 2.4.1 The Pearl River Delta -- 2.4.1.1 Typhoon Nida Rainfall -- 2.4.1.2 Atmospheric Composition Preliminary Analysis -- 2.4.2 Estimation of Hurricane Contribution to Annual Precipitation in Maryland -- 2.4.2.1 Data and Methods -- 2.4.2.2 Preliminary Results -- 2.5 Summary and Future Plans -- Acknowledgments -- References -- 3: Advancing Smart and Resilient Cities with Big Spatial Disaster Data -- 3.1 Introduction 3.2 The Role of Spatial Data in Coastal Resilience Applications -- 3.2.1 Disaster Management Cycle -- 3.2.2 Data Acquisition -- 3.2.3 Challenges and Opportunities -- 3.3 A Hurricane Sandy Inspired Big Data Framework for Coastal Resilience Investigations with Heterogeneous Spatial Data -- 3.3.1 Geospatial Response to Hurricane Sandy -- 3.3.2 Data Analytic Framework -- 3.3.3 Anatomy of Big Spatial Disaster Data -- 3.3.3.1 Volume -- 3.3.3.2 Data Structure -- 3.3.3.3 Spatial Completeness -- 3.3.3.4 Veracity -- 3.3.3.5 Velocity -- 3.3.4 Decomposition of Processing Tasks -- 3.3.4.1 Digital Elevation Models -- 3.3.4.2 Feature Extraction -- 3.3.4.3 Change Detection -- 3.3.4.4 Core Operation Categories -- 3.3.5 Identify the Uncertainty Associated with Big Data Acquisition and Processing -- 3.3.6 Computing with Big Data Infrastructure -- 3.3.7 Connecting Data Processing with Decision-Making Models -- 3.3.8 Future Improvement -- 3.4 Conclusion -- References -- 4: Smart City Portrayal -- 4.1 Introduction -- 4.2 Background and Related Work -- 4.2.1 Point Representation -- 4.2.2 Geographic Generalization -- 4.2.3 Heatmap -- 4.2.4 Circular Plot -- 4.2.5 Schematic Map -- 4.3 Point Representation: DESIGN STUDY -- 4.3.1 Concept and Formalization -- 4.3.2 Color-Coding -- 4.3.3 Implementation -- 4.3.4 Graphical Error -- 4.4 Map Transformations -- 4.4.1 Map Morphing -- 4.4.1.1 Metro Map Format -- 4.4.1.2 Station to Station Transition -- 4.4.1.3 Connection to Connection Transition -- 4.4.2 Splitting Overlapped Segments -- 4.4.3 Reactive Circle Scaling -- 4.5 Interactive Application -- 4.5.1 General Overview -- 4.5.2 Circular Diagram -- 4.6 Critical Discussion -- 4.7 Conclusions -- Acknowledgments -- References -- 5: Smart Bike-Sharing Systems for Smart Cities -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Bike Availability Prediction 5.2.2 Using Clustering to Explore Data Trends -- 5.2.3 Rebalancing -- 5.3 Data Set -- 5.4 Bike Prediction Models and Results -- 5.4.1 Univariate Models -- 5.4.2 Multivariate Models -- 5.5 Supervised Clustering -- 5.5.1 Proposed Algorithm -- 5.5.2 Model Order Selection -- 5.5.3 Day of the Week -- 5.5.4 Hour of the Day -- 5.6 Rebalancing -- 5.6.1 Problem Statement -- 5.7 Proposed Algorithm -- 5.7.1 Tour Construction Using the Deferred Acceptance Algorithm -- 5.7.2 Tour Improvement Using 2-Opt Local Search Algorithm -- 5.7.3 Tour Construction Example -- 5.8 Results -- 5.8.1 San Francesco Bay Area Instances -- 5.9 Conclusions -- Acknowledgments -- References -- 6: Indirect Monitoring of Critical Transport Infrastructure: Data Analytics and Signal Processing -- 6.1 Introduction -- 6.2 Indirect Monitoring of Transport Infrastructure -- 6.3 Road Pavement Applications -- 6.3.1 Foundation Quality -- 6.3.2 Road Cracking and Surface Roughness -- 6.3.3 Pavement Stiffness -- 6.4 Railway Track Applications -- 6.5 Bridge Applications -- 6.6 Vehicle Management -- 6.7 Data Analytics -- 6.7.1 Internet of Things -- 6.7.2 Data Analytics Challenges -- 6.8 Conclusion -- Acknowledgment -- References -- 7: Big Data Exploration to Examine Aggressive Driving Behavior in the Era of Smart Cities -- 7.1 Introduction -- 7.2 Data Description -- 7.3 Methodology -- 7.3.1 Data Partitioning -- 7.3.2 Data Extraction -- 7.3.3 Knowledge Discovery -- 7.3.3.1 K-Means Method -- 7.3.3.2 Scenario Development -- 7.3.3.3 Variable Selection -- 7.4 Results -- 7.4.1 Cluster Determination -- 7.4.2 Hot Spot Identification -- 7.4.3 Impact of Trip Travel Time -- 7.5 Conclusions -- References -- 8: Exploratory Analysis of Run-Off-Road Crash Patterns -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Method and Data -- 8.3.1 Multiple Correspondence Analysis -- 8.3.1.1 Cloud of Individuals 8.3.1.2 Cloud of Categories -- 8.3.2 Data Collection -- 8.4 Results and Discussions -- 8.5 Conclusion -- References -- 9: Predicting Traffic Safety Risk Factors Using an Ensemble Classifier -- 9.1 Introduction -- 9.2 Problem Statement -- 9.3 Method -- 9.4 Classification of Event Data -- 9.4.1 Multinomial Logistic Regression (MLR) -- 9.4.2 Random Forest (RF) -- 9.4.3 Method Selection -- 9.4.4 Multivariate Time Series Random Forest (MTS-RF) -- 9.5 Data -- 9.6 Results and Discussions -- 9.7 Conclusions and Recommendations -- 9.8 Practical Applications -- Disclaimer -- References -- 10: Architecture Design of Internet of Things-Enabled Cloud Platform for Managing the Production of Prefabricated Public Houses -- 10.1 Introduction -- 10.1.1 Housing Concerns in Shenzhen and Advantages of Prefabricated Public Houses -- 10.1.2 Existing Technical Challenges -- 10.1.3 Internet of Things-Enabled Cloud Platform -- 10.2 Research Background -- 10.2.1 Massive Production of Prefabricated Public Houses in Shenzhen, Advantages of Prefabrication and Policies to Promote Prefabrication -- 10.2.2 Technical Challenges of Managing Massive Production of Prefabricated Public Houses -- 10.3 Architecture Design for Internet of Things-Enabled Cloud Platform -- 10.3.1 Processes and Function Requirements -- 10.3.2 Service-Oriented Architecture Design -- 10.3.3 Gateway Operation System and Quick Response Code for Defining Intelligent Building Elements -- 10.3.4 Beidou and GIS Integrated Technologies for Positioning Intelligent Building Elements -- 10.3.5 Dynamic nD BIM for Visualizing the Whole Processes of Prefabrication Construction Processes -- 10.3.6 Data Source Management Service through Big Data Analytics Systems -- 10.4 Benefits to Stakeholders Involved in Massive Production of Prefabricated Public Houses -- 10.4.1 Benefits and Marketing Opportunities for Clients 10.4.2 Benefits and Marketing Opportunities for Contractors -- 10.4.3 Benefits and Marketing Opportunities for Prefabrication Manufacturers -- 10.4.4 Benefits and Marketing Opportunities for Third-Party Logistics Firms -- 10.4.5 Benefits and Marketing Opportunities for IT Vendors -- 10.5 Discussion and Future Research -- References -- Index Description based on publisher supplied metadata and other sources |
| abstractGer |
Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- 1: Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition Assessment -- 1.1 Introduction -- 1.2 Smartphone-Driven Assessment of Airport Pavement Condition -- 1.2.1 Description of Smartphone Application -- 1.2.2 Smartphone Characteristics -- 1.3 Case Study of Missouri Airports -- 1.3.1 Calibration Study -- 1.3.2 Missouri Airport Smartphone Data Collection Methodology -- 1.3.3 Missouri Airport Smartphone Data Collection Results for Each Airport -- 1.3.4 Discussion -- 1.4 Prediction of PCI Based on Smartphone-Measured IRI -- 1.4.1 Machine Learning Method -- 1.4.2 GEP-Based Formulation of PCI -- 1.5 Conclusions -- Acknowledgments -- References -- 2: Global Satellite Observations for Smart Cities -- 2.1 Introduction -- 2.2 Overview of NASA Satellite-Based Global Data Products for Smart Cities -- 2.2.1 Satellite-Based Data Products at the GES DISC -- 2.2.1.1 Multi-Satellite and Multi-Sensor Merged Global Precipitation Products -- 2.2.1.2 Global and Regional Land Data Assimilation Products -- 2.2.1.3 Modern-Era Retrospective Analysis for Research and Applications (MERRA) Products -- 2.3 Data Services -- 2.3.1 Point-and-Click Online Tools -- 2.3.1.1 NASA's Worldview -- 2.3.1.2 NASA GES DISC Giovanni -- 2.3.2 Data Rod Services -- 2.3.3 Other Web Data Services -- 2.4 Examples -- 2.4.1 The Pearl River Delta -- 2.4.1.1 Typhoon Nida Rainfall -- 2.4.1.2 Atmospheric Composition Preliminary Analysis -- 2.4.2 Estimation of Hurricane Contribution to Annual Precipitation in Maryland -- 2.4.2.1 Data and Methods -- 2.4.2.2 Preliminary Results -- 2.5 Summary and Future Plans -- Acknowledgments -- References -- 3: Advancing Smart and Resilient Cities with Big Spatial Disaster Data -- 3.1 Introduction 3.2 The Role of Spatial Data in Coastal Resilience Applications -- 3.2.1 Disaster Management Cycle -- 3.2.2 Data Acquisition -- 3.2.3 Challenges and Opportunities -- 3.3 A Hurricane Sandy Inspired Big Data Framework for Coastal Resilience Investigations with Heterogeneous Spatial Data -- 3.3.1 Geospatial Response to Hurricane Sandy -- 3.3.2 Data Analytic Framework -- 3.3.3 Anatomy of Big Spatial Disaster Data -- 3.3.3.1 Volume -- 3.3.3.2 Data Structure -- 3.3.3.3 Spatial Completeness -- 3.3.3.4 Veracity -- 3.3.3.5 Velocity -- 3.3.4 Decomposition of Processing Tasks -- 3.3.4.1 Digital Elevation Models -- 3.3.4.2 Feature Extraction -- 3.3.4.3 Change Detection -- 3.3.4.4 Core Operation Categories -- 3.3.5 Identify the Uncertainty Associated with Big Data Acquisition and Processing -- 3.3.6 Computing with Big Data Infrastructure -- 3.3.7 Connecting Data Processing with Decision-Making Models -- 3.3.8 Future Improvement -- 3.4 Conclusion -- References -- 4: Smart City Portrayal -- 4.1 Introduction -- 4.2 Background and Related Work -- 4.2.1 Point Representation -- 4.2.2 Geographic Generalization -- 4.2.3 Heatmap -- 4.2.4 Circular Plot -- 4.2.5 Schematic Map -- 4.3 Point Representation: DESIGN STUDY -- 4.3.1 Concept and Formalization -- 4.3.2 Color-Coding -- 4.3.3 Implementation -- 4.3.4 Graphical Error -- 4.4 Map Transformations -- 4.4.1 Map Morphing -- 4.4.1.1 Metro Map Format -- 4.4.1.2 Station to Station Transition -- 4.4.1.3 Connection to Connection Transition -- 4.4.2 Splitting Overlapped Segments -- 4.4.3 Reactive Circle Scaling -- 4.5 Interactive Application -- 4.5.1 General Overview -- 4.5.2 Circular Diagram -- 4.6 Critical Discussion -- 4.7 Conclusions -- Acknowledgments -- References -- 5: Smart Bike-Sharing Systems for Smart Cities -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Bike Availability Prediction 5.2.2 Using Clustering to Explore Data Trends -- 5.2.3 Rebalancing -- 5.3 Data Set -- 5.4 Bike Prediction Models and Results -- 5.4.1 Univariate Models -- 5.4.2 Multivariate Models -- 5.5 Supervised Clustering -- 5.5.1 Proposed Algorithm -- 5.5.2 Model Order Selection -- 5.5.3 Day of the Week -- 5.5.4 Hour of the Day -- 5.6 Rebalancing -- 5.6.1 Problem Statement -- 5.7 Proposed Algorithm -- 5.7.1 Tour Construction Using the Deferred Acceptance Algorithm -- 5.7.2 Tour Improvement Using 2-Opt Local Search Algorithm -- 5.7.3 Tour Construction Example -- 5.8 Results -- 5.8.1 San Francesco Bay Area Instances -- 5.9 Conclusions -- Acknowledgments -- References -- 6: Indirect Monitoring of Critical Transport Infrastructure: Data Analytics and Signal Processing -- 6.1 Introduction -- 6.2 Indirect Monitoring of Transport Infrastructure -- 6.3 Road Pavement Applications -- 6.3.1 Foundation Quality -- 6.3.2 Road Cracking and Surface Roughness -- 6.3.3 Pavement Stiffness -- 6.4 Railway Track Applications -- 6.5 Bridge Applications -- 6.6 Vehicle Management -- 6.7 Data Analytics -- 6.7.1 Internet of Things -- 6.7.2 Data Analytics Challenges -- 6.8 Conclusion -- Acknowledgment -- References -- 7: Big Data Exploration to Examine Aggressive Driving Behavior in the Era of Smart Cities -- 7.1 Introduction -- 7.2 Data Description -- 7.3 Methodology -- 7.3.1 Data Partitioning -- 7.3.2 Data Extraction -- 7.3.3 Knowledge Discovery -- 7.3.3.1 K-Means Method -- 7.3.3.2 Scenario Development -- 7.3.3.3 Variable Selection -- 7.4 Results -- 7.4.1 Cluster Determination -- 7.4.2 Hot Spot Identification -- 7.4.3 Impact of Trip Travel Time -- 7.5 Conclusions -- References -- 8: Exploratory Analysis of Run-Off-Road Crash Patterns -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Method and Data -- 8.3.1 Multiple Correspondence Analysis -- 8.3.1.1 Cloud of Individuals 8.3.1.2 Cloud of Categories -- 8.3.2 Data Collection -- 8.4 Results and Discussions -- 8.5 Conclusion -- References -- 9: Predicting Traffic Safety Risk Factors Using an Ensemble Classifier -- 9.1 Introduction -- 9.2 Problem Statement -- 9.3 Method -- 9.4 Classification of Event Data -- 9.4.1 Multinomial Logistic Regression (MLR) -- 9.4.2 Random Forest (RF) -- 9.4.3 Method Selection -- 9.4.4 Multivariate Time Series Random Forest (MTS-RF) -- 9.5 Data -- 9.6 Results and Discussions -- 9.7 Conclusions and Recommendations -- 9.8 Practical Applications -- Disclaimer -- References -- 10: Architecture Design of Internet of Things-Enabled Cloud Platform for Managing the Production of Prefabricated Public Houses -- 10.1 Introduction -- 10.1.1 Housing Concerns in Shenzhen and Advantages of Prefabricated Public Houses -- 10.1.2 Existing Technical Challenges -- 10.1.3 Internet of Things-Enabled Cloud Platform -- 10.2 Research Background -- 10.2.1 Massive Production of Prefabricated Public Houses in Shenzhen, Advantages of Prefabrication and Policies to Promote Prefabrication -- 10.2.2 Technical Challenges of Managing Massive Production of Prefabricated Public Houses -- 10.3 Architecture Design for Internet of Things-Enabled Cloud Platform -- 10.3.1 Processes and Function Requirements -- 10.3.2 Service-Oriented Architecture Design -- 10.3.3 Gateway Operation System and Quick Response Code for Defining Intelligent Building Elements -- 10.3.4 Beidou and GIS Integrated Technologies for Positioning Intelligent Building Elements -- 10.3.5 Dynamic nD BIM for Visualizing the Whole Processes of Prefabrication Construction Processes -- 10.3.6 Data Source Management Service through Big Data Analytics Systems -- 10.4 Benefits to Stakeholders Involved in Massive Production of Prefabricated Public Houses -- 10.4.1 Benefits and Marketing Opportunities for Clients 10.4.2 Benefits and Marketing Opportunities for Contractors -- 10.4.3 Benefits and Marketing Opportunities for Prefabrication Manufacturers -- 10.4.4 Benefits and Marketing Opportunities for Third-Party Logistics Firms -- 10.4.5 Benefits and Marketing Opportunities for IT Vendors -- 10.5 Discussion and Future Research -- References -- Index Description based on publisher supplied metadata and other sources |
| abstract_unstemmed |
Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- 1: Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition Assessment -- 1.1 Introduction -- 1.2 Smartphone-Driven Assessment of Airport Pavement Condition -- 1.2.1 Description of Smartphone Application -- 1.2.2 Smartphone Characteristics -- 1.3 Case Study of Missouri Airports -- 1.3.1 Calibration Study -- 1.3.2 Missouri Airport Smartphone Data Collection Methodology -- 1.3.3 Missouri Airport Smartphone Data Collection Results for Each Airport -- 1.3.4 Discussion -- 1.4 Prediction of PCI Based on Smartphone-Measured IRI -- 1.4.1 Machine Learning Method -- 1.4.2 GEP-Based Formulation of PCI -- 1.5 Conclusions -- Acknowledgments -- References -- 2: Global Satellite Observations for Smart Cities -- 2.1 Introduction -- 2.2 Overview of NASA Satellite-Based Global Data Products for Smart Cities -- 2.2.1 Satellite-Based Data Products at the GES DISC -- 2.2.1.1 Multi-Satellite and Multi-Sensor Merged Global Precipitation Products -- 2.2.1.2 Global and Regional Land Data Assimilation Products -- 2.2.1.3 Modern-Era Retrospective Analysis for Research and Applications (MERRA) Products -- 2.3 Data Services -- 2.3.1 Point-and-Click Online Tools -- 2.3.1.1 NASA's Worldview -- 2.3.1.2 NASA GES DISC Giovanni -- 2.3.2 Data Rod Services -- 2.3.3 Other Web Data Services -- 2.4 Examples -- 2.4.1 The Pearl River Delta -- 2.4.1.1 Typhoon Nida Rainfall -- 2.4.1.2 Atmospheric Composition Preliminary Analysis -- 2.4.2 Estimation of Hurricane Contribution to Annual Precipitation in Maryland -- 2.4.2.1 Data and Methods -- 2.4.2.2 Preliminary Results -- 2.5 Summary and Future Plans -- Acknowledgments -- References -- 3: Advancing Smart and Resilient Cities with Big Spatial Disaster Data -- 3.1 Introduction 3.2 The Role of Spatial Data in Coastal Resilience Applications -- 3.2.1 Disaster Management Cycle -- 3.2.2 Data Acquisition -- 3.2.3 Challenges and Opportunities -- 3.3 A Hurricane Sandy Inspired Big Data Framework for Coastal Resilience Investigations with Heterogeneous Spatial Data -- 3.3.1 Geospatial Response to Hurricane Sandy -- 3.3.2 Data Analytic Framework -- 3.3.3 Anatomy of Big Spatial Disaster Data -- 3.3.3.1 Volume -- 3.3.3.2 Data Structure -- 3.3.3.3 Spatial Completeness -- 3.3.3.4 Veracity -- 3.3.3.5 Velocity -- 3.3.4 Decomposition of Processing Tasks -- 3.3.4.1 Digital Elevation Models -- 3.3.4.2 Feature Extraction -- 3.3.4.3 Change Detection -- 3.3.4.4 Core Operation Categories -- 3.3.5 Identify the Uncertainty Associated with Big Data Acquisition and Processing -- 3.3.6 Computing with Big Data Infrastructure -- 3.3.7 Connecting Data Processing with Decision-Making Models -- 3.3.8 Future Improvement -- 3.4 Conclusion -- References -- 4: Smart City Portrayal -- 4.1 Introduction -- 4.2 Background and Related Work -- 4.2.1 Point Representation -- 4.2.2 Geographic Generalization -- 4.2.3 Heatmap -- 4.2.4 Circular Plot -- 4.2.5 Schematic Map -- 4.3 Point Representation: DESIGN STUDY -- 4.3.1 Concept and Formalization -- 4.3.2 Color-Coding -- 4.3.3 Implementation -- 4.3.4 Graphical Error -- 4.4 Map Transformations -- 4.4.1 Map Morphing -- 4.4.1.1 Metro Map Format -- 4.4.1.2 Station to Station Transition -- 4.4.1.3 Connection to Connection Transition -- 4.4.2 Splitting Overlapped Segments -- 4.4.3 Reactive Circle Scaling -- 4.5 Interactive Application -- 4.5.1 General Overview -- 4.5.2 Circular Diagram -- 4.6 Critical Discussion -- 4.7 Conclusions -- Acknowledgments -- References -- 5: Smart Bike-Sharing Systems for Smart Cities -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Bike Availability Prediction 5.2.2 Using Clustering to Explore Data Trends -- 5.2.3 Rebalancing -- 5.3 Data Set -- 5.4 Bike Prediction Models and Results -- 5.4.1 Univariate Models -- 5.4.2 Multivariate Models -- 5.5 Supervised Clustering -- 5.5.1 Proposed Algorithm -- 5.5.2 Model Order Selection -- 5.5.3 Day of the Week -- 5.5.4 Hour of the Day -- 5.6 Rebalancing -- 5.6.1 Problem Statement -- 5.7 Proposed Algorithm -- 5.7.1 Tour Construction Using the Deferred Acceptance Algorithm -- 5.7.2 Tour Improvement Using 2-Opt Local Search Algorithm -- 5.7.3 Tour Construction Example -- 5.8 Results -- 5.8.1 San Francesco Bay Area Instances -- 5.9 Conclusions -- Acknowledgments -- References -- 6: Indirect Monitoring of Critical Transport Infrastructure: Data Analytics and Signal Processing -- 6.1 Introduction -- 6.2 Indirect Monitoring of Transport Infrastructure -- 6.3 Road Pavement Applications -- 6.3.1 Foundation Quality -- 6.3.2 Road Cracking and Surface Roughness -- 6.3.3 Pavement Stiffness -- 6.4 Railway Track Applications -- 6.5 Bridge Applications -- 6.6 Vehicle Management -- 6.7 Data Analytics -- 6.7.1 Internet of Things -- 6.7.2 Data Analytics Challenges -- 6.8 Conclusion -- Acknowledgment -- References -- 7: Big Data Exploration to Examine Aggressive Driving Behavior in the Era of Smart Cities -- 7.1 Introduction -- 7.2 Data Description -- 7.3 Methodology -- 7.3.1 Data Partitioning -- 7.3.2 Data Extraction -- 7.3.3 Knowledge Discovery -- 7.3.3.1 K-Means Method -- 7.3.3.2 Scenario Development -- 7.3.3.3 Variable Selection -- 7.4 Results -- 7.4.1 Cluster Determination -- 7.4.2 Hot Spot Identification -- 7.4.3 Impact of Trip Travel Time -- 7.5 Conclusions -- References -- 8: Exploratory Analysis of Run-Off-Road Crash Patterns -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Method and Data -- 8.3.1 Multiple Correspondence Analysis -- 8.3.1.1 Cloud of Individuals 8.3.1.2 Cloud of Categories -- 8.3.2 Data Collection -- 8.4 Results and Discussions -- 8.5 Conclusion -- References -- 9: Predicting Traffic Safety Risk Factors Using an Ensemble Classifier -- 9.1 Introduction -- 9.2 Problem Statement -- 9.3 Method -- 9.4 Classification of Event Data -- 9.4.1 Multinomial Logistic Regression (MLR) -- 9.4.2 Random Forest (RF) -- 9.4.3 Method Selection -- 9.4.4 Multivariate Time Series Random Forest (MTS-RF) -- 9.5 Data -- 9.6 Results and Discussions -- 9.7 Conclusions and Recommendations -- 9.8 Practical Applications -- Disclaimer -- References -- 10: Architecture Design of Internet of Things-Enabled Cloud Platform for Managing the Production of Prefabricated Public Houses -- 10.1 Introduction -- 10.1.1 Housing Concerns in Shenzhen and Advantages of Prefabricated Public Houses -- 10.1.2 Existing Technical Challenges -- 10.1.3 Internet of Things-Enabled Cloud Platform -- 10.2 Research Background -- 10.2.1 Massive Production of Prefabricated Public Houses in Shenzhen, Advantages of Prefabrication and Policies to Promote Prefabrication -- 10.2.2 Technical Challenges of Managing Massive Production of Prefabricated Public Houses -- 10.3 Architecture Design for Internet of Things-Enabled Cloud Platform -- 10.3.1 Processes and Function Requirements -- 10.3.2 Service-Oriented Architecture Design -- 10.3.3 Gateway Operation System and Quick Response Code for Defining Intelligent Building Elements -- 10.3.4 Beidou and GIS Integrated Technologies for Positioning Intelligent Building Elements -- 10.3.5 Dynamic nD BIM for Visualizing the Whole Processes of Prefabrication Construction Processes -- 10.3.6 Data Source Management Service through Big Data Analytics Systems -- 10.4 Benefits to Stakeholders Involved in Massive Production of Prefabricated Public Houses -- 10.4.1 Benefits and Marketing Opportunities for Clients 10.4.2 Benefits and Marketing Opportunities for Contractors -- 10.4.3 Benefits and Marketing Opportunities for Prefabrication Manufacturers -- 10.4.4 Benefits and Marketing Opportunities for Third-Party Logistics Firms -- 10.4.5 Benefits and Marketing Opportunities for IT Vendors -- 10.5 Discussion and Future Research -- References -- Index Description based on publisher supplied metadata and other sources |
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Data Analytics for Smart Cities. |
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ind2="0"><subfield code="a">Data Analytics for Smart Cities.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Milton</subfield><subfield code="b">Auerbach Publications</subfield><subfield code="c">2018</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2019</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (255 pages)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield 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GEP-Based Formulation of PCI -- 1.5 Conclusions -- Acknowledgments -- References -- 2: Global Satellite Observations for Smart Cities -- 2.1 Introduction -- 2.2 Overview of NASA Satellite-Based Global Data Products for Smart Cities -- 2.2.1 Satellite-Based Data Products at the GES DISC -- 2.2.1.1 Multi-Satellite and Multi-Sensor Merged Global Precipitation Products -- 2.2.1.2 Global and Regional Land Data Assimilation Products -- 2.2.1.3 Modern-Era Retrospective Analysis for Research and Applications (MERRA) Products -- 2.3 Data Services -- 2.3.1 Point-and-Click Online Tools -- 2.3.1.1 NASA's Worldview -- 2.3.1.2 NASA GES DISC Giovanni -- 2.3.2 Data Rod Services -- 2.3.3 Other Web Data Services -- 2.4 Examples -- 2.4.1 The Pearl River Delta -- 2.4.1.1 Typhoon Nida Rainfall -- 2.4.1.2 Atmospheric Composition Preliminary Analysis -- 2.4.2 Estimation of Hurricane Contribution to Annual Precipitation in Maryland -- 2.4.2.1 Data and Methods -- 2.4.2.2 Preliminary Results -- 2.5 Summary and 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Processing -- 3.3.6 Computing with Big Data Infrastructure -- 3.3.7 Connecting Data Processing with Decision-Making Models -- 3.3.8 Future Improvement -- 3.4 Conclusion -- References -- 4: Smart City Portrayal -- 4.1 Introduction -- 4.2 Background and Related Work -- 4.2.1 Point Representation -- 4.2.2 Geographic Generalization -- 4.2.3 Heatmap -- 4.2.4 Circular Plot -- 4.2.5 Schematic Map -- 4.3 Point Representation: DESIGN STUDY -- 4.3.1 Concept and Formalization -- 4.3.2 Color-Coding -- 4.3.3 Implementation -- 4.3.4 Graphical Error -- 4.4 Map Transformations -- 4.4.1 Map Morphing -- 4.4.1.1 Metro Map Format -- 4.4.1.2 Station to Station Transition -- 4.4.1.3 Connection to Connection Transition -- 4.4.2 Splitting Overlapped Segments -- 4.4.3 Reactive Circle Scaling -- 4.5 Interactive Application -- 4.5.1 General Overview -- 4.5.2 Circular Diagram -- 4.6 Critical Discussion -- 4.7 Conclusions -- Acknowledgments -- References -- 5: Smart Bike-Sharing Systems for Smart Cities -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Bike Availability Prediction</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">5.2.2 Using Clustering to Explore Data Trends -- 5.2.3 Rebalancing -- 5.3 Data Set -- 5.4 Bike Prediction Models and Results -- 5.4.1 Univariate Models -- 5.4.2 Multivariate Models -- 5.5 Supervised Clustering -- 5.5.1 Proposed Algorithm -- 5.5.2 Model Order Selection -- 5.5.3 Day of the Week -- 5.5.4 Hour of the Day -- 5.6 Rebalancing -- 5.6.1 Problem Statement -- 5.7 Proposed Algorithm -- 5.7.1 Tour Construction Using the Deferred Acceptance Algorithm -- 5.7.2 Tour Improvement Using 2-Opt Local Search Algorithm -- 5.7.3 Tour Construction Example -- 5.8 Results -- 5.8.1 San Francesco Bay Area Instances -- 5.9 Conclusions -- Acknowledgments -- References -- 6: Indirect Monitoring of Critical Transport Infrastructure: Data Analytics and Signal Processing -- 6.1 Introduction -- 6.2 Indirect Monitoring of Transport Infrastructure -- 6.3 Road Pavement Applications -- 6.3.1 Foundation Quality -- 6.3.2 Road Cracking and Surface Roughness -- 6.3.3 Pavement Stiffness -- 6.4 Railway Track Applications -- 6.5 Bridge Applications -- 6.6 Vehicle Management -- 6.7 Data Analytics -- 6.7.1 Internet of Things -- 6.7.2 Data Analytics Challenges -- 6.8 Conclusion -- Acknowledgment -- References -- 7: Big Data Exploration to Examine Aggressive Driving Behavior in the Era of Smart Cities -- 7.1 Introduction -- 7.2 Data Description -- 7.3 Methodology -- 7.3.1 Data Partitioning -- 7.3.2 Data Extraction -- 7.3.3 Knowledge Discovery -- 7.3.3.1 K-Means Method -- 7.3.3.2 Scenario Development -- 7.3.3.3 Variable Selection -- 7.4 Results -- 7.4.1 Cluster Determination -- 7.4.2 Hot Spot Identification -- 7.4.3 Impact of Trip Travel Time -- 7.5 Conclusions -- References -- 8: Exploratory Analysis of Run-Off-Road Crash Patterns -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Method and Data -- 8.3.1 Multiple Correspondence Analysis -- 8.3.1.1 Cloud of Individuals</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">8.3.1.2 Cloud of Categories -- 8.3.2 Data Collection -- 8.4 Results and Discussions -- 8.5 Conclusion -- References -- 9: Predicting Traffic Safety Risk Factors Using an Ensemble Classifier -- 9.1 Introduction -- 9.2 Problem Statement -- 9.3 Method -- 9.4 Classification of Event Data -- 9.4.1 Multinomial Logistic Regression (MLR) -- 9.4.2 Random Forest (RF) -- 9.4.3 Method Selection -- 9.4.4 Multivariate Time Series Random Forest (MTS-RF) -- 9.5 Data -- 9.6 Results and Discussions -- 9.7 Conclusions and Recommendations -- 9.8 Practical Applications -- Disclaimer -- References -- 10: Architecture Design of Internet of Things-Enabled Cloud Platform for Managing the Production of Prefabricated Public Houses -- 10.1 Introduction -- 10.1.1 Housing Concerns in Shenzhen and Advantages of Prefabricated Public Houses -- 10.1.2 Existing Technical Challenges -- 10.1.3 Internet of Things-Enabled Cloud Platform -- 10.2 Research Background -- 10.2.1 Massive Production of Prefabricated Public Houses in Shenzhen, Advantages of Prefabrication and Policies to Promote Prefabrication -- 10.2.2 Technical Challenges of Managing Massive Production of Prefabricated Public Houses -- 10.3 Architecture Design for Internet of Things-Enabled Cloud Platform -- 10.3.1 Processes and Function Requirements -- 10.3.2 Service-Oriented Architecture Design -- 10.3.3 Gateway Operation System and Quick Response Code for Defining Intelligent Building Elements -- 10.3.4 Beidou and GIS Integrated Technologies for Positioning Intelligent Building Elements -- 10.3.5 Dynamic nD BIM for Visualizing the Whole Processes of Prefabrication Construction Processes -- 10.3.6 Data Source Management Service through Big Data Analytics Systems -- 10.4 Benefits to Stakeholders Involved in Massive Production of Prefabricated Public Houses -- 10.4.1 Benefits and Marketing Opportunities for 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Preface p. vii Editors p. ix Contributors p. xi Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition Assessment … Global Satellite Observations for Smart Cities … Advancing Smart and Resilient Cities with Big Spatial Disaster Data: Challenges, Progress, and Opportunities … Smart City Portrayal: Dynamic Visualization Applied to the Analysis of Underground Metro … Smart Bike-Sharing Systems for Smart Cities … Indirect Monitoring of Critical Transport Infrastructure: Data Analytics and Signal Processing … Big Data Exploration to Examine Aggressive Driving Behavior in the Era of Smart Cities … Exploratory Analysis of Run-Off-Road Crash Patterns … Predicting Traffic Safety Risk Factors Using an Ensemble Classifier … Architecture Design of Internet of Things-Enabled Cloud Platform for Managing the Production of Prefabricated Public Houses … Index … |
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ind2="0"><subfield code="a">Data Analytics for Smart Cities.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Milton</subfield><subfield code="b">Auerbach Publications</subfield><subfield code="c">2018</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2019</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (255 pages)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Data Analytics Applications Ser.</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- 1: Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition Assessment -- 1.1 Introduction -- 1.2 Smartphone-Driven Assessment of Airport Pavement Condition -- 1.2.1 Description of Smartphone Application -- 1.2.2 Smartphone Characteristics -- 1.3 Case Study of Missouri Airports -- 1.3.1 Calibration Study -- 1.3.2 Missouri Airport Smartphone Data Collection Methodology -- 1.3.3 Missouri Airport Smartphone Data Collection Results for Each Airport -- 1.3.4 Discussion -- 1.4 Prediction of PCI Based on Smartphone-Measured IRI -- 1.4.1 Machine Learning Method -- 1.4.2 GEP-Based Formulation of PCI -- 1.5 Conclusions -- Acknowledgments -- References -- 2: Global Satellite Observations for Smart Cities -- 2.1 Introduction -- 2.2 Overview of NASA Satellite-Based Global Data Products for Smart Cities -- 2.2.1 Satellite-Based Data Products at the GES DISC -- 2.2.1.1 Multi-Satellite and Multi-Sensor Merged Global Precipitation Products -- 2.2.1.2 Global and Regional Land Data Assimilation Products -- 2.2.1.3 Modern-Era Retrospective Analysis for Research and Applications (MERRA) Products -- 2.3 Data Services -- 2.3.1 Point-and-Click Online Tools -- 2.3.1.1 NASA's Worldview -- 2.3.1.2 NASA GES DISC Giovanni -- 2.3.2 Data Rod Services -- 2.3.3 Other Web Data Services -- 2.4 Examples -- 2.4.1 The Pearl River Delta -- 2.4.1.1 Typhoon Nida Rainfall -- 2.4.1.2 Atmospheric Composition Preliminary Analysis -- 2.4.2 Estimation of Hurricane Contribution to Annual Precipitation in Maryland -- 2.4.2.1 Data and Methods -- 2.4.2.2 Preliminary Results -- 2.5 Summary and Future Plans -- Acknowledgments -- References -- 3: Advancing Smart and Resilient Cities with Big Spatial Disaster Data -- 3.1 Introduction</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">3.2 The Role of Spatial Data in Coastal Resilience Applications -- 3.2.1 Disaster Management Cycle -- 3.2.2 Data Acquisition -- 3.2.3 Challenges and Opportunities -- 3.3 A Hurricane Sandy Inspired Big Data Framework for Coastal Resilience Investigations with Heterogeneous Spatial Data -- 3.3.1 Geospatial Response to Hurricane Sandy -- 3.3.2 Data Analytic Framework -- 3.3.3 Anatomy of Big Spatial Disaster Data -- 3.3.3.1 Volume -- 3.3.3.2 Data Structure -- 3.3.3.3 Spatial Completeness -- 3.3.3.4 Veracity -- 3.3.3.5 Velocity -- 3.3.4 Decomposition of Processing Tasks -- 3.3.4.1 Digital Elevation Models -- 3.3.4.2 Feature Extraction -- 3.3.4.3 Change Detection -- 3.3.4.4 Core Operation Categories -- 3.3.5 Identify the Uncertainty Associated with Big Data Acquisition and Processing -- 3.3.6 Computing with Big Data Infrastructure -- 3.3.7 Connecting Data Processing with Decision-Making Models -- 3.3.8 Future Improvement -- 3.4 Conclusion -- References -- 4: Smart City Portrayal -- 4.1 Introduction -- 4.2 Background and Related Work -- 4.2.1 Point Representation -- 4.2.2 Geographic Generalization -- 4.2.3 Heatmap -- 4.2.4 Circular Plot -- 4.2.5 Schematic Map -- 4.3 Point Representation: DESIGN STUDY -- 4.3.1 Concept and Formalization -- 4.3.2 Color-Coding -- 4.3.3 Implementation -- 4.3.4 Graphical Error -- 4.4 Map Transformations -- 4.4.1 Map Morphing -- 4.4.1.1 Metro Map Format -- 4.4.1.2 Station to Station Transition -- 4.4.1.3 Connection to Connection Transition -- 4.4.2 Splitting Overlapped Segments -- 4.4.3 Reactive Circle Scaling -- 4.5 Interactive Application -- 4.5.1 General Overview -- 4.5.2 Circular Diagram -- 4.6 Critical Discussion -- 4.7 Conclusions -- Acknowledgments -- References -- 5: Smart Bike-Sharing Systems for Smart Cities -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Bike Availability Prediction</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">5.2.2 Using Clustering to Explore Data Trends -- 5.2.3 Rebalancing -- 5.3 Data Set -- 5.4 Bike Prediction Models and Results -- 5.4.1 Univariate Models -- 5.4.2 Multivariate Models -- 5.5 Supervised Clustering -- 5.5.1 Proposed Algorithm -- 5.5.2 Model Order Selection -- 5.5.3 Day of the Week -- 5.5.4 Hour of the Day -- 5.6 Rebalancing -- 5.6.1 Problem Statement -- 5.7 Proposed Algorithm -- 5.7.1 Tour Construction Using the Deferred Acceptance Algorithm -- 5.7.2 Tour Improvement Using 2-Opt Local Search Algorithm -- 5.7.3 Tour Construction Example -- 5.8 Results -- 5.8.1 San Francesco Bay Area Instances -- 5.9 Conclusions -- Acknowledgments -- References -- 6: Indirect Monitoring of Critical Transport Infrastructure: Data Analytics and Signal Processing -- 6.1 Introduction -- 6.2 Indirect Monitoring of Transport Infrastructure -- 6.3 Road Pavement Applications -- 6.3.1 Foundation Quality -- 6.3.2 Road Cracking and Surface Roughness -- 6.3.3 Pavement Stiffness -- 6.4 Railway Track Applications -- 6.5 Bridge Applications -- 6.6 Vehicle Management -- 6.7 Data Analytics -- 6.7.1 Internet of Things -- 6.7.2 Data Analytics Challenges -- 6.8 Conclusion -- Acknowledgment -- References -- 7: Big Data Exploration to Examine Aggressive Driving Behavior in the Era of Smart Cities -- 7.1 Introduction -- 7.2 Data Description -- 7.3 Methodology -- 7.3.1 Data Partitioning -- 7.3.2 Data Extraction -- 7.3.3 Knowledge Discovery -- 7.3.3.1 K-Means Method -- 7.3.3.2 Scenario Development -- 7.3.3.3 Variable Selection -- 7.4 Results -- 7.4.1 Cluster Determination -- 7.4.2 Hot Spot Identification -- 7.4.3 Impact of Trip Travel Time -- 7.5 Conclusions -- References -- 8: Exploratory Analysis of Run-Off-Road Crash Patterns -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Method and Data -- 8.3.1 Multiple Correspondence Analysis -- 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Things-Enabled Cloud Platform -- 10.2 Research Background -- 10.2.1 Massive Production of Prefabricated Public Houses in Shenzhen, Advantages of Prefabrication and Policies to Promote Prefabrication -- 10.2.2 Technical Challenges of Managing Massive Production of Prefabricated Public Houses -- 10.3 Architecture Design for Internet of Things-Enabled Cloud Platform -- 10.3.1 Processes and Function Requirements -- 10.3.2 Service-Oriented Architecture Design -- 10.3.3 Gateway Operation System and Quick Response Code for Defining Intelligent Building Elements -- 10.3.4 Beidou and GIS Integrated Technologies for Positioning Intelligent Building Elements -- 10.3.5 Dynamic nD BIM for Visualizing the Whole Processes of Prefabrication Construction Processes -- 10.3.6 Data Source Management Service through Big Data Analytics Systems -- 10.4 Benefits to Stakeholders Involved in Massive Production of Prefabricated Public Houses -- 10.4.1 Benefits and Marketing Opportunities for 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