| Description |
1 online resource (255 pages) |
| Series |
Data Analytics Applications Ser |
|
Data analytics applications.
|
| Contents |
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 IRI1.4.1 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 Services2.3.1 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 Applications3.2.1 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 Extraction3.3.4.3 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 |
| Summary |
The development of smart cities is one of the most important challenges over the next few decades. Governments and companies are leveraging billions of dollars in public and private funds for smart cities. Next generation smart cities are heavily dependent on distributed smart sensing systems and devices to monitor the urban infrastructure. The smart sensor networks serve as autonomous intelligent nodes to measure a variety of physical or environmental parameters. They should react in time, establish automated control, and collect information for intelligent decision-making. In this context, one of the major tasks is to develop advanced frameworks for the interpretation of the huge amount of information provided by the emerging testing and monitoring systems. Data Analytics for Smart Cities brings together some of the most exciting new developments in the area of integrating advanced data analytics systems into smart cities along with complementary technological paradigms such as cloud computing and Internet of Things (IoT). The book serves as a reference for researchers and engineers in domains of advanced computation, optimization, and data mining for smart civil infrastructure condition assessment, dynamic visualization, intelligent transportation systems (ITS), cyber-physical systems, and smart construction technologies. The chapters are presented in a hands-on manner to facilitate researchers in tackling applications. Arguably, data analytics technologies play a key role in tackling the challenge of creating smart cities. Data analytics applications involve collecting, integrating, and preparing time- and space-dependent data produced by sensors, complex engineered systems, and physical assets, followed by developing and testing analytical models to verify the accuracy of results. This book covers this multidisciplinary field and examines multiple paradigms such as machine learning, pattern recognition, statistics, intelligent databases, knowledge acquisition, data visualization, high performance computing, and expert systems. The book explores new territory by discussing the cutting-edge concept of Big Data analytics for interpreting massive amounts of data in smart city applications |
| Notes |
4.3.2 Color-Coding |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Dr. Amir H. Alavi is an Assistant Professor with a joint appointment in the Civil and Environmental Engineering Department at the University of Missouri-Columbia and the University of Missouri Extension Business Development Program, and holds a courtesy appointment in the Department of Bioengineering. His multidisciplinary research integrates sensing, computation, control, networking, and information systems into the civil infrastructure to create cyber-physical infrastructure systems. Dr. Alavi's research interests include smart cities, structural health monitoring, deployment of advanced sensors, energy harvesting, and civil engineering system informatics. He has worked on research projects supported by Federal Highway Administration (FHWA), National Science Foundation (NSF), Missouri DOT, and Michigan DOT. Dr. Alavi has authored 5 books and over 170 publications in archival journals, book chapters, and conference proceedings. He has received a number of award certificates for his journal articles. Recently, he has been selected among the Google Scholar 300 Most Cited Authors in Civil Engineering, as well as Web of Science ESI's World Top 1% Scientific Minds. He has served as the editor/guest editor of several journals such as Case Studies in Construction Material, Automation in Construction, Geoscience Frontiers, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, and Advances in Mechanical Engineering. Dr. Alavi received his PhD degree in Civil Engineering from Michigan State University (MSU). He also holds a MSc and BSc in Civil Engineering from Iran University of Science & Technology. Dr. William G. Buttlar is the Glen Barton Chair in Flexible Pavements at the University of Missouri (MU). He has over 100 peer-reviewed journal articles and nearly 300 total publications in the areas of pavements, materials, and smart infrastructure. Prior to joining the faculty at MU in 2016, he was a faculty member at the University of Illinois at Urbana-Champaign (UIUC) for 20 years, with 5 years of administrative experience serving as the Associate Dean of the UIUC Graduate College for Science and Engineering Programs, and Associate Dean of Graduate Programs for the College of Engineering. He was also the lead faculty member behind the establishment of City Digital at UILabs in Chicago |
|
Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force. WlAbNL |
|
Print version record |
| Subject |
Smart cities.
|
|
Big data.
|
|
Quantitative research.
|
|
COMPUTERS -- Database Management -- Data Mining.
|
|
MATHEMATICS -- Probability & Statistics -- General.
|
|
TECHNOLOGY -- Electronics -- General.
|
|
Big data
|
|
Quantitative research
|
|
Smart cities
|
| Form |
Electronic book
|
| Author |
Alavi, Amir
|
|
Buttlar, William G.
|
| ISBN |
9780429786631 |
|
0429786638 |
|
9780429434983 |
|
0429434987 |
|
9780429786617 |
|
0429786611 |
|
9780429786624 |
|
042978662X |
|
