Urban Computing

by Zheng

ISBN: 9780262039086 | Copyright 0

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An authoritative treatment of urban computing, offering an overview of the field, fundamental techniques, advanced models, and novel applications.

Urban computing brings powerful computational techniques to bear on such urban challenges as pollution, energy consumption, and traffic congestion. Using today's large-scale computing infrastructure and data gathered from sensing technologies, urban computing combines computer science with urban planning, transportation, environmental science, sociology, and other areas of urban studies, tackling specific problems with concrete methodologies in a data-centric computing framework. This authoritative treatment of urban computing offers an overview of the field, fundamental techniques, advanced models, and novel applications.

Each chapter acts as a tutorial that introduces readers to an important aspect of urban computing, with references to relevant research. The book outlines key concepts, sources of data, and typical applications; describes four paradigms of urban sensing in sensor-centric and human-centric categories; introduces data management for spatial and spatio-temporal data, from basic indexing and retrieval algorithms to cloud computing platforms; and covers beginning and advanced topics in mining knowledge from urban big data, beginning with fundamental data mining algorithms and progressing to advanced machine learning techniques. Urban Computing provides students, researchers, and application developers with an essential handbook to an evolving interdisciplinary field.

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Contents (pg. v)
Preface (pg. xiii)
Acknowledgments (pg. xxi)
I. Concepts and Framework (pg. 1)
1. Overview (pg. 3)
1.1 Introduction (pg. 3)
1.2 Definition of Urban Computing (pg. 4)
1.3 General Framework (pg. 5)
1.4 Key Urban-Computing Challenges (pg. 10)
1.5 Urban Data (pg. 28)
1.6 Public Datasets (pg. 39)
References (pg. 41)
2. Urban-Computing Applications (pg. 49)
2.1 Introduction (pg. 49)
2.2 Urban Computing for Urban Planning (pg. 49)
2.3 Urban Computing for Transportation Systems& (pg. 58)
2.4 Urban Computing for the Environment (pg. 70)
2.5 Urban Computing for Urban Energy Consumption (pg. 81)
2.6 Urban Computing for Social Applications (pg. 84)
2.7 Urban Computing for the Economy (pg. 88)
2.8 Urban Computing for Public Safety and Security& (pg. 91)
2.9 Summary (pg. 96)
References (pg. 97)
II. Urban Sensing and Data Acquisition (pg. 113)
3. Urban Sensing (pg. 115)
3.1 Introduction (pg. 115)
3.2 Sensor and Facility Deployment (pg. 121)
3.3 Human-Centric Urban Sensing (pg. 133)
3.4 Filling Missing Values (pg. 139)
3.5 Summary (pg. 150)
References (pg. 150)
III. Urban Data Management (pg. 155)
4. Spatiotemporal Data Management (pg. 157)
4.1 Introduction (pg. 157)
4.2 Data Structures (pg. 161)
4.3 Spatial Data Management (pg. 167)
4.4 Spatiotemporal Data Management (pg. 180)
4.5 Hybrid Indexes for Managing Multiple Datasets&# (pg. 198)
4.6 Summary (pg. 213)
References (pg. 214)
5. Introduction to Cloud Computing (pg. 219)
5.1 Introduction (pg. 219)
5.2 Storage (pg. 221)
5.3 Computing (pg. 235)
5.4 Applications (pg. 255)
5.5 Summary (pg. 259)
References (pg. 259)
6. Managing Spatiotemporal Data in the Cloud (pg. 263)
6.1 Introduction (pg. 263)
6.2 Managing Point-Based Data (pg. 267)
6.3 Managing Network-Based Data (pg. 284)
6.4 Urban Big-Data Platform (pg. 301)
6.5 Summary (pg. 303)
IV. Urban Data Analytics (pg. 305)
7. Fundamental Data-Mining Techniques for Urban Data (pg. 307)
7.1 Introduction (pg. 307)
7.2 Data Preprocessing (pg. 314)
7.3 Frequent Pattern Mining and Association Rules&# (pg. 326)
7.4 Clustering (pg. 345)
7.5 Classification (pg. 356)
7.6 Regression (pg. 370)
7.7 Outlier and Anomaly Detection (pg. 376)
7.8 Summary (pg. 384)
References (pg. 385)
8. Advanced Machine-Learning Techniques for Spatiotemporal Data (pg. 391)
8.1 Introduction (pg. 391)
8.2 Unique Properties of Spatiotemporal Data (pg. 392)
8.3 Collaborative Filtering (pg. 396)
8.4 Matrix Factorization (pg. 404)
8.5 Tensor Decomposition (pg. 411)
8.6 Probabilistic Graphical Models (pg. 421)
8.7 Deep Learning (pg. 454)
8.8 Reinforcement Learning (pg. 471)
8.9 Summary (pg. 484)
References (pg. 485)
9. Cross-Domain Knowledge Fusion (pg. 491)
9.1 Introduction (pg. 491)
9.2 Stage-Based Knowledge Fusion (pg. 495)
9.3 Feature-Based Knowledge Fusion (pg. 498)
9.4 Semantic Meaning–Based Knowledge Fusion (pg. 504)
9.5 Comparison between Different Fusion Methods (pg. 526)
9.6 Summary (pg. 529)
References (pg. 530)
10. Advanced Topics in Urban Data Analytics (pg. 535)
10.1 How to Select Useful Datasets (pg. 535)
10.2 Trajectory Data Mining (pg. 542)
10.3 Combining Machine Learning with Data Management (pg. 576)
10.4 Interactive Visual Data Analytics (pg. 587)
10.5 Summary (pg. 588)
References (pg. 589)
About the Author (pg. 599)
Index (pg. 601)

Yu Zheng

Yu Zheng is Vice President and Chief Data Scientist at JD Finance, where he is also President of the Urban Computing Business Unit and Director of the Urban Computing Lab. He is Chair Professor at Shanghai Jiao Tong University and Adjunct Professor at Hong Kong University of Science and Technology. He is the coeditor of Computing with Spatial Technologies and Editor-in-Chief of ACM Transactions on Intelligent Systems and Technology.

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