Urban Informatics

Acknowledgements
Contents
About the Editors
1 Overall Introduction
	1.1 Defining Urban Informatics
	1.2 The Background: The Origins of Urban Informatics
	1.3 Structure of the Book
	1.4 Retrospective and Prospective
	References
Part IDimensions of Urban Science
2 Introduction to Urban Science
3 Defining Urban Science
	3.1 A Science of Cities
	3.2 City Systems and Systems of Cities
	3.3 Urban Growth: Urbanization from the Bottom Up
	3.4 Scale and Size, Networks, and Flows
	3.5 The Development of Operational Urban Models
	3.6 Future Directions in Urban Informatics
	References
4 Street View Imaging for Automated Assessments of Urban Infrastructure and Services
	4.1 Introduction
	4.2 Data Collection and Object Localization
	4.3 Deriving Urban Functions from Object Statistics
	4.4 Discussion
	References
5 Urban Human Dynamics
	5.1 Introduction
	5.2 Urban Dynamics
		5.2.1 Cellular Automata for Urban Dynamics Research
		5.2.2 Other Urban Dynamics Approaches
	5.3 Human Dynamics
		5.3.1 Effects of Information and Communications Technologies on Human Dynamics
		5.3.2 Time Geography
		5.3.3 Big Data and Space-Time GIS for Human Dynamics Research
		5.3.4 Some Other Examples Human Dynamics Studies
	5.4 Urban Human Dynamics and Urban Informatics
	References
6 Geosmartness for Personalized and Sustainable Future Urban Mobility
	6.1 Introduction
	6.2 Geosmartness
	6.3 Analyzing Urban-Mobility Patterns
		6.3.1 Data
		6.3.2 Computational Methods for Large-Scale Spatio-temporal Mobility-Pattern Analysis
		6.3.3 Studies
		6.3.4 SBB Green Class (Multi-modal and Energy-Efficient Mobility)
	6.4 Behavioral Change and Sustainable Mobility
		6.4.1 Motivation
		6.4.2 Detecting and Supporting Behavioral Change
		6.4.3 Studies
		6.4.4 GoEco!
	6.5 Mobile Decision Making
		6.5.1 Mobile Eye-Tracking and Gaze-Based Interaction
		6.5.2 Personalized Gaze-Based Decision Support
	6.6 Conclusions and Future Work
	References
7 Urban Metabolism
	7.1 Introduction
	7.2 History of Urban Metabolism
	7.3 Methods of Urban Metabolism
		7.3.1 Bottom-Up Methods
		7.3.2 Top-Down Methods
		7.3.3 Hybrid Methods
	7.4 A Case Study: The Metabolism of Singapore
	7.5 Urban Metabolism Applications, Challenges, and Opportunities
	7.6 Conclusions
	References
8 Spatial Economics, Urban Informatics, and Transport Accessibility
	8.1 Introduction
	8.2 Intellectual Context
	8.3 Econometric Models
		8.3.1 Isotropic Versus Hierarchical Market Linkages for Economic Mass (EM) Computation
		8.3.2 Control Variables
		8.3.3 Representing Spatial Spillover Effects
	8.4 Data
	8.5 Model Test Results
	8.6 Discussions
	8.7 Conclusions
	References
9 Conceptualizing the City of the Information Age
	9.1 Introduction
		9.1.1 Urban Complexity in the Age of Information and Communication Technologies
		9.1.2 A Different Kind of City
		9.1.3 The Smart City
		9.1.4 Urban Informatics
	9.2 Urban Research and Planning, Yesterday, and Tomorrow
		9.2.1 The City as Place
		9.2.2 The City as Node on a Network
		9.2.3 Planning the City
	9.3 Speculations
		9.3.1 The Robotic Era?
		9.3.2 The City’s Epistemic Planes
	9.4 Conclusion
	References
Part IIUrban Systems and Applications
10 Introduction to Urban Systems and Applications
11 Characterizing Urban Mobility Patterns: A Case Study of Mexico City
	11.1 Introduction
	11.2 Data Collection of POIs
		11.2.1 Parsing Algorithm
	11.3 Spatial Distribution of POIs
		11.3.1 Extended Radiation Model for Human Mobility
		11.3.2 Results
	11.4 Analyzing Human Mobility by Mode of Transportation
		11.4.1 Detected Mobility Groups
	11.5 Conclusions
	References
12 Laboratories for Research on Freight Systems and Planning
	12.1 Introduction
	12.2 Future Mobility Sensing, a Behavioral Laboratory
		12.2.1 Background
		12.2.2 FMS Architecture
		12.2.3 Applications
	12.3 SimMobility, a Simulation Laboratory
		12.3.1 Background
		12.3.2 SimMobility Architecture
		12.3.3 Applications
	12.4 Demonstrations
		12.4.1 Freight-Vehicle Route-Choice Model
		12.4.2 Quantification of Model Performance
		12.4.3 Replication of Specific Freight and Non-Freight-Vehicle Tours
	12.5 Concluding Remarks
	References
13 Urban Risks and Resilience
	13.1 Introduction
	13.2 Risks, Exposure, and Vulnerability
	13.3 Urban Resilience and Capacities
		13.3.1 The Definitional Quagmire
		13.3.2 Objects of Analysis
	13.4 Measurement and Assessment Informatics
	13.5 Science Informs Practice and Practice Informs Science
	13.6 Moving Forward
	References
14 Urban Crime and Security
	14.1 Introduction
	14.2 Urban Crime
		14.2.1 Historical Roots in Understanding Urban Crime: An Environmental Perspective
		14.2.2 Theoretical Concepts in Environmental Criminology
	14.3 Urban Security
		14.3.1 Fear of Crime in Urban Areas
		14.3.2 Implementation of Crime Prevention
	14.4 Latest Tools in Urban Crime Analysis and Security
		14.4.1 Crime Hotspot Mapping: From Retrospective Analysis to Prediction
		14.4.2 Advanced Police Patrolling Strategies
	14.5 Intelligent Data-Driven Policing
	14.6 Summary
	References
15 Urban Governance
	15.1 Transparency and City Open Data
		15.1.1 Open Data Platforms
		15.1.2 Open Data and Accountability
		15.1.3 Why Are Goals Important?
		15.1.4 Dashboards and Performance Indicators
	15.2 Algorithmic Decision-Making
		15.2.1 Positioning Algorithms
		15.2.2 Challenges for Operationalizing Algorithms
	15.3 Conclusion
	References
16 Urban Pollution
	16.1 Monitoring Air Quality in Urban Areas
	16.2 Remote Sensing of the Urban Heat Island
		16.2.1 Spatial Resolution of Satellite Sensors Related to Scales of Urban Climate
		16.2.2 Relationship Between Surface Temperature and Air Temperature
		16.2.3 Time of Imaging in Relation to Heat Island Maximum
		16.2.4 Anisotropy of the Satellite View
		16.2.5 The Need for Emissivity and Atmospheric Correction
	16.3 Monitoring Water Quality Along Urban Coastlines
	References
17 Urban Health and Wellbeing
	17.1 Smart Cities and Health
	17.2 Data
		17.2.1 Big Data
		17.2.2 Individual and Population Data
		17.2.3 Environmental Data
	17.3 Methods and Techniques
	17.4 BERTHA Studies
		17.4.1 AirGIS
		17.4.2 Personalized Tracking and Sensing
		17.4.3 Personalized Air-Pollution Sensors
		17.4.4 Mental Health
		17.4.5 Physical Activity
		17.4.6 Danish Blood-Donor Study
	17.5 Privacy
	17.6 Conclusions
	References
18 Urban Energy Systems: Research at Oak Ridge National Laboratory
	18.1 Introduction
	18.2 Population and Land Use
		18.2.1 Big Data and GeoAI to Create Population and Land-Use Data
		18.2.2 Estimating Urban Electricity Use in Data-Poor Regions
		18.2.3 Estimating Household-Level Energy Consumption
	18.3 Sustainable Mobility
		18.3.1 Human Interactions with Transportation Systems
		18.3.2 Emerging Options for Freight Delivery for Businesses
	18.4 Energy–Water Nexus
	18.5 Urban Resiliency
		18.5.1 Renewable Energy-Infrastructure Assessment
		18.5.2 Optimizing Energy and Safety Through Precision De-icing
	18.6 Situational Awareness of National Energy Infrastructure
	18.7 Conclusion
	References
Part IIIUrban Sensing
19 Introduction to Urban Sensing
20 Optical Remote Sensing
	20.1 Introduction
	20.2 History of Optical Remote Sensing
	20.3 Latest Developments in Optical Remote Sensing
		20.3.1 Introduction to Representative Optical Satellite Sensors
	20.4 Processing of Remote Sensing Satellite Images
		20.4.1 Image Pre-processing
		20.4.2 Image Processing
		20.4.3 Image Post-Processing
	20.5 Applications of Optical Remote Sensing
		20.5.1 Land-Use and Land-Cover Mapping
		20.5.2 Urban Vegetation Phenology
		20.5.3 Urban Heat Island Mapping
		20.5.4 Rock Outcrops Identification
	20.6 Summary
	References
21 Urban Sensing with Spaceborne Interferometric Synthetic Aperture Radar
	21.1 Synthetic Aperture Radar
	21.2 Interferometric Synthetic Aperture Radar
	21.3 Multi-temporal InSAR (MTInSAR)
	21.4 Applications in Urban Areas
		21.4.1 Construction of Fine Resolution DEM
		21.4.2 Subsidence Measurement
		21.4.3 Monitoring Stability of Infrastructures
	21.5 Summary
	References
22 Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics
	22.1 Introduction
	22.2 Detection of Urban Objects with ALS and Co-registered Imagery
		22.2.1 General Strategy
		22.2.2 Feature Derivation
		22.2.3 AdaBoost Classification
	22.3 Detection of Urban Traffic Dynamics with ALS Data
		22.3.1 Artifacts Effect of Vehicle Motion in ALS Data
		22.3.2 Detection of Moving Vehicles
		22.3.3 Concept for Vehicle Velocity Estimation with ALS Data
	22.4 Experiments and Results
		22.4.1 Detection of Urban Objects with ALS Data Associated with Aerial Imagery
		22.4.2 Accuracy Prediction for Vehicle Velocity Estimation Using ALS Aata
	22.5 Summary
	References
23 Photogrammetry for 3D Mapping in Urban Areas
	23.1 Introduction
	23.2 Fundamentals of Photogrammetry
		23.2.1 Image Orientation
		23.2.2 Bundle Adjustment
		23.2.3 Image Matching
	23.3 Advances in Photogrammetry for 3D Mapping in Urban Areas
		23.3.1 Structure from Motion and Multi-view Stereo
		23.3.2 Integrated 3D Mapping from Multiple-Source Data
	23.4 Summary
	References
24 Underground Utilities Imaging and Diagnosis
	24.1 Mapping and Imaging
		24.1.1 EMI/PCL
		24.1.2 GPR
		24.1.3 Comparison Between EMI/PCL and GPR
	24.2 Diagnosis
		24.2.1 Ground-Based Technologies
		24.2.2 In-Line Technologies
	24.3 Future Trends of Research and Development
		24.3.1 Multi-array and Fully Automated GPR
		24.3.2 In-Line Robotic Imaging with Micro-robots Carrying Small Sensors in Pressurized and Gravity Utilities
		24.3.3 Multi-disciplinary Research on Sensors, Robotics, Electronics, Pattern Recognition, and Change Detection
		24.3.4 Utility Lab
	24.4 Conclusion and the Way Forward
		24.4.1 Human-Factor Perspective
		24.4.2 Technological Perspective
	References
25 Mobile Mapping Technologies
	25.1 Introduction
	25.2 Roadmap of Mobile Mapping Technologies
	25.3 Recent Progress on Mobile Mapping Technology
		25.3.1 Digital Imaging Systems
		25.3.2 Positioning and Orientation Systems
		25.3.3 Sensor Fusion Algorithms
		25.3.4 Collaborative Mobile Mapping Schemes
		25.3.5 Mobile Mapping Technology for Rapid Disaster Response Applications
		25.3.6 Mobile Mapping Technology for Indoor Mapping Applications
		25.3.7 Mobile Mapping Technology for Autonomous Vehicle Applications
		25.3.8 The Latest Developments of HD Maps for Autonomous Driving Applications in Taiwan
	25.4 Future Trends in Mobile Mapping Technology
	25.5 Conclusion
	References
26 Smartphone-Based Indoor Positioning Technologies
	26.1 Introduction
	26.2 The State-of-the-Art Indoor Positioning with Smartphones
		26.2.1 Positioning Technology of RF Signals
		26.2.2 Positioning Technology Based on Embedded Sensors
		26.2.3 Positioning Technology of Multi-source Fusion
	26.3 Difficulties in Indoor Positioning
		26.3.1 Complex Channel Transmission and Spatial Topology in Indoor Environments
		26.3.2 Heterogeneous Source of Positioning
		26.3.3 Limited Computing Resources on Mobile Terminals
	26.4 The Development Trends of Indoor Positioning Technology
		26.4.1 Explore New Positioning Sources for Fine-Precision, High-Utility Smartphone Indoor Positioning
		26.4.2 Fusion of Heterogeneous Positioning Sources
		26.4.3 GIS-Based Semantic Constraint Location and Semantic Cognitive Collaboration Positioning
	26.5 Conclusions
	References
27 What Urban Cameras Reveal About the City: The Work of the Senseable City Lab
	27.1 Introduction
	27.2 Computer Vision and the City: Google Street View Images
	27.3 Thermals Images of the City
	27.4 Navigating Urban Spaces Using Computer Vision
	27.5 Conclusion
	References
28 User-Generated Content: A Promising Data Source for Urban Informatics
	28.1 Introduction
		28.1.1 Background and Definition
	28.2 Characteristics of UGC
	28.3 Analytical and Computational Framework to Process UGC Data
	28.4 Single-Source UGC-Based Urban Studies
		28.4.1 User Information and Citizen Demographics
		28.4.2 Human Mobility, Urban Spatial Structure, and Transportation
		28.4.3 Place Semantics and Sentiments
	28.5 Multi-source Data-Driven Urban Studies
		28.5.1 Fusion of Multiple UGC Sources
		28.5.2 Fusion of UGC and PGC
	28.6 Conclusion
	References
29 User-Generated Content and Its Applications in Urban Studies
	29.1 Introduction
	29.2 User-Generated Content
		29.2.1 Geo-Tagged Photos
		29.2.2 Social Media Data
		29.2.3 Crowdsourcing GPS Trajectories
		29.2.4 Videos
	29.3 Urban Studies Driven by User-Generated Content
		29.3.1 Framework for UGC-Driven Urban Studies
		29.3.2 Urban Planning
		29.3.3 Urban Transportation
		29.3.4 Urban Environments and Health
		29.3.5 Urban Safety
	29.4 Challenges and Future Directions
		29.4.1 Data Quality and Privacy
		29.4.2 Multi-source UGC Fusion
		29.4.3 Integrating Urban Sensing and Urban Governance
	29.5 Conclusion
	References
Part IVUrban Big Data Infrastructure
30 Introduction to Urban Big Data Infrastructure
31 Cultivating Urban Big Data
	31.1 Introduction
	31.2 Sources of Urban Big Data
	31.3 User Stories
	31.4 Elements of Urban Big Data
	31.5 Data-Collecting and Processing Techniques
	31.6 Toward Urban Big Data Infrastructure
	31.7 Concluding Remarks
	References
32 Geoprivacy, Convenience, and the Pursuit of Anonymity in Digital Cities
	32.1 Introduction
		32.1.1 Application #1: The Role of Cities in Slavery Prior to the Civil War
		32.1.2 Application #2: Informed Delivery by the US Postal Service
		32.1.3 Application #3: Geoslavery in the Middle East and China
	32.2 Tracking Technologies
	32.3 Informed Acceptance of Benefits and Adverse Acceptance of Risks
	32.4 Legal and Regulatory Responses to Tracking Technologies
	32.5 Geoprivacy, the Inconscient Syndrome, and Control in the Academy
	32.6 Conclusions
	32.7 Epilogue
	References
33 3D Modeling of the Cadastre and the Spatial Representation of Property
	33.1 Introduction
	33.2 Spatial Rights to Real Property
		33.2.1 Legal Context of a 3D Cadastre
		33.2.2 Geometry of 3D Property with Homogeneous Land Space
	33.3 Integral Spatial Modeling of 3D Property
	33.4 Heterogeneity of Land Space Used for Property
	33.5 A Case Study of Spatial Modeling of Ownership Structure in China
		33.5.1 Ownership of Condominiums in China
		33.5.2 Implementation Tool for Spatial Modeling of Ownership
		33.5.3 An Example of Spatial Representation of the Internal Structure of Ownership
	33.6 Summary
	References
34 Semantic 3D City Modeling and BIM
	34.1 Digital Models of the Built Environment
	34.2 Semantic 3D City Modeling
		34.2.1 Purpose and Key Applications
		34.2.2 Modeling Paradigm
		34.2.3 The International Standard CityGML
	34.3 Building Information Modeling
		34.3.1 Purpose and Key Applications
		34.3.2 Modeling Paradigm
		34.3.3 The International Standard IFC
	34.4 Integration of Semantic 3D City Modeling and BIM
		34.4.1 Applications/Use Cases
		34.4.2 Relationship of Semantic 3D City Modeling and BIM
	34.5 Recent Developments in Urban Informatics Involving Digital Models of the Built Environment
		34.5.1 Integrated Planning Models
		34.5.2 Digital Models of the Built Environment, Smart Cities, and Digital Urban Twins
	34.6 Summary and Conclusions
	References
35 CityEngine: An Introduction to Rule-Based Modeling
	35.1 3D: One Better than 2D
	35.2 2D Shapes + Rules = 3D Models
	35.3 On the (Many) Origins of Shapes
		35.3.1 Dynamic Shapes: Streets, Blocks, and Lots
		35.3.2 Graphs and Cities
	35.4 Writing CGA Rules for Fun and Profit
		35.4.1 Writing Rules
		35.4.2 Modeling Workflow
		35.4.3 Attributes
		35.4.4 Exploring Design Space
	35.5 Beyond CityEngine: Export Pathways
	35.6 Conclusion
	References
36 Integrating CyberGIS and Urban Sensing for Reproducible Streaming Analytics
	36.1 Introduction and Background
		36.1.1 Urban Sensing Data
		36.1.2 CyberGIS
		36.1.3 Spatial Data Synthesis
		36.1.4 Cyberinfrastructure
	36.2 Framework
		36.2.1 Architecture
		36.2.2 User Environment
		36.2.3 Analytics
	36.3 Case Study
		36.3.1 Study Area
		36.3.2 AoT Data
		36.3.3 CyberGIS-Jupyter
	36.4 Concluding Discussion
	References
37 Spatial Search
	37.1 Spatial Search in the Context of Urban Studies
	37.2 Geocoding
	37.3 Spatial Indexing
	37.4 Search Algorithms
		37.4.1 Spatial Queries
		37.4.2 Spatial Search with Graph Theory
	37.5 Distributed Search and Interoperability in the Web Environment
	37.6 Trends
	37.7 Conclusion
	References
38 Urban IoT: Advances, Challenges, and Opportunities for Mass Data Collection, Analysis, and Visualization
	38.1 The Urban Internet of Things
	38.2 The Digital Twin
	38.3 Potential Versus Reality
	38.4 Putting It into Practice: Bats and Creatures
	38.5 The Humble Lamp Post
	38.6 Urban Modeling
	38.7 Talking to the Neighbors
	38.8 Conclusion
	References
Part VUrban Computing
39 Introduction to Urban Computing
40 Visual Analytics for Characterizing Mobility Aspects of Urban Context
	40.1 Introduction
	40.2 State of the Art
	40.3 Mobility Data: Properties and Problems
	40.4 Data Types: Events, Trajectories, Spatial Time Series, and Situations
		40.4.1 Context Acquisition from Movement Data
		40.4.2 Flow Context
		40.4.3 Time Context
	40.5 Specifics of Episodic Movement Data
	40.6 Discussion and Conclusions
	References
41 Cloud, Edge, and Mobile Computing for Smart Cities
	41.1 Introduction
		41.1.1 Why Computing is Important in Smart Cities
		41.1.2 Major Computing Techniques in Smart City Studies
	41.2 Computing for Smart Cities
		41.2.1 Data and Model in Smart Cities
		41.2.2 Computing Challenges in Smart Cities
		41.2.3 Generic Computing Architecture for Smart Cities
	41.3 Cloud Computing for Smart Cities
		41.3.1 Methodology
		41.3.2 Challenges, Motivations and Opportunities
		41.3.3 Urban Heat Island Use Case
	41.4 Edge Computing for Smart Cities
		41.4.1 Methodology
		41.4.2 Challenges, Motivations, and Opportunities
		41.4.3 Urban Heat Island Use Case
	41.5 Mobile Computing for Smart Cities
		41.5.1 Methodology
		41.5.2 Challenges, Motivations, and Opportunities
		41.5.3 Urban Heat Island Use Case
	41.6 Case Study
		41.6.1 Urban Heat Island (UHI)
		41.6.2 UHI Challenges and Opportunities
		41.6.3 Integrated Workflow
	41.7 Summary
		41.7.1 The Future of Urban Computing for Smart Cities
	References
42 Data Mining and Knowledge Discovery
	42.1 Overview
	42.2 Data Mining for Urban Analysis
		42.2.1 Urban Pattern Discovery
		42.2.2 Urban Activity Modeling
		42.2.3 Urban Mobility Modeling
		42.2.4 Urban Event Detection
	42.3 Multimodal Embedding for Urban Activity Modeling
		42.3.1 Method Overview
		42.3.2 Multimodal Embedding via Attribute Reconstruction
		42.3.3 The Optimization Procedure
	42.4 Experiments
	42.5 Summary
	42.6 Future Directions
	References
43 AI and Deep Learning for Urban Computing
	43.1 Background
	43.2 Challenges
	43.3 Traditional AI Techniques
		43.3.1 Supervised Learning
		43.3.2 Unsupervised Learning
		43.3.3 Semi-supervised Learning
		43.3.4 Matrix Factorization
		43.3.5 Graphical Model
	43.4 Deep Learning
		43.4.1 Restricted Boltzmann Machines (RBM)
		43.4.2 CNN
		43.4.3 RNN and LSTM
		43.4.4 Autoencoder (AE)
	43.5 Reinforcement Learning
	43.6 Applications of AI Techniques in Urban Computing
		43.6.1 Urban Planning
		43.6.2 Urban Transportation
		43.6.3 Location-Based Social Networks (LBSNs)
		43.6.4 On-Demand Service
		43.6.5 Urban Safety and Security
		43.6.6 Urban Environment Monitoring
	43.7 Conclusion
	References
44 Microsimulation
	44.1 Background to Microsimulation
	44.2 Overview of Methods and Concepts
		44.2.1 Population Synthesis
		44.2.2 Iterative Proportional Fitting
		44.2.3 Reweighting
		44.2.4 Data Linkage
		44.2.5 Efficient Representation and Flexible Aggregation
		44.2.6 List Processing
	44.3 An Example: Models of National Infrastructure
		44.3.1 Overview
		44.3.2 An Application of Spatial MSM to Energy Modeling
		44.3.3 Extensions
	44.4 Priorities for Spatial Microsimulation
		44.4.1 Computation
		44.4.2 Uncertainty
		44.4.3 Data Assimilation
		44.4.4 Dynamics
		44.4.5 Interdependence
	44.5 Conclusions
	References
45 Cellular Automata Modeling for Urban and Regional Planning
	45.1 Introduction
	45.2 Methodology and Data Collection
		45.2.1 Urban CA for Formulating Urban and Regional Planning Scenarios
		45.2.2 Data Collection and Model Calibration
	45.3 Types of Urban CA Models
	45.4 Applications of Urban CA in Urban Planning
	45.5 Discussion and Conclusion
		45.5.1 Current Issues in Urban CA Modeling
		45.5.2 Summary and Future Research Directions
	References
46 Agent-Based Modeling and the City: A Gallery of Applications
	46.1 Introduction
	46.2 What is Agent-Based Modeling?
		46.2.1 Examples of Why to Model
		46.2.2 Steps in Building an Agent-Based Model
		46.2.3 Application Areas for Geographically Explicit Agent-Based Models
	46.3 Integrating Data and Decision-Making into Agent-Based Models
		46.3.1 Incorporating Decision-Making into Agent-Based Models
		46.3.2 The Growth of Data and Its Utilization Within Agent-Based Models
		46.3.3 The Potential of Machine Learning and Agent-Based Modeling
	46.4 Summary and Outlook
	References
47 Transportation Modeling
	47.1 Introduction
	47.2 Informatics and Travel Behavior
		47.2.1 Real-Time Travel-Related Information
		47.2.2 New Mobility Services and Technologies
	47.3 Informatics and Transportation Network Performance
	47.4 Informatics and Data Support for Travel-Demand Modeling
		47.4.1 Informatics-Based Survey Methods
		47.4.2 Passive Trip Tracking
		47.4.3 Data Fusion and Imputation
	47.5 Informatics and Modeling Methods
	47.6 Chapter Summary
	References
Part VIPerspective for the Future
48 A Final Word: The Value of Urban Informatics
	48.1 Introduction
	48.2 Visions for Urban Informatics
		48.2.1 Urban Intelligence
		48.2.2 Urban Science
		48.2.3 Urban Planning and Design
		48.2.4 Urban Development
	48.3 Unintended Consequences
	48.4 The Future of Urban Informatics
	References