Intelligence for Future Cities: Planning Through Big Data and Urban Analytics

Preface
Contents
1 Introduction
1.1 Overview of the Book
1.1.1 Digital Cities
1.1.2 Mobility Futures
1.1.3 Fine-Scale Urban Analysis
1.2 Intelligence for Future Cities
Reference
Part I Digital Cities
2 Hybrid Smartness: Seeking a Balance Between Top-Down and Bottom-Up Smart City Approaches
2.1 Introduction
2.2 The Idea of Smartening Cities-Exploring Smart Cities’ Terms and Concepts Over Time
2.3 Early Ideas: Top-Down Smartness
2.4 The Emergence of Bottom-Up Smartness
2.5 Towards Hybrid Smartness: The Need for a Socio-Technical Approach
2.6 Reflections on Current Smart Realities
2.7 Final Remarks−Rethinking Planning at Large
2.8 Conclusion
References
3 Interpreting the Smart City Through Topic Modeling
3.1 Introduction
3.2 Literature Review
3.3 Application of Human-Centered Topic Modeling
3.3.1 Modeling
3.3.2 Topic Comparison
3.4 Results
3.4.1 Modeling of Grant Applications
3.4.2 Modeling of Finalists’ Proposals
3.4.3 Comparing the First Round to the Finalists’ Round
3.5 Discussion
3.5.1 What Constitutes the Canadian Smart “City”?
3.5.2 What Constitutes the Canadian “Smart” City?
3.5.3 What Is the Utility of Topic Modelling for Abductive Inquiry About Smart Cities?
3.6 Conclusion
References
4 The Venue Code: Digital Surveillance, Spatial (Re)organization, and Infrastructural Power During the Covid Pandemic in China
4.1 Digital Surveillance and Infrustructures in China
4.2 Methodology
4.3 Organizational Structure
4.4 Definition, Piloting, and Incorporation
4.5 Spatial (Re)organization
4.6 Logistics and Technologies
4.7 Conclusion
References
5 The Platformization of Public Participation: Considerations for Urban Planners Navigating New Engagement Tools
5.1 Background
5.1.1 Urban Technology Platforms
5.1.2 Public Participation in Planning
5.1.3 Convenor Platforms as Mediators of Public Participation
5.2 The Rise of Participatory Convenor Platforms
5.3 The Platformization of Public Participation: Questions for Planning Practice and Research
5.4 Conclusions
References
Part II Mobility Futures
6 Shared Micro-mobility: A Panacea or a Patch for Our Urban Transport Problems?
6.1 Shared Micro-mobility: The New Kid on the Block
6.1.1 The Three Generations of Shared Micro-mobility
6.1.2 Contribution and Disruption to Cities
6.2 Shared Micro-mobility Transforming Cities: The Research Landscape
6.2.1 Shared Micro-mobility Transforming the Urban Economy
6.2.2 Shared Micro-mobility Transforming Urban Sustainability
6.2.3 Shared Micro-mobility Transforming Urban Accessibility
6.2.4 Shared Micro-mobility Transforming Urban Lifestyles
6.3 What Does and Doesn’t The Data Tell? An Empirical Demonstration
6.3.1 Study Area
6.3.2 Data
6.3.3 Building a Typology of Shared Micro-mobility Trips
6.3.4 People: The Missing Puzzle
6.4 Two Scenarios of Shared Micro-mobility: A Panacea or a Patch for Urban Transport Problems?
6.4.1 Scenario 1: A Shared Micro-mobility Paradise
6.4.2 Scenario 2: When the Hype is Over
6.5 Conclusion: Making Shared Micro-mobility Work for Cities
References
7 Understanding Bikeability: Insight into the Cycling-City Relationship Using Massive Dockless Bike-Sharing Records in Beijing
7.1 Introduction
7.2 Methodology
7.2.1 Research Design
7.2.2 Variables and Data
7.3 Results
7.3.1 Data Processing and Preliminary Tests
7.3.2 Regression Analysis and Results
7.4 Conclusions and Discussion
References
8 Disclosing the Impact of Micro-level Environmental Characteristics on Dockless Bikeshare Trip Volume: A Case Study of Ithaca
8.1 Introduction
8.2 Literature Review
8.2.1 Perceived Built Environment and Cycling Behavior
8.2.2 SVI, CV, and ML for Micro-level Environment Characteristics
8.3 Dataset and Methodology
8.3.1 Study Area
8.3.2 Methods
8.3.3 Data Collection and Processing
8.3.4 Correlation Analysis
8.4 Results
8.4.1 Trip Volumes
8.4.2 Strength of Association by Attribute Groups
8.4.3 OLS Regression Results and Performances
8.5 Discussion
8.5.1 Micro-level Environment Characteristics
8.5.2 Conventional Macro-level Environment Characteristics
8.6 Conclusion
Appendix
References
9 A Planning Support System for Boosting Bikeability in Seoul
9.1 Introduction
9.2 Literature Review
9.2.1 Bikeability Index
9.2.2 Planning Support Systems
9.2.3 Research Gap and Contribution
9.3 Methodology
9.3.1 Methodology Summary
9.3.2 Development of the Model’s Dataset
9.3.3 Development of the Models: Poisson and GWR
9.4 Results and Discussion
9.4.1 Centrality Indices Using UNA Tool
9.4.2 Spatial Validation Tests: Autocorrelation, Dependence, and Heterogeneity
9.4.3 Geographically Weighted Regression Model
9.5 Bikeability Index-Based Planning Support System
9.6 Conclusion and Policy Implications
References
10 Integrating Big Data and a Travel Survey to Understand the Gender Gap in Ride-Hailing Usage: Evidence from Chengdu, China
10.1 Introduction
10.2 Background
10.2.1 Gendered Travel Needs and Behavior
10.2.2 Gender Gap in the Age of Ride-Hailing
10.3 Case Study and Data
10.3.1 Case Study City: Chengdu
10.3.2 Data Sources
10.4 Methodology
10.4.1 Measurement of Activity Space
10.4.2 Built Environment and Socioeconomic Status
10.4.3 Models
10.5 Results
10.5.1 Gendered Ride-Hailing Usage Seeing from Travel Survey
10.5.2 Spatial Variations in Ride-Hailing Usage Seeing from Big Data
10.5.3 Gendered Activity Space
10.5.4 Ride-Hailing Usage, Gender, and Influencing Factors
10.6 Conclusions and Discussion
References
11 Urban Airspace Route Planning for Advanced Air Mobility Operations
11.1 Introduction and Motivation
11.1.1 Literature Review
11.2 AAM and eVTOLs
11.2.1 eVTOL Aircraft Capabilities
11.2.2 Concept Overview
11.3 Use Case: Atlanta Airport City
11.3.1 Inner Aerotropolis Network
11.3.2 Ensuring Synergies with Existing Airport
11.3.3 Airport Shuttle Network
11.3.4 Demand
11.4 Conclusions and Recommendation
11.4.1 Conclusion
11.4.2 Future Work
References
Part III Fine-Scale Urban Analysis
12 “Eyes on the Street”: Estimating Natural Surveillance Along Amsterdam’s City Streets Using Street-Level Imagery
12.1 Introduction
12.2 Method
12.3 Data
12.4 Results
12.5 Discussion
12.6 Conclusion
References
13 Automatic Evaluation of Street-Level Walkability Based on Computer Vision Techniques and Urban Big Data
13.1 Introduction
13.1.1 Background
13.1.2 Related Studies
13.1.3 Current Gaps and Research Objectives
13.2 Methodology
13.2.1 Constructing a Walkability Index for Automatic Evaluation
13.2.2 Measuring Walkability
13.2.3 Accessing the Relative Importance of Variables
13.3 Experimental Results
13.3.1 Objective Walkability in Kowloon West
13.3.2 Subjective Walkability in Kowloon West
13.4 Discussion
13.4.1 Can the Proposed Measurement Method Produce a Low-Cost, Fast, and Reliable Walkability Evaluation?
13.4.2 Do the Proposed Walkability Index and Its Methods Have High Applicability and Generalization Potential?
13.4.3 What Are the Advantages of Applying Multiple AI Techniques in Walkability Evaluations?
13.5 Conclusion
References
14 Promoting Sustainable Travel Through a Web-Based Tourism Support System
14.1 Introduction
14.2 Related Work
14.3 System Design
14.3.1 System Overview
14.3.2 Design of Each System
14.4 Database Creation of the System
14.4.1 Data Used in the System
14.4.2 Inference of Tourism Congestion
14.4.3 Calculation of the Feature Values of Tourist Attractions
14.4.4 Database Creation
14.5 System Development
14.5.1 System Frontend
14.5.2 System Backend
14.5.3 System Interface
14.6 System Operation
14.6.1 Operation Overview
14.6.2 Operation Results
14.7 System Evaluation
14.7.1 Overview of the Web Questionnaire Survey for Users
14.7.2 Evaluation Result
14.7.3 Identification of Improvement Measures
14.8 Conclusion
References
15 Applying the AURIN Walkability Index at the Metropolitan and Local Levels by Sex and Age in Australia
15.1 Introduction
15.2 Literature Review
15.2.1 Included Criteria and Attributes
15.2.2 Socio-Demographic Profiling
15.2.3 Scale of the Analysis/Geography
15.2.4 Unit of Analysis
15.2.5 Method of Visualisation
15.3 Methodology
15.3.1 Study Area
15.3.2 AURIN Walkability Index (AWI)
15.3.3 Correlation of the AWI with Observed Data of “Walking to Work”
15.4 Analysis and Findings
15.4.1 At the National Level
15.4.2 At the Metropolitan Level
15.5 Discussion and Conclusion
References
16 Predicting Urban Heat Island Mitigation with Random Forest Regression in Belgian Cities
16.1 Introduction
16.2 Methodology
16.3 Study Area and Dataset
16.4 Parameters Influencing LST
16.4.1 Building Density
16.4.2 Building Volume Index
16.4.3 Sky View Factor
16.4.4 Solar Radiation
16.4.5 Normalized Difference Vegetation Index (NDVI)
16.4.6 Normalized Difference Built-Up Index (NDBI)
16.4.7 Frontal Area Index (FAI)
16.4.8 Height Variation (HV)
16.4.9 Average Height (AH)
16.4.10 Distance to Water
16.5 Land Surface Temperature (LST)
16.6 Data Processing
16.7 RF Regression
16.8 Simulating Green Roofs
16.9 Results
16.9.1 Model Results and Accuracy
16.9.2 Variable Importance at Optimal Ntree and Mtry
16.9.3 Comparing Predicted and Observed Values of LST
16.9.4 Prediction After Green Roofs
16.10 Discussion and Conclusions
References
17 A Framework to Probe Uncertainties in Urban Cellular Automata Modelling Using a Novel Framework of Multilevel Density Approach: A Case Study for Wallonia Region, Belgium
17.1 Introduction
17.2 Background
17.2.1 Approaches in Evaluating Uncertainty Based on Scale Effects: A Multiscale Approach
17.2.2 Scenario Description and Impacts of Uncertainty on Transition Rules
17.3 Materials and Methods
17.3.1 Study Area
17.3.2 Conceptual Framework
17.4 Observation and Assessments
17.5 Conclusion
References
Index