Big Data Analytics for Smart Urban Systems (Urban Sustainability)

Preface
Acknowledgements
About This Book
Praise for Big Data Analytics for Smart Urban Systems
Contents
About the Authors
1 Big Data Analytics: An Introduction to Their Applications for Smart Urban Systems
	1.1 The Emergence of Big Data Analytics
	1.2 The Aim and Objectives of the Book
	1.3 The Structure of Two Volumes on Big Data Analytics
	1.4 A Summary
	Box 1.1 Examples of ‘Smart Cities’ reports and documents
	Box 1.2 Examples of ‘Smart Cities’ reports and documents
	Box 1.3 Examples of ‘Smart Cities’ reports and documents
	Box 1.4 Examples of ‘Smart Cities’ reports and documents
	Box 1.5 Examples of ‘Smart Cities’ reports and documents
	Box 1.6 Examples of ‘Smart Cities’ reports and documents
	Box 1.7 Examples of ‘Smart Cities’ reports and documents
	Box 1.8 Examples of ‘Smart Cities’ reports and documents
	Box 1.9 Examples of ‘Smart Cities’ reports and documents
	Box 1.10 Examples of ‘Smart Cities’ reports and documents
	References
2 Stock Market Prediction During COVID-19 Pandemic: A Time-Series  Big Data Analysis Method
	2.1 Introduction
	2.2 Literature Review
		2.2.1 Big Data Analytics in Stock Markets
	2.3 Methodology
		2.3.1 Data Preprocessing
		2.3.2 Pattern Retrieval Using DTW
		2.3.3 Feature Selection
		2.3.4 Predicted Stock Data Generation Using LSTM
	2.4 Result Analysis and Discussion
		2.4.1 Data Preprocessing
		2.4.2 Estimation of Close Price and COVID-19 Data
		2.4.3 Pattern Selection
		2.4.4 Feature Selection Result with Analysis
		2.4.5 Result for LSTM Price Prediction
		2.4.6 Predicted Price and Covid-19 Data Factors
	2.5 Conclusion
	References
3 A Big Data Solution to Predict Cryptocurrency Market Trends: A Time-Series Machine Learning Approach
	3.1 Introduction
	3.2 Literature Review
		3.2.1 Cryptocurrency Pattern Recognition and Clustering
		3.2.2 Bitcoin Price Prediction
	3.3 Methodology
		3.3.1 Dataset Selection and Pre-processing
		3.3.2 Data Pattern Recognition via Clustering
		3.3.3 Predictive Analysis
	3.4 Result and Discussion
		3.4.1 Trend Prediction
	3.5 Conclusion
	References
4 Big Data Analytics for Credit Risk Prediction: Machine Learning Techniques and Data Processing Approaches
	4.1 Introduction
	4.2 Literature Review
	4.3 Methodology
		4.3.1 Dataset and Data Pre-processing
		4.3.2 Machine Learning Models
	4.4 Result and Discussion
	4.5 Conclusion
	References
5 Worldwide Mobility Trends and the COVID-19 Pandemic: A Federated Regression Analysis During the pandemic’s Early Stage
	5.1 Introduction
	5.2 Literature Review on Existing Research Studies
		5.2.1 Influence Factors
		5.2.2 Pharmacological and Non-pharmacological Interventions
		5.2.3 Social Distance Policy
		5.2.4 Reflection of H1N1
		5.2.5 Cultural Susceptibility and Policy
		5.2.6 Voluntary Mechanisms
	5.3 Methodology
		5.3.1 Data Sources
		5.3.2 Statistical Analysis
		5.3.3 Data Analysis
		5.3.4 Correlation Matrix
		5.3.5 Regression
	5.4 Results and Discussion
		5.4.1 Correlation
		5.4.2 Regression Results
	5.5 Conclusions
	References
6 Adaptive Feature Selection for Google App Rating in Smart Urban Management: A Big Data Analysis Approach
	6.1 Introduction
	6.2 Literature Review
		6.2.1 Traditional Dimension Reduction Techniques
		6.2.2 Random Forest
		6.2.3 Data Pre-processing
	6.3 Methodology
	6.4 Results and Discussions
		6.4.1 Overall Comparison
		6.4.2 Discussion on Random Forest
		6.4.3 Discussion on Linear Discriminant Analysis
	6.5 Conclusions
	References
7 Improve the Daily Societal Operations Using Credit Fraud Detection: A Big Data Classification Solution
	7.1 Introduction: An Overview of Recent and Ongoing Research on Credit Fraud Detection
	7.2 Literature Review Related to Big Data and Credit Fraud Detection
	7.3 Methodology
		7.3.1 Dataset Introduction
		7.3.2 Data Preprocess and Feature Extraction
		7.3.3 Model Description
		7.3.4 Model Implementation
	7.4 Results and Analysis
	7.5 Conclusions
	References
8 Moving Forward with Big Data Analytics and Smartness
	8.1 A Brief Reflection on Big Data Analytics and Smart Urban Systems
	8.2 Methodological Contributions of the Book
	8.3 Concluding Remarks: A Summary of Lessons Learnt for Future Research
	References
Index