Big Data Analytics for Smart Urban Systems (Urban Sustainability)

✍ Scribed by Saeid Pourroostaei Ardakani, Ali Cheshmehzangi

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 143
• Edition: 1st ed. 2023
• Category: Library

✦ Synopsis

Big Data Analytics for Smart Urban Systems aims to introduce Big data solutions for urban sustainability smart applications, particularly for smart urban systems. It focuses on intelligent big data which takes the benefits of machine learning to analyse large and rapidly changing datasets in smart urban systems. The state-of-the-art Big data analytics applications are presented and discussed to highlight the feasibility of big data and machine learning solutions to enhance smart urban systems, smart operations, urban management, and urban governance. The key benefits of this book are, (1) to introduce the principles of machine learning-enabled big data analysis in smart urban systems, (2) to present the state-of-the-art data analysis solutions in smart management and operations, and (3) to understand the principles of big data analytics for smart cities and communities.

Endorsements
‘Over the many years of collaboration between academia and industry, we noticed the common language is ‘big data’; with that, we have developed novel ideas to bridge the gaps and help promote innovation, technologies, and science’.- Tian Tang, Independent Researcher, China
‘Big Data Analytics is a fascinating research area, particularly for cities and city transformations. This book is valuable to those who think vigorously and aim to act ahead’.- Li Xie, Independent Researcher, China
‘For urban critiques, knowledge trains aspiring opportunities toward outstanding manifestations. Smartness has evolved or/ advanced rambunctious & embracing realities along (with) novel directions and nurturing integrated city knowledge’.- Aaron Golden, SELECT Consultants, UK

✦ Table of Contents

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