Applied Data Science in Tourism: Interdisciplinary Approaches, Methodologies, and Applications

Preface
	Purpose of the Book and Potential Audience
	What This Book Is Not!
	Features of This Book
Acknowledgments
Contents
Notes on Contributors
Abbreviations and Acronyms
Introduction: Data Science in Tourism
	A Brief Introduction and the Structure of This Book
	Data Science and Tourism
	Data Science in Tourism and the Structure of This Book
		Chapters 1-5: Theoretical Fundamentals
		Chapters 6-14: Machine Learning
		Chapters 15-20: Natural Language Processing
		Chapters 21-26: Additional Methods
	Conclusion
	References
Industry Insights from Data Scientists: QandA Session
	Interview 1
	Interview 2
	Interview 3
	Interview 4
	Interview 5
	Concluding Remarks
Part I: Theoretical Fundaments
	AI and Big Data in Tourism
		1 Introduction
		2 AI, Machine Learning, and Data Science
		3 AI for Big Data
			3.1 AI for Problem Framing
			3.2 AI for Data Gathering
			3.3 AI for Data Cleaning and Preparation
			3.4 AI for Data Processing
			3.5 AI for Data Exploitation
		4 AI and Big Data in Tourism
		Further Readings
		Other Sources
		References
	Epistemological Challenges
		1 Introduction
		2 Epistemological Evolution
		3 Epistemological Challenges: Data Science in Tourism Research
			3.1 Topic Formulation and Relevance for Academia and Industry
			3.2 Data and Its Access and Collection
			3.3 Data Pre-processing
			3.4 Feature Engineering
			3.5 Data Analysis
			3.6 Model Evaluation and Model Tuning
			3.7 Interpretation of Results
		4 Conclusion
		References
	Data Science and Interdisciplinarity
		1 Introduction
		2 Problem Identification
		3 Data Science Is an Interdisciplinary Area
		4 The Importance of Core Competencies in Data Science
		5 Conclusion
		References
	Data Science and Ethical Issues
		1 Introduction
		2 Ethics
			2.1 Data Science and Ethics
		3 Data Science Ethics Issues
			3.1 Privacy
			3.2 Data Validity
			3.3 Algorithm Fairness and Bias
		4 Big Data
		5 Artificial Intelligence and Machine Learning
		6 Conclusion
		References
	Web Scraping
		1 Introduction and Theoretical Foundations
			1.1 Open Data
			1.2 APIs
			1.3 Scraping Data
			1.4 Legal Perspectives of Text and Data Mining
			1.5 Typical Use Cases of Web Scraping in Tourism
			1.6 BeautifulSoup
			1.7 Selenium
			1.8 Scrapy
		2 Practical Demonstration
		Further Readings and Other Sources
			Blogposts
		References
Part II: Machine Learning
	Machine Learning in Tourism: A Brief Overview
		1 Introduction and Theoretical Foundations
			1.1 The Machine Learning Process
			1.2 Unsupervised Learning
				1.2.1 Clustering
				1.2.2 Dimensionality Reduction
			1.3 Supervised Learning
				1.3.1 Classification
				1.3.2 Regression
			1.4 Reinforcement Learning
			1.5 Neural Networks
			1.6 Machine Learning Limitations and Challenges
			1.7 Auto-ML
		Further Readings and Other Sources
		References
	Feature Engineering
		1 Introduction
			1.1 Definitions
			1.2 Feature Engineering Cycle
		2 Combining Features
			2.1 Normalization
			2.2 Discretization
			2.3 Missing Data
			2.4 Descriptive Features
		3 Reducing Features
			3.1 Feature Importance
			3.2 Feature Selection
		4 Expanding Features
			4.1 Computable Features
			4.2 One-Hot Encoding
			4.3 Decomposing Complex Features
			4.4 External Data
		5 Practical Demonstration: Airbnb Pricing
			5.1 Dataset and EDA
			5.2 Data Split
			5.3 Feature Transformers
			5.4 Indexing Categorical Features
			5.5 Set Features: Amenities and Host Verifications
			5.6 Decomposing Complex Features: host_since
			5.7 EDA
			5.8 Imputation
			5.9 Base Model
			5.10 First Iteration
			5.11 Feature Selection: Dropping Amenities
			5.12 Second Iteration
			5.13 Expanding Using External Data: SkyTrain Stations
			5.14 Case Study Wrap-Up
		6 Conclusions
		Further Readings and Other Sources
		References
	Clustering
		1 Introduction and Theoretical Foundations
			1.1 Hierarchical Cluster Analysis
			1.2 Partitioning
			1.3 Density-Based Spatial Clustering of Applications with Noise
			1.4 Cluster Evaluation and Profiling
		2 Practical Demonstration
			2.1 k-Means Clustering
			2.2 Hierarchical Clustering
				2.2.1 Top-Down Clustering
				2.2.2 Agglomerative (Bottom-Up) Clustering
				2.2.3 DBSCAN
		3 Research-Case
		References
	Dimensionality Reduction
		1 Introduction and Theoretical Foundations
			1.1 PCA
			1.2 tSNE
			1.3 UMAP
		2 Practical Demonstration
		Further Readings and Other Sources
		References
	Classification
		1 Introduction and Theoretical Foundations
			1.1 Motivation and Basic Concepts
			1.2 Evaluation
				1.2.1 Generalization Error
				1.2.2 Hold-Out Method and Cross Validation
				1.2.3 Hyperparameter Selection
				1.2.4 Evaluation Measures
					Confusion Matrix and Evaluation Measures Computed Therefrom
					ROC Analysis
					Categorical Cross-Entropy
			1.3 Data Preprocessing
				1.3.1 One-Hot Encoding
				1.3.2 Feature Scaling/Normalization
				1.3.3 Projection Methods
				1.3.4 Missing Values Imputation
				1.3.5 General Caveat
			1.4 Classification Methods
				1.4.1 K-Nearest Neighbor
					Advantages
					Disadvantages
				1.4.2 Logistic Regression
					Advantages
					Disadvantages
				1.4.3 Naïve Bayes
					Advantages
					Disadvantages
				1.4.4 Decision Trees
					Advantages
					Disadvantages
				1.4.5 Random Forest
					Advantages
					Disadvantages
				1.4.6 Gradient Tree Boosting
					Advantages
					Disadvantages
				1.4.7 Support Vector Machines
					Advantages
					Disadvantages
				1.4.8 Artificial Neural Networks
					Advantages
					Disadvantages
		2 Practical Demonstration
			2.1 Use Case
			2.2 The Data Set
			2.3 Descriptive Analysis of the Raw Data Set
			2.4 Aggregation of Data: Creation of User Profiles
			2.5 Classification of Visitors: Model Building
				2.5.1 Summary of Results
			2.6 Application of the Models
		Further Readings and Other Sources
		References
	Regression
		1 Introduction and Theoretical Foundations
			1.1 Motivation and Basic Concepts
			1.2 Evaluation
			1.3 Regression Methods
				1.3.1 Linear Regression
					Advantages
					Disadvantages
				1.3.2 Regression Trees
					Advantages
					Disadvantages
				1.3.3 Regression Tree Ensembles
					Advantages
					Disadvantages
				1.3.4 Support Vector Regression
					Advantages
					Disadvantages
				1.3.5 Artificial Neural Networks
					Advantages
					Disadvantages
		2 Practical Demonstration
			2.1 Use Case
			2.2 The Data
			2.3 Splitting Training and Test Data
			2.4 Prediction of Visitors´ Turnover: Model Building
				2.4.1 Summary of Results
			2.5 Application of the Model
		Further Readings and Other Sources
		References
	Hyperparameter Tuning
		1 Introduction and Theoretical Foundations
			1.1 Motivations
			1.2 Techniques
				1.2.1 Manual Search
				1.2.2 Grid Search
				1.2.3 Random Search
				1.2.4 Bayesian Optimization
				1.2.5 Genetic Algorithms
			1.3 Summary
		2 Practical Demonstration
			2.1 Data Preprocessing and Visualization
			2.2 Modelling
				2.2.1 Manual Search
				2.2.2 Grid Search
				2.2.3 Random Search
				2.2.4 Bayesian Optimization
				2.2.5 Genetic Algorithms
				2.2.6 Conclusion
		3 Research Case
		Further Readings and Other Sources
		References
	Model Evaluation
		1 Introduction
		2 Performance of Classification Models
			2.1 Performance at Fixed Operating Conditions
				2.1.1 Classification Accuracy
				2.1.2 Recall (Sensitivity)
				2.1.3 Precision
				2.1.4 F1
				2.1.5 Specificity
			2.2 ROC Curves, P-R Curves
		3 Regression
			3.1 Evaluation Scores
				3.1.1 Mean Square Error (MSE)
				3.1.2 Root Mean Square Error (RMSE)
				3.1.3 Mean Absolute Error (MAE)
				3.1.4 Coefficient of Determination (R2)
		4 Overfitting
			4.1 Random Sampling
			4.2 Cross-Validation
			4.3 Leave-One-Out
		5 Practical Demonstration
			5.1 Confusion Matrix
			5.2 ROC Curve
			5.3 Lift Curve
			5.4 Data Over- or Undersampling
		6 Research Case
		Further Readings and Other Sources
		References
	Interpretability of Machine Learning Models
		1 Introduction and Theoretical Foundations
			1.1 Introduction to Explainability
			1.2 Why Are Some Models Uninterpretable?
			1.3 For Whom Can Explainability Be Useful or Necessary?
			1.4 Why Should One Care About the Interpretability of ML Systems?
				1.4.1 Providing Trust
				1.4.2 Complying to Regulations
				1.4.3 Understanding Predictions
				1.4.4 Creating Better Models
			1.5 Explainability Frameworks
				1.5.1 Model Agnostic Strategy
				1.5.2 LIME
				1.5.3 ELI5
				1.5.4 Anchors
				1.5.5 Counterfactuals
				1.5.6 SHAP
				1.5.7 Deep Learning
				1.5.8 Cloud Platforms
			1.6 Fairness and Adversarial Attacks
		2 Practical Demonstration
			2.1 Data Description
			2.2 Data Preparation
			2.3 Classification Model
			2.4 Explicability
				2.4.1 SHAP: Global Interpretation
				2.4.2 SHAP: Local Interpretation
				2.4.3 Lime
			2.5 Conclusions
		3 Research-Case
		Further Readings and Other Sources
		References
Part III: Natural Language Processing
	Natural Language Processing (NLP): An Introduction
		1 Introduction and Theoretical Foundations
		2 Text Analysis in Tourism
		3 NLP Techniques
		4 Text Preparation and Pre-processing
			4.1 Language Detection
			4.2 Tokenisation
			4.3 Lowercasing and Removal of Punctuation
			4.4 Expand Contractions
			4.5 Removal of Stop Words
			4.6 Removal of URLs, HTML Tags, and Emotions/Emojis
			4.7 Correction of Spelling
			4.8 Stemming and Lemmatisation
			4.9 Part of Speech Tagging (POS)
			4.10 Named Entity Recognition (NER)
			4.11 Feature Extraction
			4.12 Visual EDA
		5 Challenges of Working with Text
		6 Practical Demonstration
			6.1 Tips for Using Python for an NLP Study
		Further Readings and Other Sources
		References
	Text Representations and Word Embeddings
		1 Introduction and Theoretical Foundations
			1.1 One Hot Encoding
			1.2 Bag-of-Words (CountVectorizer)
			1.3 TF-IDF
			1.4 Word Embeddings
				1.4.1 Word2vec
				1.4.2 Doc2Vec
				1.4.3 fastText
				1.4.4 GloVe
				1.4.5 ELMo
				1.4.6 BERT
			1.5 Visualization of Multidimensional Data
			1.6 The Future of Embeddings
			1.7 Embeddings in Tourism-Related Research
		2 Practical Demonstration
			2.1 BOW
			2.2 TF-IDF
			2.3 Word2vec
			2.4 BERT
		Further Readings and Other Sources
		References
	Sentiment Analysis
		1 Introduction
		2 Theoretical Foundations
		3 Practical Demonstration
		4 Research Case 1: Lexicon-Based Sentiment Analysis
		5 Research Case 2: Machine Learning Sentiment Analysis
		Further Readings and Other Sources
		References
	Topic Modelling
		1 Introduction and Theoretical Foundations
		2 Topic Modelling Approaches
			2.1 Latent Dirichlet Allocation (LDA)
				2.1.1 LDA Hyperparameters
			2.2 Non-negative Matrix Factorisation (NMF)
			2.3 Correlation Explanation (CorEX)
			2.4 Top2Vec
			2.5 BERTopic
		3 Topic Modelling Limitations and Challenges
			3.1 Evaluating and Interpreting Topics
		4 Topic Modelling in Tourism Studies
		5 Topic Model Toolkits and Software Solutions
		6 Practical Demonstration
			6.1 LDA: Data Preparation and Preprocessing
			6.2 Topic Modelling with CorEx
		Further Readings and Other Sources
		References
	Entity Matching: Matching Entities Between Multiple Data Sources
		1 Introduction and Theoretical Foundations
			1.1 Entity Matching Problem Statement
			1.2 Entity Matching Examples in the Travel Industry
			1.3 Overview of the Stages of an Entity Matching Approach
		2 Practical Demonstration
			2.1 Data Formatting and Pre-processing
			2.2 Candidate Generation
			2.3 Record Pair Comparison (Threshold-based)
			2.4 Record Pair Comparison (Neural-based)
		3 Summary
		Further Readings and Other Sources
		References
	Knowledge Graphs
		1 Introduction and Theoretical Foundations
			1.1 Fundamentals
			1.2 Modeling the Domain
		2 Steps Toward Building a Tourism Knowledge Graph
			2.1 Knowledge Graph Construction
			2.2 Knowledge Graph Identification
			2.3 Storing, Querying, and Using the Knowledge Graph
		3 Practical Demonstration and How-To Guidelines
			3.1 Hints and Tips
		4 Research Case
		Further Readings and Other Sources
		References
Part IV: Additional Methods
	Network Analysis
		1 Introduction and Theoretical Foundations
			1.1 Network Analysis in a Nutshell
		2 Practical Demonstration
		3 A Worked Example
		4 Research-Case
		Further Readings and Other Sources
		References
	Time Series Analysis
		1 Introduction and Theoretical Foundations
		2 Practical Demonstration
			2.1 Research Case
			2.2 Forecasting Methods
				2.2.1 Seasonal Naïve
				2.2.2 Single Exponential Smoothing (SES)
				2.2.3 Error Trend Seasonal (ETS)
				2.2.4 Forecasting Combination Method
			2.3 Measures of Forecasting Accuracy
		3 Results
		Further Readings and Other Sources
		References
	Agent-Based Modelling
		1 Introduction and Theoretical Foundations
			1.1 Tourism as a Complex System
			1.2 ABM Benefits
			1.3 Background of ABM
			1.4 Key ABM Features
				1.4.1 Agents
				1.4.2 Environment
				1.4.3 System-level
				1.4.4 Interactions
			1.5 Challenges When Applying ABM
			1.6 Tourism-related ABM
		2 Practical Demonstration
			2.1 Defining the Model Purpose
			2.2 Conceptual Model Set-up
			2.3 Model Description
			2.4 Model Components
				2.4.1 Agents
				2.4.2 Environment
				2.4.3 System-Level Variables
				2.4.4 Simulated Time
				2.4.5 Interactions
			2.5 Software
			2.6 Analysis
				2.6.1 Verification
				2.6.2 Validation
				2.6.3 Analysing Findings
		3 Research Case
			3.1 Contribution of Method
			3.2 Analysis
		Further Readings and Other Sources
			Agent-Based Modelling
			Overview, Design Concepts, and Details (ODD) + Decision-making (ODD+D) Protocols
			Software Selection
			GIS
			Analysis
		References
	Geographic Information System (GIS)
		1 Introduction
		2 Theoretical Foundations
			2.1 Data
			2.2 Analysis
			2.3 Creating Maps
		3 Practical Demonstration
		Further Readings and Other Sources
			Books
			Websites
			Tourism Applications
		References
	Visual Data Analysis
		1 Introduction and Theoretical Foundations
			1.1 Data Visualization Techniques
			1.2 Data Analysis Workflow
			1.3 Data Visualization in the Data Analysis Workflow
		2 Demonstration
			2.1 Datasets
			2.2 Data Analysis Workflow
				2.2.1 Discover
				2.2.2 Wrangle
				2.2.3 Profile
				2.2.4 Model
				2.2.5 Report
		References
	Software and Tools
		1 RapidMiner (by Wolfram Höpken)
		2 Orange (by Ajda Pretnar)
		3 KNIME Analytics Platform (by Stefan Helfrich)
		4 WEKA (by Tony C Smith)
		5 SAS Viya (by Piere Paolo Ippolito)
		6 BigML (by BigML)
		7 Dataiku (by Laura Wiest)
		8 DataRobot (by DataRobot)
		References
Glossary
Index