This textbook covers the key topics in mobility data analysis, including all steps of the data science pipeline illustrated with real-world examples.

The book is composed of three parts. Part I Fundamental Concepts provides the background for this book by introducing spatial and temporal databases and motivating the need for mobility databases. Further chapters in this part are devoted to a formal model for representing mobility data, an introduction to mobility data visualization, and the topic of querying mobility databases. Part II Advanced Topics covers topics such as query processing and indexing, illustrated with PostgreSQL, introduces mobility data warehouses using synthetic data, and concludes with distributed mobility databases. Part III Mobility Analytics covers important topics like mobility data cleaning, including the identification of erroneous data, and mobility analysis using foundational algorithms for spatial and mobility data. It also includes an urban mobility use case that illustrates the concepts presented throughout the book in a real application setting.

This textbook is written for undergraduate and graduate computer science courses on mobility data science. As such, it follows a pedagogical style to make the work of the instructor easier and to help students to understand the concepts being delivered, complementing the presentation with exercises and a companion GitHub repository. SQL is used as a high-level language for analytics, allowing students to write complex data science code, while abstracting away implementation details. Researchers and practitioners who are interested in an introduction to the area of mobility data science will also find the book a useful reference.

Covers the key topics in mobility data analysis, with all steps of the pipeline illustrated by real-world examples Written for undergraduate and graduate computer science courses on mobility data science Complemented by a GitHub repository with SQL and Python scripts to reproduce all examples and use cases

Autorentext

Mahmoud Sakr is a professor at the Université libre de Bruxelles. His main research scope is mobility data science, and he has published many papers on this topic. He is a main contributor and a co-founder of MobilityDB, a mobility database that extends PostgreSQL and PostGIS with temporal and spatiotemporal data types. He is also a main contributor and co-chair of the Moving Feature Standards Working Group of the Open Geospatial Consortium. He participates in several Horizon Europe research projects on the topic of mobility data science, including Mobispaces and EMERALDS. Alejandro Vaisman is a professor at the Instituto Tecnológico de Buenos Aires, where he also chairs the graduate program in data science. He has been a visiting researcher at the University of Toronto, Universidad Politécnica de Madrid, Universidad de Chile, Hasselt University, and Université libre de Bruxelles (ULB). His research interests are in the field of databases, business intelligence, and geographic information systems. He has coauthored many scientific papers published at major conferences and in major journals. Esteban Zimányi is a professor at Université libre de Bruxelles. His current research interests include spatio-temporal and mobility databases, data warehouses, and geographic information systems. He has coauthored and coedited several books and published many papers on these topics. He is also the coordinator of the Erasmus Mundus master's and doctorate programs "Information Technologies for Business Intelligence" and "Big Data Management and Analytics'' as well as the Marie Sklodowska-Curie doctorate program "Data Engineering for Data Science". He is a main contributor and a co-founder of MobilityDB.

Inhalt

Preface .- Part I Fundamental Concepts 1 Mobility Data Science 2 Spatial, Temporal, and Mobility Databases 3 Mobility Data Representation 4 Mobility Data Visualization 5 Querying Mobility Databases .- Part II Advanced Topics 6 Query Processing and Indexing 7 Mobility Data Warehouses 8 Distributed Mobility Databases .- Part III Mobility Analytics 9 Mobility Data Cleaning 10 Mobility Data Analysis 11 Urban Mobility Use Case 12 Concluding Remarks.