Spatial network big data (SNBD) can be widely found in every corner of modern society. Examples include temporally detailed road maps that provide car speeds every minute for every road segment and GPS trace data from our cell phones. SNBD features the volume, variety, and update velocity that exceeds the capacity of commonly used spatial computing technologies and facilities. It generates obstacles for current methods, models, and algorithms to perform well. To develop the spatial network big database system (SNDBS), three key challenges have to be overcome. First, a new data model has to be introduced to present the SNBD structures. Second, scalable query processing and optimization methods are to be explored. Finally, efficient input/output (I/O) storage and access methods are to be designed on SNBD. This book is a comprehensive introduction to the literature and knowledge on SNBD. It also provides readers with a start toward overcoming these challenges.

The book is organized into seven chapters. Chapter 1 covers SNBD concepts, application, and computational challenges. Chapter 2 reviews the basic graph algorithms. Chapter 3 to 5 introduces three important problems including capacity constrained network Voronoi diagrams, distance-constrained k spatial sub-networks, and evacuation route planning (ERP). Each one of the problems is related to a graph partition problem and presents an NP-hard challenge. Chapter 6 introduces the storage schemes for spatio-temporal network datasets. At the end of the book, chapter 7 provides a summary. Chapters 3 to 6 in particular all follow a similar and clear narrative structure including these sections: problem definition, literature review, existing algorithms, case study, summary, and related references. This structure enables readers to easily and deeply understand the topic. Many of the examples included in these chapters are also very interesting. For instance, in chapter 5, Hurricane Rita and the Tohoku tsunami are used as examples to illustrate the essential importance of evacuation route planning (ERP) in civic emergency preparedness.

The topic of this book is attractive and beneficial to readers and researchers who are interested in spatial networks, graph data, big data, and databases. It includes a clear structure when introducing the basic information, existing methods, and practical applications. The case studies provide a practical view of SNBD that should help readers better understand this area.