This book explores the essential computing paradigm of crowdsourcing, particularly the rise in utility and application of crowdsourced spatiotemporal data. This book begins with an overview of how the ubiquity of mobile internet services and the growth of the sharing economy have created ideal conditions for the growth of spatial crowdsourcing, why humans are ideal contributors to spatiotemporal tasks that must be completed at specific locations and times, and the core challenges of crowdsourcing that data. Readers then get a deeper dive into practical application issues. In particular, this book centers on identifying four core algorithmic issues in spatial crowdsourcing: (1) task assignment, (2) quality control, (3) incentive mechanism design, and (4) privacy protection. Readers find this books contains a comprehensive and systematic review of existing research on these issues and analyzes representative spatial crowdsourcing applications with explanations of how those four technical issues enable them. This book describes a number of real-life examples that demonstrate recent successes, techniques, and specific challenges in crowdsourcing for applications that provide urban services, such as ride sharing and live-mapping. Finally, this book discusses open questions that need to be addressed in future spatial crowdsourcing research and applications.