This volume discusses the concept of The Spatial University as part of the broad growth of spatial science and the need for spatial infrastructure in colleges and universities. The book centers on the development of U-Spatial, the spatial science infrastructure at the University of Minnesota that offers a range of spatial activities and services, including data access, training, and community building. Against a backdrop of the changing nature of research, teaching, and service in higher education, the story of U-Spatial anchors a broader discussion of what it means to be a spatial university. This narrative framing demonstrates-with specific examples-the importance of institutions offering dedicated spatial research infrastructure. In six chapters, the text explores the importance of spatial thinking, learning, and research for student and researcher success. The volume offers lessons that are applicable far beyond the University of Minnesota to apply to a broad array of domains and institutional specializations. The book will be useful to students, researchers, and policymakers concerned with how institutions can encourage spatial research, teaching, and service. It will also appeal to researchers and practitioners interested in broader uses of spatial science. This book shows how GIS can transform a university, speaking to the need for leadership in higher education around the power of bringing everything together using spatial and geographic concepts. Jack Dangermond Co-Founder and President, Esri

Transformation of the Earth's social and ecological systems is occurring at a rate and magnitude unparalleled in human experience. Data science is a revolutionary new way to understand human-environment relationships at the heart of pressing challenges like climate change and sustainable development. However, data science faces serious shortcomings when it comes to human-environment research. There are challenges with social and environmental data, the methods that manipulate and analyze the information, and the theory underlying the data science itself; as well as significant legal, ethical and policy concerns. This timely book offers a comprehensive, balanced, and accessible account of the promise and problems of this work in terms of data, methods, theory, and policy. It demonstrates the need for data scientists to work with human-environment scholars to tackle pressing real-world problems, making it ideal for researchers and graduate students in Earth and environmental science, data science and the environmental social sciences.