The burgeoning field of Data Science has had a significant impact in both academia and industry, and with good reason. The ability to make use of large amounts of data to find solutions for pressing problems in society, environment and business, constitutes both an opportunity and a challenge. Data is our best prospect to significantly improve our understanding of the world, ease the attrition in human/environment interaction, optimize resource allocation and mitigate human suffering and deprivation. Nevertheless, data, especially big data, pose difficult research challenges that need to be met and overcome, in order to bring these promises to fruition. To address these challenges is the mission of Data Science. Different types of data require specific tools methods and different analysis contexts require different analytic approaches. Spatial data science is concerned with research and problems where location is a central component of the problem. Spatial data science expertise is central in many practical problems, such as environmental management, public health, crime, remote sensing, just to mention a few. Significant progress has been made in the last few years, often driven by the industry. Academia needs to support this progress, contributing with general solutions and fundamental principles that can be of use in different contexts.