Geographic information is everywhere from street addresses to location-aware systems and climate change models. As different as its applications are the methods required to represent and process it. While early computing systems for storing and processing geographic information were scientific computing applications usable only by specialized experts, geographic information today comes from a variety of sources and serves a variety of purposes and can be used by nearly everyone. Over the last decades, research has focused on what allows us to correctly handle geographic information and a science with its own methods has developed as a result. Key properties of geographic information are: uncertainty from measurement and data integration, context-dependence, and being one of the most prominent and earliest instances of Big Data.
This course is the first part of a series which teaches Geographic Information Science, its methods, history, and areas of current research. GIScience 1 focuses on the mathematical fundamentals underlying the representation of geographic information and on the computational framework that is required for storing and processing massive amounts of geographic information. It is a prerequisite for GIScience 2, which explores uncertainty handling and spatial analysis.
You can find a preliminary syllabus here.