The GIScience perspective is grounded in spatial thinking. The aim of this knowledge area is to recognize, identify, and appreciate the explicit spatial, spatio-temporal and semantic components of the geographic environment at an ontological and epistemological level in preparation for modeling the environment with geographic data and analysis. To do this, one must understand the nature of space and time as a context for geographic phenomena.This knowledge area covers the ways in which views of the geographic environment depend on philosophical viewpoints, physics, human cognition, society, and the task at hand. This knowledge area also requires an understanding of the fundamental principles in the discipline of geography, the "language" of spatial tasks. On a more advanced level, this area incorporates mathematical and graphical models that formalize these concepts, such as set theory, algebra, and semantic nets. Because of its wide range of foundational principles, this knowledge area forms a basis for the other knowledge areas. Wise design and use of geospatial technologies requires an understanding of the nature of geographic information, the social and philosophical context of geographic information, and the principles of geography. This knowledge area is especially closely tied to Knowledge Areas Data Modeling (DM) and Design Aspects (DA), as generic data models and application designs need to be grounded in sound conceptual models. The foundations of geographic information have developed over several decades. Philosophical and scientific views on the nature of space and time have evolved since the ancient Greeks. Early papers during the Quantitative Revolution, such as Berry (1964), began to formalize the structure of information used in geographic inquiry.The fundamental data structures and algorithms comprising the GIS software developed in the 1960`s and 1970`s were based on implicit "common-sense" conceptual models of geographic information. During the 1980`s, several researchers questioned these underlying assumptions. Some were refuted, other confirmed, and many extended. However, the most rapid pace of development in this area was during the 1990`s with the rise of GIScience as a distinct discipline, and the many cooperative initiatives it comprised.The new millennium has seen some of these foundational principles incorporated into commercial software, thus making theoretical knowledge even more important for practitioners. It is expected that the concepts in this knowledge area will be learned gradually. An introductory course may cover only a few topics in a cursory manner, an intermediate course on data modeling or data analysis may consider several theoretical topics of practical application, and a number of graduate courses could cover each topic in a research-oriented environment. Discussion of this knowledge area includes several terms that can have multiple meanings. For the purposes of this document, two in particular require definition: Geographic: Almost any subject or discourse involving earthly phenomena, studied from a spatial perspective at a medium scale (sub-astronomical and super-architectural). Phenomenon: Any subject of geographic discourse that is perceived to be external to the individual, including entities, events, processes, social constructs, and the like.