National Center for Geographic Information and Analysis

This outline gives an overview of the units that comprise the Core Curriculum in GIScience, along with hyperlinks to each. Note that hyperlinks are active only from the downloaded PDF document. 

This introduction provides context to the Core Curriculum in GIScience, including its design philosophy, organization of core concepts, editorial procedure, a development timetable, list of original developers and editorial notes. Note that hyperlinks are active only from the downloaded PDF document.

This unit describes the origin, components, functioning, and uses of land information systems, with particular emphasis on systems for maintaining cadastral (land ownership) data.

This unit presents an introduction to multimedia and virtual reality as they relate to GIS, including definitions, technological issues, computer science aspects, user interface considerations, and applications.

This unit outlines an introduction to the problem of uncertainty propagation in GIS; the definition and identification of a stochastic error model for quantitative spatial attributes; a description of common error propagation techniques; applications of the theory; and how the results of an uncertainty analysis may be used to improve the accuracy of GIS products.

This unit has two primary segments: Unit 006 - Human Cognition of the Spatial World and Unit 007 - Asking Geographic Questions. The first section sets the scene by differentiationg between objects and processes and by  using fundamental understanding of the spatial world to provide a necessary knowledge  base for GIS. The second section builds on the knowledge base to demonstrate how specific  knowledge about objects and spatial relations among them can be unpacked by the  mechanism of asking geographic questions.

This unit discusses error detection and evaluation by graphical methods. It describes example methods used in statistics, cartography and GIS; challenges including issues in graphic design, metadata, error analysis and detection; techniques for analyzing raw data; and a framework for graphical methods.

This unit presents a definition of artificial neural networks (ANN); describes different types of ANN and their applications in geography and spatial analysis; explains differences between ANN and AI and between ANN and statistics; and describes how to apply a supervised ANN in model classification and function estimation problems.

This unit explains the purpose of exploratory data analysis and exploratory spatial data analysis (ESDA); the model which underlies statistical ESDA; the importance of cartographical, graphical and tabular tools in processing geographical data for ESDA; a range of ESDA techniques and their use in analyzing geographical data; and the limitations of current GIS software for undertaking ESDA.

This unit covers topics including the basic elements of a network; how networks are represented; commonly used shortest path algorithms; and common application areas in which networks are used.