This Guide has been designed to be accessible to a wide range of readers — from undergraduates and postgraduates studying GIS and spatial analysis, to GIS practitioners and professional analysts. It is intended to be much more than a cookbook of formulas, algorithms and techniques ― its aim is to provide an explanation of the key techniques of spatial analysis using examples from widely available software packages. It stops short, however, of attempting a systematic evaluation of competing software products. A substantial range of application examples are provided, but any specific selection inevitably illustrates only a small subset of the huge range of facilities available. Wherever possible, examples have been drawn from non-academic sources, highlighting the growing understanding and acceptance of GIS technology in the commercial and government sectors.
The scope of this Guide incorporates the various spatial analysis topics included within the NCGIA Core Curriculum (Goodchild and Kemp, 1990) and as such may provide a useful accompaniment to GIS Analysis courses based closely or loosely on this programme. More recently the Education Committee of the University Consortium for Geographic Information Science (UCGIS) in conjunction with the Association of American Geographers (AAG) has produced a comprehensive “Body of Knowledge” (BoK) document, which is available from the AAG bookstore (http://www.aag.org/cs/aag_bookstore). This Guide covers materials that primarily relate to the BoK sections CF: Conceptual Foundations; AM: Analytical Methods and GC: Geocomputation. In the general introduction to the AM knowledge area the authors of the BoK summarize this component as follows:
“This knowledge area encompasses a wide variety of operations whose objective is to derive analytical results from geospatial data. Data analysis seeks to understand both first-order (environmental) effects and second-order (interaction) effects. Approaches that are both data-driven (exploration of geospatial data) and model-driven (testing hypotheses and creating models) are included. Data-driven techniques derive summary descriptions of data, evoke insights about characteristics of data, contribute to the development of research hypotheses, and lead to the derivation of analytical results. The goal of model-driven analysis is to create and test geospatial process models. In general, model-driven analysis is an advanced knowledge area where previous experience with exploratory spatial data analysis would constitute a desired prerequisite.” (BoK, p83 of the e-book version).