The degree to which a set of features tend to be clustered together (positive spatial autocorrelation) or be evenly dispersed (negative spatial autocorrelation) over the earth’s surface. When data are spatially autocorrelated, the assumption that they are independently random is invalid, so many statistical techniques are invalidated.
Published in Chapter:
Spatial Autocorrelation and Association Measures
J. Negreiros (Universidade Lusófona, Portugal), M. Painho (Instituto Superior de Estatística e Gestão de Informação - Universidade Nova de Lisboa, Portugal), I. Lopes (Universidade Lusófona, Portugal), and A.C. Costa (Instituto Superior de Estatística e Gestão de Informação - Universidade Nova de Lisboa, Portugal)
Copyright: © 2008
|Pages: 7
DOI: 10.4018/978-1-59904-885-7.ch199
Abstract
Several classical statements relating to the definition of GIS can be found in specialized literature such as the GIS International Journal, expressing the idea that spatial analysis can somehow be useful. GIS is successful not only because it integrates data, but it also enables us to share data in different departments or segments of our organizations. I like this notion of putting the world’s pieces back together again (ArcNews, 2000). “GIS is simultaneously the telescope, the microscope, the computer and the Xerox machine of regional analysis and the synthesis of spatial data” (Abler, 1988). “GIS is a system of hardware, software and liveware implemented with the aim of storing, processing, visualizing and analyzing data of a spatial nature. Other definitions are also possible” (Painho, 1999). “GIS is a tool for revealing what is otherwise invisible in geographical information” (Longley, Goodchild, Maguire, & Rhind, 2001). Certainly, GIS is not a graphic database.