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Learning To Think Spatially
to the greater flexibility and power of digital technologies. Moreover, the older GISciences evolved in an era of distinct, analog technologies—as long as the paper and pen of cartography had little in common with the analytical stereoplotter of photogrammetry or the theodolite of surveying, there was every reason for them to evolve separately, with separate research agendas. Today, however, all three fields have embraced digital technology wholeheartedly. They serve overlapping applications and face similar issues of representation, database design, accuracy, and visualization.
The world of geographic information has also grown more complex, as new questions have arisen that require the skills and principles of other sciences. Remote sensing, the science of Earth observation, is now an important source of geographic information with its own issues and principles. The unique problems of spatial information have begun to intrigue computer scientists, and spatial databases, computational geometry, and spatial indexing are now recognized subfields of computer science with special significance for GIScience (Liu et al., 2003; Worboys, 1995). Spatial statistics and geostatistics, recognized subfields of statistics, provide important frameworks for the study of accuracy and uncertainty in GIScience (Zhang and Goodchild, 2002), and for the development of advanced methods of spatial analysis, modeling, and visualization (Haining, 2003; Longley and Batty, 2003; O’Sullivan and Unwin, 2003). GIScience is a legitimate subfield of information science, and it is particularly attractive to information scientists because of the well-defined nature of geographic information and the comparatively advanced state of knowledge about this information type. Finally, an important section of the GIScience research agenda asks questions of interest to cognitive scientists: How are geographic knowledge and skills acquired by the human brain, and how can GIS be made more readily understood and usable by humans?
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