Summary
Contents
Subject index
The widespread use of Geographical Information Systems (GIS) has significantly increased the demand for knowledge about spatial analytical techniques across a range of disciplines. As growing numbers of researchers realize they are dealing with spatial data, the demand for specialized statistical and mathematical methods designed to deal with spatial data is undergoing a rapid increase. Responding to this demand, The SAGE Handbook of Spatial Analysis is a comprehensive and authoritative discussion of issues and techniques in the field of spatial data analysis.
Neural Networks for Spatial Data Analysis
Neural Networks for Spatial Data Analysis
Introduction
The term ‘neural network’ has its origins in attempts to find mathematical representations of information processing in the study of natural neural systems (McCulloch and Pitts, 1943; Widrow and Hoff, 1960; Rosenblatt, 1962). Indeed, the term has been used very broadly to include a wide range of different model structures, many of which have been the subject of exaggerated claims to mimic neurobiological reality.1 As rich as neural networks are, they still ignore a host of biologically relevant features. From the perspective of applications in spatial data analysis, however, neurobiological realism is not necessary. In contrast, it would impose entirely unnecessary constraints. Thus, the focus in this chapter is on neural networks as efficient ...
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