Modelling spatial interaction using a neural net

Neurocomputing has the potential to revolutionise many areas of urban and regional modelling by providing a general purpose systems modelling tool in applications where data exist. This chapter examines the empirical performance of a feedforward neural net as the basis for representing the spatial interaction contained within journey to work data. The performance of the neural net representation is compared with various types of conventional model. It is concluded that there is considerable potential for many more neural net applications in this and related areas.

[1]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[2]  Marvin Minsky,et al.  Perceptrons: An Introduction to Computational Geometry , 1969 .

[3]  R. H. Ketellapper,et al.  Exploratory and explanatory statistical analysis of spatial data , 1979 .

[4]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[5]  Robert M. Farber,et al.  How Neural Nets Work , 1987, NIPS.

[6]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[7]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Stan Openshaw,et al.  Alternative methods of estimating spatial interaction models and their performance in short-term forecasting , 1979 .

[9]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[10]  A. A. Mullin,et al.  Principles of neurodynamics , 1962 .

[11]  Stan Openshaw,et al.  An Empirical Study of Some Spatial Interaction Models , 1976 .

[12]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Tarun Khanna,et al.  Foundations of neural networks , 1990 .

[14]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[15]  W. Tobler,et al.  An Alternative Formulation for Spatial-Interaction Modeling , 1983 .

[16]  Philip D. Wasserman,et al.  Neural computing - theory and practice , 1989 .

[17]  Stan Openshaw.  Some Suggestions Concerning the Development of Artificial Intelligence Tools for Spatial Modelling and Analysis in GIS , 1992 .

[18]  Igor Aleksander,et al.  Introduction to Neural Computing , 1990 .