Visualising Neural Network Training in Geographic Space
This paper presents a method which helps analysts understand the classification derived by any feed forward Artificial Neural Network (ANN).
Due to their non-linearity, hidden layer(s) and large number of connections feedforward ANNs are seen as too complex for users to understand. How a result is derived is not easy to interpret from the weights matrix. There have been attempts to reduce the complexity of network construction by automating, as much as possible, the structure and training. However this still does not enable the user to visualise what the network is doing, and why it is doing it. Such complexity means ANNs are used as black box classifiers where information is fed in one end and a result appears at the other.
Using any GIS it is possible to replicate the network structure and visualise it as geographic entities rather than as symbolic nodes. By visualising network training using the geographic nature of the data it becomes possible to gain an understanding of how a result is calculated. This leads to increased confidence in the results, as well as enabling greater control of training by non-expert users.
This paper will discuss the visualisation in geographic space of ANN training and present an example with discussion, ending with concluding remarks.