Using Gis, Artificial Neural Networks and Remote Sensing to Model Urban Change in the Minneapolis-st.paul and Detroit Metropolitan Areas

We parameterized the GIS and neural net-based Land Transformation Model for the Detroit and Twin Cities Metropolitan Areas using historical land use data derived from aerial photography. We built several neural net models and attempted to test whether these models were transferable across the two metropolitan regions and whether a regional model provided as good a fit as a locally parameterized model. The overall accuracy of the model to predict urban transitions was 37% and 33% for the TCMA and DMA, respectively. An “internal” versus “external” learning exercise resulted in models that appeared to be fairly transferable in one case (DMA applied to TCMA) and not well transferable in the other case (TCMA applied to DMA). A “local” versus “regional” exercise produced results suggesting that learning from larger scale spatial patterns does not reduce the affect of the model to predict smaller, local trends. We discuss the implications of these two learning exercises and suggest ways in which the models could be improved. Overall accuracy of the presented models is judged against previous LTM applications in Michigan’s Grand Traverse Bay Watershed and Kuala Lumpur, Malaysia.