Calibrating a neural network‐based urban change model for two metropolitan areas of the Upper Midwest of the United States

We parameterized neural net‐based models for the Detroit and Twin Cities metropolitan areas in the US and attempted to test whether they were transferable across both metropolitan areas. Three different types of models were developed. First, we trained and tested the neural nets within each region and compared them against observed change. Second, we used the training weights from one area and applied them to the other. Third, we selected a small subset (∼1%) of the Twin Cities area where a lot of urban change occurred. Four model performance metrics are reported: (1) Kappa; (2) the scale which correct and paired omission/commission errors exceed 50%; (3) landscape pattern metrics; and (4) percentage of cells in agreement between model simulations. We found that the neural net model in most cases performed well on pattern but not location using Kappa. The model performed well only in one case where the neural net weights from one area were used to simulate the other. We suggest that landscape metrics are good to judge model performance of land use change models but that Kappa might not be reliable for situations where a small percentage of urban areas change.

[1]  E. Lambin,et al.  Proximate Causes and Underlying Driving Forces of Tropical Deforestation , 2002 .

[2]  B. Pijanowski,et al.  Using neural networks and GIS to forecast land use changes: a Land Transformation Model , 2002 .

[3]  R. G. Pontlus.  Quantification Error Versus Location Error in Comparison of Categorical Maps , 2006 .

[4]  Robert J. Marks,et al.  Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks , 1999 .

[5]  Dennis Ojima,et al.  The global impact of land-use change , 1994 .

[6]  R. G. Pontius.  Statistical Methods to Partition Effects of Quantity and Location During Comparison of Categorical Maps at Multiple Resolutions , 2002 .

[7]  E. Lambin,et al.  Predicting land-use change , 2001 .

[8]  David M. Skapura,et al.  Building neural networks , 1995 .

[9]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[10]  James R. Anderson,et al.  A land use and land cover classification system for use with remote sensor data , 1976 .

[11]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[12]  J. Michael Scott,et al.  Predicting Species Occurrences: Issues of Accuracy and Scale , 2002 .

[13]  E. Lambin,et al.  Dynamics of Land-Use and Land-Cover Change in Tropical Regions , 2003 .

[14]  Bryan C. Pijanowski,et al.  Forecasting and assessing the impact of urban sprawl in coastal watersheds along eastern Lake Michigan , 2002 .

[15]  R. Walker.  Evaluating the Performance of Spatially Explicit Models , 2003 .

[16]  N. Gotelli.  Predicting Species Occurrences: Issues of Accuracy and Scale , 2003 .

[17]  E. E. Hardy,et al.  A Land Use and Land Cover Classification System for Use with Remote Sensor Data GEOLOGICAL SURVEY PROFESSIONAL PAPER 964 , 2006 .

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

[19]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..