Modelling urbanization patterns in two diverse regions of the world
Authors:
B. C. Pijanowski a;
K. T. Alexandridis ab;
D. M
ller c
ller c
| Affiliations: | a Human-Environment Modeling and Analysis Laboratory (HEMA), Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana, USA |
| b CSIRO Sustainable Ecosystems, Davies Laboratory, University Drive, Townsville, QLD, Australia | |
| c Research Group on Postsocialist Land Relations, Humboldt University Berlin, Luisenstr. 56, Berlin, Germany |
DOI:
10.1080/17474230601058310
Publication Frequency:
4 issues per year
Subjects:
Applied & Economic Geology;
Geographic Information Systems;
Plant & Animal Ecology;
Transport Geography;
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Abstract
We present work applying a similarly parameterized urbanization model to two diverse regions of the world, one in the USA and another in Albania. Eight calibration metrics are used to estimate model goodness of fit: four location-based measures (e.g. kappa), and four patch metrics based on patch size, shape and configuration. We conclude that if we use location goodness of fit estimates, the model fits observed data very well for most simulations. The model fit to data better in Albania than in the USA probably owing to top-down land ownership policies occurring in Albania and owing to the fact that commonly used land use change model drivers, such as distance to road, are not likely to capture individual behaviours that are important in the USA. Patch metrics provided additional information on model fit to observed data, and we suggest that, in some circumstances, patch metrics may be more useful than location metrics to calibrate a land use change model.
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| Keywords: Urbanization; Neural networks; Calibration metrics; Landscape patterns metrics |
| view references (28) : view citations |

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