[Service_Parameters: Service_Topic='MODELS', Service_Term='LAND SURFACE MODELS']
Land Transformation ModelEntry ID: PURDUE_LTM
Abstract: The Land Transformation Model is a land use forecasting model as well as a tool that can be used to examine the spatial and temporal aspects of driving forces of land use change. The model uses a set of spatial interaction rules and machine learning, through neural net technology, to determine the nature of spatial interactions of drivers, such as transportation, urban infrastructure and proximity to lakes and rivers, that have historically contributed toward land use change in the past. This information is then used to conduct forecasting studies.
[Summary provided by Purdue University.]
ISO Topic Category
Access Constraints The Land Transformation Model is released under the GNU General Public License.
Distribution Media: Online
Fees: No fees
Robert Gilmore Pontius Jr., Wideke Boersma, Jean-Christophe Castella, Keith Clarke, Ton de Nijs, Charles Dietzel, Zengqiang Duan, Eric Fotsing, Noah Goldstein, Kasper Kok, Eric Koomen, Christopher D. Lippitt, William McConnell, Bryan Pijanowski, Snehal Pithadia, Alias Mohd Sood, Sean Sweeney, Tran Ngoc Trung, and Peter H. Verburg. In press. Comparing input, output and validation maps for several models of land change. Annals of Regional Science.
Pijanowski, B., K. Alexandridis and D. Mueller. 2006. Modeling urbanization in two diverse regions of the world. Journal of Land Use Science (1):83-108.
Tang, Z., B. Engel, K. Lim, B. Pijanowski and J. Harbor. 2005. Minimizing the impact of urbanization on long-term runoff. Journal of the Water Resources Association. 41(6): 1347-1359.
Tang, Z., B. A. Engel, B.C. Pijanowski, and K. J. Lim. 2005. Forecasting Land Use Change and Its Environmental Impact at a Watershed Scale. Journal of Environmental Management. 76: 35-45.
Pijanowski, B., S. Pithadia, K. Alexandridis, and B. Shellito. 2005. Forecasting large-scale land use change with GIS and neural networks. International Journal of Geographic Information Science. 19(2): 197-215.
Wiley, M. J., B. C. Pijanowski, P. Richards, C. Riseng, D. Hyndman, P. Seelbach and R Stevenson. 2004. Combining valley segment classification with neural net modeling of landscape change: A new approach to integrated risk assessment for river ecosystems. Proceedings of WEF 2004 Specialty Conference Series: Watershed 2004, Dearborn Michigan. Water Environment Federation.
Shellito, B. and B. Pijanowski. 2003. Using Neural Nets to Model the Spatial Distribution of Seasonal Homes. Cartography and Geographic Information Systems 30 (3):281-290.
Pijanowski, B.C., D. G. Brown, G. Manik and B. Shellito. 2002. Using Neural Nets and GIS to Forecast Land Use Changes: A Land Transformation Model. Computers, Environment and Urban Systems 26(6) 553-575.
Wayland, K., D. Long, D. Hyndman, B. Pijanowski, and S. Haack. 2002. Modeling The Impact Of Historical Land Uses On Surface Water Quality Using Ground Water Flow And Solute Transport Models. Lakes and Reservoirs 7: 189-199.
Pijanowski, B.C., B. Shellito and S. Pithadia. 2002. Using artificial neural networks, geographic information systems and remote sensing to model urban sprawl in coastal watersheds along eastern Lake Michigan. Lakes and Reservoirs 7: 271-285.
Pijanowsk, B., D. Hyndman and B. Shellito. 2001. The application of the Land Transformation, Groundwater Flow and Solute Transport Models for Michigan's Grand Traverse Bay Watershed. Proceedings of the American Planning Association, New Orleans, Lousiana, March 14, 2001.
Pijanowski, B.C., S.H. Gage, and D.T. Long. 2000. A Land Transformation Model: Integrating Policy, Socioeconomics and Environmental Drivers using a Geographic Information System; In Landscape Ecology: A Top Down Approach, Larry Harris and James Sanderson eds.
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