Using neural networks and GIS to forecast land use changes: a Land Transformation Model Export

@article{citeulike:2718986, abstract = {The Land Transformation Model (LTM), which couples geographic information systems (GIS) with artificial neural networks (ANNs) to forecast land use changes, is presented here. A variety of social, political, and environmental factors contribute to the model's predictor variables of land use change. This paper presents a version of the LTM parameterized for Michigan's Grand Traverse Bay Watershed and explores how factors such as roads, highways, residential streets, rivers, Great Lakes coastlines, recreational facilities, inland lakes, agricultural density, and quality of views can influence urbanization patterns in this coastal watershed. ANNs are used to learn the patterns of development in the region and test the predictive capacity of the model, while GIS is used to develop the spatial, predictor drivers and perform spatial analysis on the results. The predictive ability of the model improved at larger scales when assessed using a moving scalable window metric. Finally, the individual contribution of each predictor variable was examined and shown to vary across spatial scales. At the smallest scales, quality views were the strongest predictor variable. We interpreted the multi-scale influences of land use change, illustrating the relative influences of site (e.g. quality of views, residential streets) and situation (e.g. highways and county roads) variables at different scales.}, author = {Pijanowski, Bryan C. and Brown, Daniel G. and Shellito, Bradley A. and Manik, Gaurav A.}, citeulike-article-id = {2718986}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/S0198-9715(01)00015-1}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0198971501000151}, citeulike-linkout-2 = {http://www.sciencedirect.com/science/article/B6V9K-45Y6F85-1/1/a16b9783f573d60a615976cd9ab27f85}, doi = {10.1016/S0198-9715(01)00015-1}, journal = {Computers, Environment and Urban Systems}, keywords = {pattern, spatial, urban, urban-growth}, month = {November}, number = {6}, pages = {553--575}, posted-at = {2008-04-25 17:33:15}, priority = {2}, title = {Using neural networks and GIS to forecast land use changes: a Land Transformation Model}, url = {http://dx.doi.org/10.1016/S0198-9715(01)00015-1}, volume = {26}, year = {2002} }

TY - JOUR ID - citeulike:2718986 L3 - citeulike-article-id:2718986 N2 - The Land Transformation Model (LTM), which couples geographic information systems (GIS) with artificial neural networks (ANNs) to forecast land use changes, is presented here. A variety of social, political, and environmental factors contribute to the model's predictor variables of land use change. This paper presents a version of the LTM parameterized for Michigan's Grand Traverse Bay Watershed and explores how factors such as roads, highways, residential streets, rivers, Great Lakes coastlines, recreational facilities, inland lakes, agricultural density, and quality of views can influence urbanization patterns in this coastal watershed. ANNs are used to learn the patterns of development in the region and test the predictive capacity of the model, while GIS is used to develop the spatial, predictor drivers and perform spatial analysis on the results. The predictive ability of the model improved at larger scales when assessed using a moving scalable window metric. Finally, the individual contribution of each predictor variable was examined and shown to vary across spatial scales. At the smallest scales, quality views were the strongest predictor variable. We interpreted the multi-scale influences of land use change, illustrating the relative influences of site (e.g. quality of views, residential streets) and situation (e.g. highways and county roads) variables at different scales. IS - 6 JF - Computers, Environment and Urban Systems EP - 575 TI - Using neural networks and GIS to forecast land use changes: a Land Transformation Model VL - 26 SP - 553 KW - pattern KW - spatial KW - urban KW - urban-growth AU - Pijanowski, Bryan AU - Brown, Daniel AU - Shellito, Bradley AU - Manik, Gaurav PY - 2002/11// UR - http://dx.doi.org/10.1016/S0198-9715(01)00015-1 ER -