Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Former trainee Herbert says residential squatters may be a good thing

Work by Couper, Farley et al. shows impact of racial composition on neighborhood choice

Thompson details killings and shaping of official narrative in 1971 Attica prison uprising

More News

Highlights

Michigan ranked #12 on Business Insider's list of 50 best American colleges

Frey's new report explores how the changing US electorate could shape the next 5 presidential elections, 2016 to 2032

U-M's Data Science Initiative offers expanded consulting services via CSCAR

Elizabeth Bruch promoted to Associate Professor

Next Brown Bag

PSC Brown Bags
will resume fall 2016

Using Neural Nets and GIS to Forecast Land Use Changes: A Land Transformation Model

Publication Abstract

Pijanowski, B.C., Daniel G. Brown, B.A. Shellito, and G.A. Manik. 2002. "Using Neural Nets and GIS to Forecast Land Use Changes: A Land Transformation Model." Computers, Environment and Urban Systems, 26(6): 553-575.

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.

DOI:10.1016/S0198-9715(01)00015-1 (Full Text)

Browse | Search : All Pubs | Next