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

PSC In The News

RSS Feed icon

Work by Brown, Jackson, Ryan cited in brief for UT Supreme Court case on race-conscious college admissions

Thompson says criminal justice policies led to creation of prison gangs like Aryan Brotherhood

Schmitz finds job loss before retirement age contributes to weight gain, especially in men

Highlights

Overview of Michigan's advanced research computing resources, Monday, June 27, 9-10:30 am, BSRB - Kahn Auditorium

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

Elizabeth Bruch promoted to Associate Professor

Susan Murphy elected to the National Academy of Sciences

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