Collaborators: Dr. Giorgos Mountrakis, Jida Wang, Dr. Dimitrios Triantakonstantis
Funders: NSF, NASA, SUNY ESF Graduate Assistantship
Motivation: Urbanization is an important issue concerning scientists from different disciplines such as urban planners, biologists, engineers and is also extensively considered by policy-makers. A computational model quantifying locations and quantities of urban growth would provide environmental and socioeconomic benefits. Traditional urban growth models are based on a single-algorithm fitting procedure and thus restricted on their ability to capture spatial heterogeneity.
Methodology: A GIS-based modeling framework titled Multi-Network Urbanization (MuNU) model is developed that integrates multiple neural networks. The MuNU model enables a filtering approach where input data patterns are automatically reallocated into appropriate neural networks with targeted accuracies. We hypothesize that observations classified by each neural network share greater homogeneity and can be modeled more accurately with a collection of targeted algorithms.
Findings: Land use and land cover datasets of two time snapshots (1977 and 1997) covering the Denver Metropolitan Area are used for model training and validation. Compared to a single-step algorithm – either a stepwise logistic regression or a single neural network – several major improvements are pronounced in the visual output of the MuNU model. Statistical validations further quantify the superiority of the MuNU model given its effectiveness to incorporate spatial heterogeneity. Improvements range between 3% and 11% at the 1km scale when compared with a single neural network and logistic regression respectively.
Relevant papers: One published in IJGIS, another to be submitted in 2-3 weeks (as of April, 2011).