JSM 2005 Online Program

Abstract #302919

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Activity Number:	74
Type:	Contributed
Date/Time:	Sunday, August 7, 2005 : 8:00 PM to 9:50 PM
Sponsor:	General Methodology
Abstract - #302919
Title:	Nonlinearity in a Large Dataset for an Urban Development Study
Author(s):	Wei-hong Wang*+ and Pin-Shuo Liu
Companies:	The College of New Jersey and William Paterson University
Address:	Department of Mathematics and Statistics, Ewing, NJ, 08628, United States
Keywords:	polynomial logistic regression ; predictive modeling ; Classification Tree ; Neural Network ; misclassification rates ; Genetic Algorithm
Abstract:	This project investigates the nonlinearity in a dataset for an urban development study. The dataset contains 1.4 million cases with 14 predictors and one target variable with binary outcomes. The relationships of some of the predictive variables are clearly nonmonotonic, hence the standard logistic regression or polynomial logistic regression is not adequate in the predictive modeling, regardless of the use of different link functions. In this paper, we compare a number of modeling approaches, including Classification Tree, Neural Network, Genetic Algorithm, and Stochastic Gradient Boosting by the corresponding misclassification rates, lift charts, and a number of other model assessment measures. Finally, Geographic Information Systems (GIS) are used to create an urban growth database to estimate the possible relationship between urban growth and several growth, stimulant, and deterrent variables.

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