NDLI: Calibrating nonparametric cellular automata with a generalized additive model to simulate dynamic urban growth

Content Provider	Springer Nature Link
Author	Feng, Yongjiu Tong, Xiaohua
Copyright Year	2017
Abstract	Understanding factors that drive urban growth is essential to cellular automata (CA) based urban modeling. Multicollinearity among correlated factors may cause negative effects when building CA transition rules, leading to a decrease in simulation accuracy. We use a nonparametric generalized additive model (GAM) to evaluate these relationships through flexible smooth functions to capture the dynamics of urban growth. A GAM-based CA (termed GAM-CA) model was then developed to simulate the rapid urban growth in Shanghai, China from 2000 to 2015. GAM highlights the significance of each candidate factor driving urban growth during the past 15 years. Compared to logistic regression, the GAM-CA transition rules fitted the observed data better and yielded improved overall accuracy and hence more realistic urban growth patterns. The new CA model has great potential for capturing key driving factors to simulate dynamic urban growth, and can predict future scenarios under various spatial constraints and conditions.
Starting Page	1
Ending Page	15
Page Count	15
File Format	PDF
ISSN	18666280
Journal	Environmental Earth Sciences
Volume Number	76
Issue Number	14
e-ISSN	18666299
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2017-07-20
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Urban growth Cellular automata Nonparametric transition rules Generalized additive model Shanghai Geology Hydrology/Water Resources Geochemistry Environmental Science and Engineering Terrestrial Pollution Biogeosciences
Content Type	Text
Resource Type	Article
Subject	Global and Planetary Change Earth-Surface Processes Soil Science Environmental Chemistry Pollution Geology Water Science and Technology

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