Using the ART-MMAP Neural Network to Model and Predict Urban Growth: A Spatiotemporal Data Mining Approach

Weiguo Liu and Karen C Seto
Additional contact information
Weiguo Liu: Department of Geography and Planning, University of Toledo, Toledo, OH 43606, USA
Karen C Seto: Department of Geological and Environmental Sciences, Stanford University, Stanford, CA 94305, USA

Environment and Planning B, 2008, vol. 35, issue 2, 296-317

Abstract: Predicting patterns of urban growth will be a major challenge for policy makers and environmental scientists in the 21st century. How cities grow—their shape and size—will have enormous implications for environmental sustainability and infrastructure needs. This paper presents a spatiotemporal ART-MMAP neural method to simulate and predict urban growth. Factors that affect urban growth—that is, transportation routes, land use, and topography—were directly used as inputs to the neural network model for model calibration. The calibrated network was then applied to a study site—St Louis, Missouri—to predict future urban growth and to examine future land development scenarios. This paper also introduces an effective and straightforward method for model validation and accuracy assessment, the prediction error matrix, which has been used in the pattern recognition field for several decades. In order to assess the performance of the neural network model, an in-depth accuracy assessment was conducted in which the model results were compared against a null model, an alternative naïve model, and two random models. The neural network model consistently outperformed the naïve model and two random models, and produced similar or better results than the null model. Furthermore, we evaluated the models' performance at different spatial resolutions. The prediction accuracy increases when spatial resolution becomes coarser. One particularly interesting result is that when the results are aggregated to 1 km spatial resolution, there is 100% accuracy of urban growth predicted by the neural network model versus actual urban growth.

Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1068/b3312 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:35:y:2008:i:2:p:296-317

DOI: 10.1068/b3312

Access Statistics for this article

More articles in Environment and Planning B
Bibliographic data for series maintained by SAGE Publications ().