Urbanization prediction with an ART-MMAP neural network based spatio- temporal data mining method

Data mining methods have been widely and successfully used in many fields in the last decade. And geographic knowledge discovery and spatial data mining also have attracted more attentions recently. This paper presents an ART-MMAP neural network based spatio-temporal data mining method to simulate and predict urban expansion. The spatial matrices derived from different urban related features, i.e. transportation, land use, topography, were directly used as inputs to the neural network model for learning. The trained network was then applied to research region to predict the land use change to urban. The learning and prediction process are automatic and free of intervention. The method has been successfully validated with the urban growth prediction at St. Louis region at Missouri, USA.