Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the Athens metropolitan area

This paper presents an artificial intelligence approach integrated with geographical information systems (GISs) for modeling urban evolution. Fuzzy logic and neural networks are used to provide a synthetic spatiotemporal methodology for the analysis, prediction and interpretation of urban growth. The proposed urban model takes into account the changes over time in population and building use patterns. A GIS is used for handling the spatial and temporal data, performing contingency analysis and mapping the results. Spatial entities with similar characteristics are grouped together in clusters by the use of a fuzzy c-means algorithm. Each cluster represents a specific level of urban growth and development. A two-layer feed-forward multilayer perceptron artificial neural network is then used to predict urban growth. The model, applied to the prefecture of Attica, Greece, delineates the current and future evolution trends of the Athens metropolitan area, which are illustrated by maps of the urban growth dynamics. The proposed methodology aims to assist planners and decision makers in gaining insight into the transition from rural to urban.

► We use fuzzy clustering and neural networks to analyze and predict urban evolution.
► Urban evolution estimates are based on temporal data (population and buildings).
► Fuzzy clustering delineates the urban state of each spatial unit over time.
► Spatial contingency is examined by analyzing the changes of neighboring areas.
► A neural network predicts future urban evolution per spatial unit.