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Abstract
Urban growth prediction has acquired an important consideration in urban sustainability. An effective approach of urban prediction can be a valuable tool in urban decision making and planning. A large urban development has been occurred during last decade in the touristic village of Pogonia Etoloakarnanias, Greece, where an urban growth of 57.5% has been recorded from 2003 to 2011. The prediction of new urban settlements was achieved using fractals and theory of chaos. More specifically, it was found that the urban growth is taken place within a Sierpinski carpet. Several shapes of Sierpinski carpets were tested in order to find the most appropriate, which produced an accuracy percentage of 70.6% for training set and 81.8% for validation set. This prediction method can be effectively applied in urban growth modelling, once cities are fractals and urban complexity can be successfully described through a Sierpinski tessellation.
Highlights
Fractals are dynamic objects, where their geometry depends on an evolutionary process
This prediction method can be effectively applied in urban growth modelling, once cities are fractals and urban complexity can be successfully described through a Sierpinski tessellation
The accuracy of the urban growth prediction is calculated by overlaying the Sierpinski carpet and the land use map of 2011 and measuring the number of points which are situated within the remaining area of Sierpinski carpet after each iteration
Summary
Fractals are dynamic objects, where their geometry depends on an evolutionary process. An important characteristic of fractals is the complexity of spatial objects which it can be described by self-similarity and scaledependence [1]. The complexity which exists in nature, science must adopt some simplifications (Occam’s razor plays an important role for selecting the simplest explanation) in order to achieve a better representation of natural phenomena. Instead of retrieving complexity with simplicity, multiplicity may be an alternative aspect of handling real world. This means that an intermediate approach is followed where an occasion is divided in many simple parts, where a solution close to reality is obtained (complexity: difficult to understand, simplicity: easy to understand, multiplicity: closer to reality)
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