Abstract
In recent years, models of land-use change and urban growth have become important tools for city planners, economists, ecologists, and resource managers. In most models, future land-use changes are forecasted based on past development pattern and expansion to periphery. While today, metropolitan areas employ smart-growth strategies. The main objectives in this study are according to the smart-growth infill. In this approach, transmission of incompatible land uses to the outside of the city boundary, redevelopment, improvement, and renovation of urban old district and worn-out texture and reuse of abandoned land to new urban development are considered. In fact, the objective is the using of the infill development pattern to modeling approach for simulating urban future development using potentials inside the city. This paper presents a Land Transformation Model of urban land-use change based on an artificial neural network and a geographical information system. For developing this approach, future development of Tabriz city based on past development trend and infill development pattern is modeled. The modeling result based on past development pattern shows that the 31.26 % of green spaces and 60.93 % of agricultural land and wasteland will be destroyed and the built area will increase 89.75 % from 2005 to 2021. Development of infill development pattern model can regularize urban expansion in the coming decades. The result of infill development pattern, show that the built area will increase 40.32 percent and agricultural land and wasteland area decrease 32.67 percent until 2021. In fact, redevelopment of urban land uses in infill development pattern until 2021, not only preserve the green spaces and agricultural areas but also improve and rehabilitate old and worn-out textures.
Highlights
In recent years, models of land-use change and urban growth have become important tools for city planners, economists, ecologists, and resource managers
Conclusions: redevelopment of urban land uses in infill development pattern until 2021, preserve the green spaces and agricultural areas and improve and rehabilitate old and worn-out textures
The model relies on geographic information systems (GIS), artificial neural network (ANN) routines, remote sensing, and customized geospatial tools and can be used to help understand what factors are most important to land-use change
Summary
Models of land-use change and urban growth have become important tools for city planners, economists, ecologists, and resource managers. The rapid growth of urban areas has led to complex problems, including traffic congestion, environmental pollution, reduced open space, the deterioration of old downtown centers, and unplanned or poorly planned land development (Lee, 2008). To address these urban problems and to identify approaches for Models of land-use change, which couple biophysical and socioeconomic drivers, are needed to address the complex issue of land-use change and build up sustainable land-use practices and policies (Van Daalen et al, 2002; Lambin and Geist, 2006). It should focus on filling gaps in the existing urban areas (Municipal Research & Services Center of Washington, 1997)
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