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A methodological approach to urban landuse change modeling using infill development pattern——a case study in Tabriz, Iran

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Ecological Processes 2016年第1期5卷 1-15页

作者： Akbar Rahimi Department of Landscape Engineering University of TabrizBolvare 29 BahmanTabrizIran

Introduction:In recent years,models of land-use change and urban growth have become important tools for city planners,economists,ecologists,and resource *** most models,future land-use changes are forecasted based on past development pattern and expansion to *** today,metropolitan areas employ smart-growth *** main objectives in this study are according to the smart-growth *** 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 *** fact,the objective is the using of the infill development pattern to modeling approach for simulating urban future development using potentials inside the ***:This paper presents a Land Transformation Model of urban land-use change based on an artificial neural network and a geographical information *** developing this approach,future development of Tabriz city based on past development trend and infill development pattern is ***: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 *** of infill development pattern model can regularize urban expansion in the coming *** 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 ***: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.

关键词： Land Transformation Model infill development Urban expansion Sprawl Artificial neural network GIS

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