This study proposes a land use allocation model based on genetic principles and spatial characteristics of a landscape. The proposed land allocation model is developed to aid in optimizing competing and complementary objectives. An independent tool, MLUSOT (Macroscopic Land Use Spatial Optimization Tool) that seamlessly integrates with ArcGIS software package is developed to enable spatial analysis. The research study involves understanding of spatial elements in land use system, their interactions and influence on allocation of land uses. It focuses on evaluation of land use alternatives to accommodate community needs and protect future resources. This study encompasses an intense literature review to identify state-of-the art methodologies and current practices. Major shortcomings including lack of assessment of spatial interactions in the current practices are identified. A land use allocation model based on genetic principles and spatial interactions is devised to enhance current practices and address existing deficiencies in existing land use models.;In order to implement and test the proposed land allocation methodology extensive data was collected from various sources including federal, state and local agencies. Different sets of data were analyzed and finalized for calibration and validation of the allocation model. For calibration, 1998 and 2003 datasets were used. And for validation 2003, 2010, and 2025 datasets were used. The proposed model, MLUSOT is calibrated and validated using different datasets within the Front Range urban corridor of Colorado. Three cases studies are demonstrated to understand various application of MLUSOT. The findings from these case studies were satisfactory considering the proposed allocation model is a preliminary land use allocation model. The findings from all the case studies were compared with DRCOG (Denver Regional Council of Governments) obtained future land use maps. Only the findings from case study three were compared with IDRISI findings and DRCOG future land use plans. In all the case studies green areas and residential development were mostly over predicted. Overall, the findings were satisfactory and could be used as an input in advanced land use allocation models.
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