Spatial optimization as a generative technique for sustainable multiobjective land-use allocation

In this paper, we examine the applicability of spatial optimization as a generative modelling technique for sustainable land-use allocation. Specifically, we test whether spatial optimization can be used to generate a number of compromise spatial alternatives that are both feasible and different from each other. We present a new spatial multiobjective optimization model, which encourages efficient utilization of urban space through infill development, compatibility of adjacent land uses, and defensible redevelopment. The model uses a density-based design constraint developed by the authors. The constraint imposes a predefined level of consistent neighbourhood development to promote contiguity and compactness of urban areas. First, the model is tested on a hypothetical example. Further, we demonstrate a real-world application of the model to land-use planning in Chelan, a small environmental amenity town in the north-central region of the State of Washington, USA. The results indicate that spatial optimization is a promising method for generating land-use alternatives for further consideration in spatial decision-making.