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. Keywords: Multiobjective land‐use allocation modellingGenerative modellingSpatial optimizationSustainable land use Acknowledgements This material is partly supported by the National Science Foundation Directorate for Social, Behavioral & Economic Sciences under Grant No. 0623482. The authors would also like to thank three anonymous reviewers for their valuable comments on an earlier version of the manuscript.