Multi-objective urban land use optimization using spatial data: A systematic review

Land use optimization is a promising approach to achieve urban sustainability. Despite the increasing number of literature on land use optimization, a little investigation is made to systematically review urban land use optimization: its objectives, methodological approaches, and spatial data used etc. This creates room to review the methodological approaches to urban land use optimization. This study systematically reviews 55 articles following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to understand important aspects of urban land use optimization. We have found that the most common objectives which were used in urban land use optimization are maximization of spatial compactness (16.67 %, n=28) and maximization of land use compatibility (13.69%, n=23), followed by maximization of land use suitability (11.90%, n=20). The findings suggest that a) one and only one land use in each cell, b) minimum and maximum area of certain land use, and c) restriction on specific land use change are the important constraints. This study also identifies that urban sustainability has been merely touched upon in urban land use optimizations. While environmental (including ecology) and economic aspects of urban sustainability were included in 46.67% and 43.33% studies respectively, the social aspect (10%, n= 3) was mostly ignored. Our findings also indicate that there is no generalized method to measure economic, environmental, and social benefits from land use planning. This study also finds that the Genetic Algorithm (GA) (32.14%, n=18) accounts for a major contribution to solve the urban land use optimization problem. Based on the findings, this study proposes some recommendations for further research and practice. The most important of them include a) framing land use optimization objective functions considering urban sustainability, b) developing a standard method to calculate values of objective functions, and c) integrating a participatory approach with mathematical optimization to derive more feasible solutions. These recommendations could be the scope of future research.