Spatial Change Optimization: Integrating GA with Visualization for 3D Scenario Generation

Urban spatial analysis is becoming an increasingly complex problem due to the overwhelming demands imposed by the population and several other factors. Consequently, tools are needed to solve complex urban spatial problems that are multiobjective in nature. This study presents a multiobjective optimization approach to generating alternative land use scenarios and offers a visual evaluation tool for assessing the Pareto solutions. Typically, with genetic algorithms (GA), decision makers are finally left with alternative solutions in the form of the Pareto set, from which one or a few more will be chosen. Hence, a visualization tool is employed in this study, whereby the decision makers can better evaluate the alternative solutions from the Pareto set. Modeling futuristic land uses is devised as an optimization problem wherein spatial configurations are created through the use of evolutionary algorithms. With the goal of sustainable urban land use planning, the evolutionary algorithm is designed for multiple objectives, such as maximization of per capita green space, maximization of urban housing density, maximization of public service space, and conflict resolution among neighboring land uses. The results evince the validity of the GA framework and also corroborate the utility of the virtual scenarios.