This article describes a genetic algorithm that can be used to generate land use plans that maximize both additive and spatial objectives in a vector-based GIS environment. To test the usefulness of the algorithm it was integrated in a geodesign tool and applied to a planning process in a peat meadow area in the Netherlands. The objective of this article is to demonstrate the potential and limitations of a genetic optimization algorithm to support collaborative land use planning workshops. The article shows how the algorithm can be used to 1. generate a set of alternatives to start a decision process, 2. To identify similarities and differences between collaborative planning results and results from optimization and 3. As an interactive tool using feedback from stakeholders. It proved possible to generate a relevant set of non-dominated solutions to begin the planning process. Comparing results from the optimizer with stakeholder results demonstrated that both approaches generated plans with similar values for the objectives but with large differences.in the maps that were produced. Integrating the optimizer in a geodesign tool demonstrated how the optimizer can complement stakeholder input if it is used as an interactive geodesign tool. Collaborative planning is based on the assumption that the stakeholders have knowledge that is not, or even cannot be represented in a formal model. The challenge is to combine optimization with stakeholder input in such a way that both approaches complement each other to get the best of both worlds.
|Number of pages||11|
|Journal||Landscape and Urban Planning|
|Publication status||Published - Oct 2015|
- Land use planning
- Genetic algorithm