The Need

Spatial aggregation is crucial in numerous industries where data from low-level spatial units, such as census blocks, must be grouped into larger, meaningful regions. Traditional approaches often struggle with the computational complexity of these tasks and tend to focus on finding a single solution. However, real-world applications require the ability to identify multiple optimal or near-optimal solutions to account for varying spatial configurations that meet the same objectives. This demand for robust, flexible, and efficient spatial optimization methods drives the need for advanced solutions capable of addressing these challenges.

The Technology

Our innovative heuristic method tackles the complexities of spatial aggregation by employing a two-phase algorithm: multistart and recombination. The multistart phase generates a diverse pool of random solutions, which are then refined using an efficient give-and-take algorithm. The second phase uses a recombination algorithm to create new, improved solutions, ensuring the discovery of multiple unique optimal or near-optimal configurations. This approach is both computationally efficient and capable of addressing the need for diverse spatial solutions.

Commercial Applications

• Urban planning and zoning optimization
• Market area analysis and territory design
• Environmental conservation and resource management
• Public health resource allocation
• Transportation and logistics planning

Benefits/Advantages

• Efficiently identifies multiple optimal or near-optimal solutions
• Enhances flexibility in decision-making with diverse spatial configurations
• Reduces computational intensity compared to traditional methods
• Scalable for a wide range of problem sizes and applications
• Customizable solution pool size to meet specific user needs