The stock-flow model of spatial data infrastructure development refined by fuzzy logic

Area of Science:

• Geographic Information Science
• Computer Science
• Systems Engineering

Background:

• Spatial Data Infrastructures (SDI) are crucial for managing and sharing geographic data across administrative levels.
• SDI development involves complex interactions between quantitative and qualitative (linguistic) variables.
• Existing system dynamics models may not fully capture the nuances of linguistic variables in SDI development.

Purpose of the Study:

• To investigate the suitability of different fuzzy logic models for simulating SDI development.
• To enhance existing system dynamics models by incorporating linguistic variables more effectively.
• To identify optimal fuzzy inference and defuzzification methods for SDI dynamic modeling.

Main Methods:

• Employed fuzzy logic to incorporate linguistic variables and their joint effects into an SDI development model.
• Utilized system dynamics as the foundational modeling technique.
• Evaluated two fuzzy inference methods (e.g., Average-Average) and two defuzzification methods (e.g., Center of Area) on an existing SDI model.

Main Results:

• The combination of Average-Average inference and Center of Area defuzzification demonstrated superior performance in modeling SDI dynamics.
• Fuzzy logic effectively integrates qualitative variables, improving the fidelity of SDI development simulations.
• Specific fuzzy logic configurations were identified as more capable of capturing the complex, dynamic nature of SDIs.

Conclusions:

• Fuzzy logic, particularly with Average-Average inference and Center of Area defuzzification, offers a robust approach for modeling spatial data infrastructure development.
• This methodology enhances the simulation accuracy of SDIs by better representing the interplay of diverse variables.
• The findings provide valuable insights for developing more sophisticated and realistic SDI models.