Abstract
The system dynamics technique has been demonstrated to be a proper method by which to model and simulate the development of spatial data infrastructures (SDI). An SDI is a collaborative effort to manage and share spatial data at different political and administrative levels. It is comprised of various dynamically interacting quantitative and qualitative (linguistic) variables. To incorporate linguistic variables and their joint effects in an SDI-development model more effectively, we suggest employing fuzzy logic. Not all fuzzy models are able to model the dynamic behavior of SDIs properly. Therefore, this paper aims to investigate different fuzzy models and their suitability for modeling SDIs. To that end, two inference and two defuzzification methods were used for the fuzzification of the joint effect of two variables in an existing SDI model. The results show that the Average–Average inference and Center of Area defuzzification can better model the dynamics of SDI development.
Highlights
Spatial data infrastructure (SDI) is essential for successful collaborative spatial data management
Behavior reproduction is selected to assess the ability of the model in imitating the dynamic behavior of the real system according to the behavior of the inputs
With this approach in mind, we suggest redefining the modeling of the joint effect of linguistic variables on the simulation model of developing SDI (SMSDI) using fuzzy logic because the model involves several linguistic variables
Summary
Spatial data infrastructure (SDI) is essential for successful collaborative spatial data management. SDI has been evolving and as the SDI concept matures, its complex dynamic nature is increasingly realized (Chan and Williamson 1999; Erik de Man 2006; Hendriks et al 2012). Grus et al (2010) addressed SDI complexity and dynamics from a complex adaptive system (CAS) point of view. Other efforts have been made to model SDIs from different perspectives using different tools. Most efforts to date have been limited to conceptually explaining the complexity and dynamics of SDIs (Chan et al 2001; Erik de Man 2006; Grus et al 2006, 2010), and fewer efforts have been made to model the SDI’s complexities. The better the SDI complexities are modelled, the more reliable plans can be made to develop it
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
