The stock-flow model of spatial data infrastructure development refined by fuzzy logic
(2016) In SpringerPlus 5(1).- 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... (More)
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.
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- author
- Abdolmajidi, Ehsan LU ; Harrie, Lars LU and Mansourian, Ali LU
- organization
- publishing date
- 2016-12-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Defuzzification, Fuzzy logic, Inference method, Spatial data infrastructure, System dynamics technique
- in
- SpringerPlus
- volume
- 5
- issue
- 1
- article number
- 267
- publisher
- Springer
- external identifiers
-
- wos:000373219600003
- pmid:27006876
- scopus:84963569152
- ISSN
- 2193-1801
- DOI
- 10.1186/s40064-016-1922-1
- project
- Modeling and improving Spatial Data Infrastructure (SDI)
- language
- English
- LU publication?
- yes
- id
- cf3b2cf7-7c8e-481b-8cf7-16011aeb6471
- date added to LUP
- 2016-04-28 13:23:47
- date last changed
- 2024-10-04 15:13:18
@article{cf3b2cf7-7c8e-481b-8cf7-16011aeb6471, abstract = {{<p>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.</p>}}, author = {{Abdolmajidi, Ehsan and Harrie, Lars and Mansourian, Ali}}, issn = {{2193-1801}}, keywords = {{Defuzzification; Fuzzy logic; Inference method; Spatial data infrastructure; System dynamics technique}}, language = {{eng}}, month = {{12}}, number = {{1}}, publisher = {{Springer}}, series = {{SpringerPlus}}, title = {{The stock-flow model of spatial data infrastructure development refined by fuzzy logic}}, url = {{https://lup.lub.lu.se/search/files/8380746/published.pdf}}, doi = {{10.1186/s40064-016-1922-1}}, volume = {{5}}, year = {{2016}}, }