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Developing a data model for understanding geographical analysis models with consideration of their evolution and application processes
Author(s) -
Chen Min,
Yang Chen,
Hou Tao,
Lü Guonian,
Wen Yongning,
Yue Songshan
Publication year - 2018
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12484
Subject(s) - computer science , data science , abstraction , metadata , bridge (graph theory) , data mining , management science , engineering , world wide web , medicine , philosophy , epistemology
Geographical analysis models are widely employed to mirror real phenomena and processes on Earth. The current geographical analysis models can provide prediction and decision support‐oriented information in various domains through analysis and simulation results. However, the complexity of models is increasing due to their continuous development and related research, and the relationships between models are becoming increasingly complicated, which severely hinders the ability to select and use suitable models. To bridge the requirements of model understanding with related abundant information, a data model for geographical analysis models is designed with consideration of their evolution and application processes. In addition to basic metadata (e.g., name, classification, and modeling approach), evolution and application information, which is often neglected in traditional model expression methods, can provide clues about model development histories and usage relationships. Thus, this information will provide scientists with a comprehensive understanding and will form an overall picture of geographic models that can be used for future research. Based on the analysis of the elements related to the evolution and application information, the data model is designed and an information abstraction strategy is proposed. The Soil and Water Assessment Tool (SWAT) is employed as a case study to show the capacity of the designed data model to contribute to both sharing of geographical analysis model knowledge and further model analysis.