A Multi-Scale Fuzzy Spatial Analysis Framework for Large Data Based on IT2 FS

作者：Guo Jifa^*; Mao Jian; Cui Tiejun; Li Chongwei

来源：International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 2015, 23(1): 73-104.

DOI：10.1142/S021848851550004X

• High-order vagueness
• interval type-II fuzzy set
• scale aggregation
• scale polymorphism
• spatial relationship

The geographical world is an intricate system that comprises the interaction of the Earth's atmosphere, hydrosphere, biosphere, lithosphere, and pedosphere. Existing technologies and systems can only store, represent, and analyze crisp or type-I fuzzy spatial data and obtain spatial knowledge on several discrete scales. However, these technologies are limited to multi-scale and high-order vagueness spatial data representation and analysis, particularly regarding the representation and acquisition of multi-scale knowledge. In this paper, the uncertainty in geographic information systems (GISs) and existing problems in classical spatial analysis methods are summarized. Innovative concepts, such as the scale aggregation model and scale polymorphism, are proposed. A multi-scale fuzzy spatial analysis framework based on an interval type-II fuzzy set is introduced, and critical points are highlighted, such as an interval type-II fuzzy geographical object model (the boundary model and metric methods for geometric properties), direction relations, topological relations, and overlap methods. An actual case based on a multi-scale regional debris-flow hazard assessment is used to confirm the validity of the theory proposed in this paper.

• 出版日期2015-2
• 单位天津师范大学