Is the Trier Social Stress Test used similarly across studies: A systematic review of the methodology

This article gives a critical review of the development and applications of fuzzy set and fuzzy logic approach to geographical analysis, in general, and geographical information, in particular. Because of the intrinsic fuzziness, it is essential to develop a formal methodology for the analysis of spatial structures and processes. Within the fuzzy set framework, fundamental concepts such as distance, direction, and connection can be characterized by our natural language with formal representations that respect intrinsic imprecision. Concept of a region, fuzziness of boundaries, and graduation of phenomena over space can be appropriately captured and analyzed. Spatial economic behavior is also governed by a fuzzy preference-indifference structure on which decisions are made. Due to imprecision and to accommodate for flexibility, objectives and constraints may be fuzzily specified and the planning process can be treated as a fuzzy optimization problem. Since binary logic–based geographical information system (GIS) is not adequate to handle information, topological relationships, and operations involving fuzziness, fuzzy logic is a general method to construct GIS on which spatial reasoning and decisions are made. Fuzzy set and fuzzy logic, together with other paradigms such as probability and rough set, will provide a powerful and flexible framework for uncertainty analysis, particularly in the big data era.