1). History / research of airport / aerotropolis development best practices – relationship / correlation to urban spatial development / distribution & current / anticipated land use around similar / comparable airports;
2). Study positive and negative airport / aerotropolis community / economic causes & effects (i.e. socio-demographics, land-use, etc.); 3). Long - term sustainable development(s) / environmental impact(s);
4). Development of best case scenario conceptual development, planning & policy recommendations to assist and manage long-term sustainable aerotropolis development / land-use around DFW & DAL;
5). Creation of assessment guide (criteria) to aid in …show more content…
The method can also be used as a judgment, decision-aiding or forecasting tool (Rowe & Wright, 1999), and can be applied to program planning and administration (Delbeq, Van de Ven, & Gustafson, 1975). The Delphi method can be used when there is incomplete knowledge about a problem or phenomena (Adler & Ziglio, 1996; Delbeq et al., 1975). The method can be applied to problems that do not lend themselves to precise analytical techniques but rather could benefit from the subjective judgments of individuals on a collective basis (Adler& Ziglio, 1996) and to focus their collective human intelligence on the problem at hand (Linstone & Turloff, 1975). Also, the Delphi is used to investigate what does not yet exist (Czinkota & Ronkainen, 1997; Halal, Kull, & Leffmann, 1997; Skulmoski & Hartman 2002).
While the Delphi is typically used as a quantitative technique (Rowe & Wright, 1999), a researcher can use qualitative techniques with the Delphi method. The Delphi method is well suited to rigorously capture qualitative data. It may be seen as a structured process within which one uses qualitative, quantitative or mixed research methods (Wang, 2013). Using this methodology under the appropriate conditions will give reliable and professional guidance to many of the questions of this …show more content…
A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership function that assigns to each object a grade of membership ranging from zero to one. Fuzzy sets generalize classical sets, because the indicator functions of classical sets are special cases of the membership functions of the fuzzy sets, if the latter only has values of 0 or 1. In fuzzy set theory, classical bivalent sets are commonly called crisp sets. Triangle fuzzy number Ā consists of three parameters (a1, a2, a3) shown in Table 2 and the membership function of Ā can be donated as ua(x) as seen in equation