Civil Engineering Reference
In-Depth Information
1
Introduction
Leisure city is largely defined as travel to visit friends or relatives, for outside
recreation and for entertainment and other relaxing personal or group activities
(
LaMondia and Bhat 2012
). In fact, leisure travel has become human way of
life, with many travelers routinely making both daily short-distance leisure trips
and long-distance vacation trips (
Jun et al. 2012
). The city leisure index (CLI),
comprehensive estimates of the recreational functions of cities, and the development
of a quantitative description of the city recreation system.
Estimating factors of leisure city, specialized culture, and nature condition are
complex. It involves vary elements, such as traffic, people, culture, effort, and
objective conditions. The value of LC is difficult to evaluate by traditional statistics,
such as
Wu and Hsu
(
2004a
,
b
) identified the model construction through qualitative
simulation,
Chen and Wang
(
1999
) proposed fuzzy statistical testing method to
discuss the stability of Taiwan short-term money demand function,
Wu and Sun
(
2001
) demonstrated the concepts of fuzzy statistic and applied it to social survey,
and
Wu and Tseng
(
2002
) used fuzzy regression method of coefficient estimation
to analyze Taiwan monitoring index of economic. For an extensive treatment of the
theory of fuzzy statistics the interested reader may refer to see
Nguyen and Wu
(
2006
). In addition,
Chen and Niou
(
2011
);
Ye h
(
2011
) fuzzy relative weights of the
analysis of fuzzy numbers, these studies are to obtain good results. How to evaluate
the index of leisure from the perspective of human needs, leisure city is to meet the
leisure needs as the core. Form the perspective of urban economic, leisure and city
factors of production (labor, capital, land, ect.). nevertheless, the leisure industry,
and the tertiary industry, the main industries in the urban economy which accounts
for the absolute proportion of the city.
2
How to Evaluate the Index of Leisure
Soft computing in the fuzzy evaluation
, it is appropriate to apply the membership
function, a more precise mathematical techniques, in analyzing the fuzzy infor-
mation. The value of the membership function, between 0 and 1, is derived from
the characteristic function, to express the membership grade of each element in a
set. Though subjectivity coming from human thought is often involved, what fuzzy
theories handles is not semantic uncertainty but to compute the degree of objectivity
for the semantic uncertainty. That is, we will give the value of intelligent capital
into different linguistic terms, such as
valueless, not too valuable, lightly valuable,
valuable, very valuable, extremely valuable, hugely valuable, and invaluable.
Each
term will be correspondent with a real value, which will be determined by the
sampling survey and fuzzy statistical analysis. In social science research, statistical
analysis is indispensable, especially, in the aspect of survey and methodology.
Basic descriptive statistics, such as mean, median, and mode, are used very often
in social science study. When analyzing data, describing statistics can describe
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