(4-2) Synthesize evaluation of a
living environment and the proposal for a joint tunnel
This is a try to evaluate a living
environment synthesized using the discrimination method with the theory of
quantification II which is multi valuable factor analysis.
Classes of the outsider were ggoodh, gneutralh
or gbadh. The scores were obtained for categories of factors which affect the
synthesized evaluation of a living environment with the theory of
quantification II. The dividing points for discrimination were obtained on the
mini-max method. They were used to predict a class of the outsider with a total
Here it is omitted to tell every detail of
the social survey, but we distributed questionnaires with the effort to find
places which result in even distribution on each category for each factor. As a
result, the period of survey was for 11 years to have 540 answers.
Table 1 Category scores
for combined rating of a living environment
At the questionnaires we got 23 factors for
a living environment but most
of present residential areas in Japan are in city types and the internal
correlation between each factor in twenty-three was high. They were reduced to
eight factors with the least internal correlation to be applicable for the
condition of the theory. Namely, each factor must be independent.
The obtained result on the theory is given
in Table 1. Categories
of a factor were collected to have less correlation ratio in Table 1.
How well the outsider is explained with the
factors used is given by a correlation ratio.
It has the dimension of variance and its
square root is obtained for the dimension of a correlation coefficient. It was
How deeply each factor is correlated to the
outsider is given by a partial correlation coefficient. Each factorfs partial
correlation coefficient is shown in Fig.1.
Fig.1 Partial correlation coefficient of each
When the outsider has three classes ggoodh, gneutralh
or gbadh, it needs points where it should be distinguished on the total score.
They should be given to have the highest hit provability. Here the mini max
method was applied when the provability to fall on each class is unknown. Each
area of distribution was divided to be equal between two neighboring dividing
points there the frequency distribution was imagined to have the normal distribution.
Thus obtained dividing points are shown in
Fig. 2, the one between "bad" and "neutralh was Z1=-0.38
and the one between gneutralh and ggoodh was Z2=1.03. The
provability calculated by the area was 68% and the one obtained from the
original data was 69%.
The normal distribution of each class and the
frequency distribution of the original data are shown with the distribution
Fig.2 Dividing points for
combined rating for a living environment
If this method is applied to the
experimental house at Kaiwaka,
Convenience for shopping-bad;-0.251
Sunny in winter-very good; 0.303
Light in the night-bad; -0.113
Safety for commute and school-good;
Greens around-very good; 0.216
Public moral around-good; 0.382
Safety for childrenfs play-neutral;
, and the total score is 0.733. It is referred to the dividing points in
Figure 2 and it is predicted gneutralh. It is very close to ggoodh. For
instance, if the convenience for shopping is improved, the living circumstances
are estimated as ggoodh. The result coincides to my daily impression.
environment along a high way with high noise level
We got an opportunity to survey living environments exposed with
high noise level along the high way in Osaka (from the 8th July to
the 3rd August in 1983). The median value of the noise level was 65 dB(A)
during the night. About 500 answers were sent back with 90% of collection.
Fig.3 shows their responses to their living environments.
From the total score of each answer, a class of the outsider was
predicted by the method given in the previous section and is shown in the left.
The real responses are given in the square. For instance, 84 were predicted to
be good but responded answers got 50 for ggoodh and 34 for gneutralh
The prediction coincided with 65%. The hitting provability given in
the previous section was 68% and this method shows practically usable.
Fig.3 Evaluation of their living environments in the left and
their responded results in the square.
the improvement of the living environments in this area
If noise environment is very bad, it takes -0.578 from the score in
Fig.1and it needs 0.715(=0.137+0.578) to improve the noise environment to
When a factor of Shopping convenience, Night lighting, or Greens has
neutral, either of them can not improve the high noise environment by itself
even it gets very good environment.
The importance of the noise control at the source is clearer as it
has been suggested after it is compared with other living environments.
When noise environment is improved
to neutral and the gained score is added to the answered results, synthesized
evaluation along the high way changed to higher classes. How many of them were
changed is shown in Fig.4.
Fig.4 How many have changes to higher
classes after the noise environment is changed to neutral.
Every class gets higher one by the
improvement of noise environment. Even other living environments are kept same,
27% of people get ggoodh and 83% of people get gneutralh
It shows that they need really the
improvement of the noise environment.
of a joint tunnel
A joint tunnel can have electrical cables, civic gas pipes,
telephone lines, information lines of optic fiber, sewage system, water lines,
waste conveyance and so on. Suppose a highway is berried underground with a
joint tunnel having the green belt on top.
The area along the high way gets 0.715(=0.137+0.578) from the noise environment
improvement and 0.929(=0.708+0.212) from greens and is expected to get 1.644 in
If they are in bad living environment to have |0.800, they get 0.844 in the total score
having the joint tunnel. If the score is referred to Fig.2, the living
environment becomes better to gneutralh besides it is very close to ggoodh.
They have a certain level of satisfaction on other factors in such a
city type area. It is natural that they get higher evaluation by the
improvement of noise and greens environments.
Thus, I propose the introduction of the