Redesign Project
Youth Indicators, 2005
Trend in the Well-being of American Youth
By Ji Woong Oh
Beginning
At first, I was trying to=
find
some poorly represented data from financial companies and websites, but aft=
er
spending several hours, I realized that nowadays they use most up to date
programs to represent their data, so I gave up on my initial interested are=
a,
and started to google.
Education is my other are=
a of
interest (my mother is a current elementary teacher), and from the U.S.
Department of Education, I finally found some graph I can improve, or repre=
sent
in different ways.
Redesign
Source:=
http://nces.ed.gov/programs/youthindicators/Indicators.asp?PubP=
ageNumber=3D32

This graph doesn’t look too bad, compared to others I =
have
seen, but I could see I could apply some of the techniques I learned in cla=
ss,
and I decided to go on with this graph.
This graph shows the interested readers the income in the U.S.
earned by both male and female, with different age group.
It is quite clear from the graph that the differences in ear=
ning
income between male and female has been reduced significantly, especially f=
or
the younger sector of the age.
This might tell the readers that education in the U.S.
has been quite effective and equally taught without much sexual disadvantage
(as this data is from the U.S. Education Department)
The purpose of=
this
data or this graph was to show readers that inequality of earnings in sex is
continuously disappearing.
I did two redesigns, simply because I wanted to use different
techniques to show different ways to present the data. =
p>
First redesig=
n

In the first redesign, I did not mark each data points as I =
did
in the second one because I thought that the purpose of this graph was not
about actual data, but what it tells us, so I did reproduce the graph myself
with R, but I included the differences of the male and female data in the
second panel of the graph.
The second panel of the graph is somewhat interesting. This clearly shows how much more m=
en
have been making than women throughout the years. The reason I included the se=
cond
panel is to show the actual amount of difference over time.
In the original graph, it looks like there has been signific=
ant
decrease in gap between earnings in male and female, since the amount of
earnings has been increased over years.&nb=
sp;
But the second panel of the redesign graph shows us that the dollar
amount difference in 2002 is pretty much same as 1955. Of course, male made signifi=
cantly
more than female during mid 60’s to mid 80’s but I see this as =
war
effects. =
p>
In fact, the gap between male and female in age group 20 to =
24
has been increased after 1995.
Considering the fact that this numbers are all inflation
adjusted, plus the change in living standard and lifestyle, American still =
have
long way to go in terms of education in gender.
Second Redesign

In the second redesign, I just made look better and clearer =
with
using distinct color and notations on the data points. =
p>
I use circle for the male data points and triangle for femal=
e,
but used dashed line and empty figures for the data points represent age gr=
oup
20 to 24.
Also, I used manly color for male and girly color for female=
to
distinguish more clearly.
End
What I like to do more on this graph is to use interac=
tive
tool for this graph. For example, it would be nice to show actual data numb=
ers
and selective year when reader points at them. Also I like to link each data poin=
t with
extra facts about that specific year, such as the fact about what was the m=
ost
famous and highly valued jobs, or how well the economy was, or any historic
significance in that particular time of period.
Source:=
&n=
bsp;
http://nces.ed.gov/programs/youthindicators/Indicators.asp?PubP=
ageNumber=3D32
&n=
bsp;
&n=
bsp;
&n=
bsp;
&n=
bsp;
Additional data
&n=
bsp;
|
Year
|
All ages, 15 and over (M)
|
ages 20 to 24 (M)=
|
All ages, 15 and over (F)
|
ages 20 to 24 (F)=
|
|
1=
955
|
28,502
|
22,145
|
18,352
|
18,581
|
|
1=
960
|
33,032
|
23,800
|
20,032
|
19,175
|
|
1=
965
|
37,002
|
26,876
|
22,176
|
21,205
|
|
1=
970
|
43,174
|
31,154
|
25,447
|
22,878
|
|
1=
975
|
43,307
|
28,925
|
24,777
|
21,566
|
|
1=
980
|
42,574
|
26,924
|
24,883
|
20,597
|
|
1=
985
|
41,983
|
23,061
|
26,607
|
19,617
|
|
1=
990
|
39,162
|
22,029
|
27,663
|
19,724
|
|
1=
991
|
39,293
|
20,606
|
27,383
|
19,419
|
|
1=
992
|
39,337
|
20,706
|
27,683
|
19,499
|
|
1=
994
|
37,778
|
19,679
|
27,304
|
18,633
|
|
1=
995
|
37,301
|
20,110
|
27,208
|
18,458
|
|
1=
996
|
37,109
|
20,477
|
27,629
|
18,570
|
|
1=
997
|
38,138
|
20,732
|
28,451
|
18,683
|
|
1=
998
|
39,508
|
22,193
|
28,892
|
19,690
|
|
1=
999
|
39,876
|
21,941
|
28,886
|
19,888
|
|
2=
000
|
39,645
|
22,198
|
29,206
|
20,428
|
|
2=
001
|
39,655
|
21,984
|
30,154
|
20,589
|
|
2=
002
|
39,830
|
21,829
|
30,363
|
20,017
|
&n=
bsp;
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