Regional Economic Development in the Chinese Mode
------the central has a rapid development, at the same time ,
there has a big gap between east and west.
Xiaokang Liu
The NO.9 Dormitory of Student, School of Science, Beijing University of Posts and
Telecommunica-tions, 10 Xitucheng Road, Haidian District, Beijing, P.R.CHINA
Abstract--The integrated development of central city is an important driving force for the boo-ming economy of a
region. In paper, I study the level of development of the 35 central cit-ies and the surrounding areas in China. Wit
h the factor analysis model, I selecting 12 signific-ance indicators and using the SPSS 13.0 to make a rank for the
35 central cities’ develop-ment standard. Besides, making an analysis and giving some suggestions base on Chinese
actual economic policies and regional realities.
Keyword------the indicators of assessment , the integrated development of center cities, factor analysis
1 The indicators of assessment
In paper, I select 12 indicators. They are :
Eight indicators of social economy
X1---The city's annual average population (ten
thousand people);
X2---The city's total industrial output value (te
n thousand yuan );
X3---Total freight(Ten thousand tons);
X4---Wholesale and retail Accommodation and
Catering Industry Employed Persons (ten
thousand people);
X5---Local financial Budget revenue ( te
n thousand yuan);
X6---Urban and rural residents years The end
of the savings balance ( ten thousand yuan);
X7---The end of the unit Number of employee
s (ten thousand people);
X8---- Workers in the post Total wages (ten
thousand yuan);
Fourindicators of urbanpublic facilities .
X9---- Residential land area (square kilometer
s);
X10---Per million people have bus (vehicle);
X11--- Per capita urban Road area (Square
meters);
X12---- Green space per capita Area (square
meters).
2 Assessment of method
Using factor analysis method to analyze the lev
el of development of the central cities in paper.
Factor Analysis method is a multivariate sta
tistic-al method. The main idea of the factor an
alysis is that researching the internal depende
ncies rel-ation of the raw data’s correlation m
atrix and reducing the dimension of variables.
In addition, It can transform some intricate rel
ationship vari-ables to a few factors that contain
the most infor-mation.
3 The process of analysis
3.1 Standardization of data in order to eliminat
e
the influence of the dimensionless. Using the
Standard deviation of standardized method.
The formula :
Yi=XiെX
S, X
ഥ=1
NσXi
N
1,
S=ට1
Nെ1൫XiെX
ഥ൯2
2
Yi-----Indicators normalized values ,
Xi-----Indicators of the initial value ,
X
ഥ ----Indicators of the initial average ,
S-----Indicators of the initial standard deviation,
N--Number of samples.
Open Journal of Applied Sciences
Supplement：2012 world Congress on Engineering and Technology
Cop
y
ri
g
ht © 2012 SciRes.223

The following is Standardization of data :
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EHLMLQJ        
WLDQMLQ       
VKLMLD]
K
         
WDL\XDQ          
KXKHKDR          
VKHQ\DQ
J
         
FKDQJFKX
Q
         
KDHUELQ        
VKDQJKDL        
QDQMLQJ        
KDQJ]KRX        
KHIHL          
IX]KRX         
QDQFKDQ
J
           
MLQDQ        
]KHQ]KR
X
           
ZXKDQ       
FKDQJV
K
       
JXDQJ]K
R
      
QDQQLQJ          
KDLNRX         
FKHQJG
X
        
JXL\DQJ           
NXQPLQ
J
       
[LDQ        
ODQ]KRX         
[LQLQJ         
\LQFKXD
Q
          
ZXOXPX
T
          
GDOLDQ      
QLQJER     
[LDPHQ       
TLQJGDR     
VKHQ]KH
Q
          
Direction: Because the absence of few Reside
-ntial land area’s row data, there have no anal
y-sis about Shanghai and Guangzhou in the ra
nk about 35 cities.
3.2
Calculating the operating results. Importing th
e standardization of data into SPSS software
and selecting “Analyze—Data--Factor”. Analysi
s the data with principal components. Accordi
ng to the principle of Eigen values greater th
an one, electing three common factors. the cu
mulative variance contribution rate is 90.56
3%.
Factor analysis
Now, the practical significance of the non-rotate
d
common factor is hard to explain. so making a
rotation for common factor with varimaxˈth
e SPSS output as follow:
Total Variance Explained
6.74456.19856.198 6.74456.19856.198
2.75522.96079.158 2.75522.96079.158
1.36911.40490.563 1.36911.40490.563
.396 3.303 93.866
.258 2.148 96.014
.158 1.314 97.328
.137 1.144 98.472
.117.971 99.443
.041.342 99.785
.017 .145 99.930
.006.050 99.980
.002.020 100.000
Component
1
2
3
4
5
6
7
8
9
10
11
12
To t al% of VarianceCumulative %To t al% of VarianceCumulative %
Initial EigenvaluesExtraction Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Component Matrix
a
.531 -.525 .584
.801 .294 .226
.551 -.271 .753
.895 -.133 -.398
.978 -.030 -.104
.961 -.054 -.112
.963 -.116 -.204
.932 -.073 -.333
.900 -.114 .089
.332 .871 .045
.130 .867 .221
.314 .870 .131
X1
X2
X3
X4
X5
X6
X7
X8
X9
X1 0
X1 1
X1 2
1 2 3
Componen t
Extraction Method: Principal Component Analysi
s
3 components extracted.
a.
IJijIJIJIJıĺĹĸķĶĵĴijIJ
ń Ű Ů ű Ű ů Ŧů ŵġŏ Ŷ Ů ţ Ŧų
ĸ
ķ
Ķ
ĵ
Ĵ
ij
IJ
ı
Eigenvalue
Scree Plot
Rotated Component Matrix
a
.288 -.243 .870
.593 .510 .409
.208 .040 .948
.988 -.017 .027
.927 .167 .285
.918 .138 .278
.965 .059 .217
.987 .062 .080
.785 .114 .451
.184.910 -.0 9 9
-.071 .901 -.019
.132.924 -.0 3 1
X1
X2
X3
X4
X5
X6
X7
X8
X9
X1 0
X1 1
X1 2
123
Component
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 4 iterations.
a.
224 Cop
y
ri
g
ht © 2012 SciRes.

According to the result above ,
X1=.288*F1-.243*F2+.870*F3 ,
X2=.593*F1+.510*F2+.409*F3 ,
X3=.208*F1+.040*F2+.948*F3
X4=.988*F1-.017*F2+.027*F3 ,
X5=.927*F1+.167*F2+.285*F3 ,
X6=.918*F1+.138*F2+.278*F3
X7=.965*F1+.059*F2+.217*F3 ,
X8=.987*F1+.062*F2+.080*F3 ,
X9=.785*F1+.114*F2+.451*F3
X10=.184*F1+.910*F2-.099*F3 ,
X11=-.071*F1+.901*F2-.019*F3 ,
X12=.132*F1+.924*F2-.031*F3
In order to get the conclusion, making a rank
for Xi (i=1,2 ,3 ,4,5,6,7,8,9,10,11,12).
To calculate the Integrated score ( F ) by
F=(47.800*F1+24.007*F2+18.756*F3)/90.563
In the other hand, the table about F1, F2, F3,
F. as follow(Table one):
Table
one:
&LW\) ) ) )
%HLMLQJ 
7LDQMLQ
6KLMLD]KXD
Q
 
7DL\XDQ
+RKKRW 
6KHQ\DQJ  
&KDQJFKXQ   
+DUELQ 
6KDQJKDL 
1DQMLQJ
+DQJ]KRX  
+HIHL  
)X]KRX 
1DQFKDQJ  
-LQDQ  
=KHQ]KRX  
:XKDQ
&KDQJVKD  
*XDQJ]KRX 
1DQQLQJ
+DLNRX  
&KHQJGX
*XL\DQJ 
.XQPLQJ 
;LDQ
/DQ]KRX   
;LQLQJ 
<LQFKXDQ 
8UXPTL 
'DOLDQ
1LQJER
;LDPHQ  
4LQJGDR 
6KHQ]KHQ
&KRQJTLQJ  
F1 factor score for the x-axis, F2 factor score for
the y-axis, drawing the city factor score plot.
4 Analysis the result from the experiment
with the Chineseactualeconomic policies
and regional realities.
Total Variance Explained
6.74456.19856.1985.73647.800 47.800
2.75522.96079.1582.88124.007 71.807
1.36911.40490.5632.25118.756 90.563
.396 3.303 93.866
.258 2.148 96.014
.158 1.314 97.328
.137 1.144 98.472
.117.971 99.443
.041.342 99.785
.017.145 99.930
.006 .050 99.980
.002.020 100.000
Component
1
2
3
4
5
6
7
8
9
10
11
12
To tal% of Variance
Cumulative
%To tal% of Variance
Cumulativ
e %
Initial EigenvaluesRotation Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Explain the mean of Xi
Common factor F1 has a large proportion of
value on X4, X5, X6, X7, X8,X9 .
X1,X7 andX8 are the indicators to reflect the siz
e of the cities;
X2,X3, the index reflecting the urban industrialize
- tion;
X4 is stand for the scale of development of
the tertiary industry in cities;
Rotated Component Matrix
a
.988 -.017 .027
.987 .062 .080
.965 .059 .217
.927 .167 .285
.918 .138 .278
.785 .114 .451
.593 .510 .409
.132.924 -.031
.184.910 -.099
-.071 .901 -.01 9
.208 .040 .948
.288 -.243 .870
X4
X8
X7
X5
X6
X9
X2
X1 2
X1 0
X1 1
X3
X1
1 2 3
Component
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 4 iterations.
a.
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REGR factor score 2 for analysis 1
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X5 is the income of Government as the manage
r of the state and the owner of state-owned ass
ets . It reflect the income level of residents to
a certain extent, In the current Distribution Po
licy,
Government and residents are the major to obt
a-in the distributable income . So X5, X6, sho
w the city's national income level;
F1 is the common factor to reflect the size of
city and living conditions of urban residents.
Higher the score, better the living condition
of urban residents, greater the size of the citie
s.
Common factor F2 has a large proportion of
value on X10, X11, X12. It reflects the leve
l of urban infrastructure, the score of this facto
r reflects the level of a city's infrastructure;
Common factor F3 only has a large load on the
X1, X2, X3. It is a common factor to reflect the
level of economic development.
Table one show that more score of cities on
the urban scale factor F1 is Beijing , Tianjin
,Hangzhou, Shenzhen. Among those cities the
score of Beijing is 5.08946 much higher t
han other cities.It means that the size of those
cities are big enough and living conditions o
f urban residents are very good . The size of
Yinchuan , Hohhot, Haikou, Xining are small
er and the living conditions of urban residents
are worse .
More score on the F2 are Shenzhen ,Qingdao,
Nanjing, Xiamen,the lower score are Chongqi
ng ,Harbin, Guiyang, Zhenzhou .The score of
F2 indicate the level of a city's infrastructure; th
e more score ,the higher level. So there hav
e the better infrastructure in Shenzhen, Qingda
o, Nanjing ,Xiamen. However, the infrastructur
e of Chongqing is bad and government shoul
d to devote more funds to improve. Chongqing,
Chengdu ,Tianjin ,Wuhan ,Qingdao have goo
d score on F3. F3 is the common factor stan
d for the level of economic development Xi
ning, Beijing, Haikou, Xiamen are worse.
In recent years, Chinese government increase
d the investment in economic development of
the central region of china. undoubtedly,The la
rge investment contributed to the rapid develo
pment
of the central region . Chongqing, Chengdu, Wu
han have experienced rapid development depen
d on the large investment. Beijing’s score is l
ow on the F3, the reason is that Beijing i
s not only the capital of China but the
China’s political and cultural center. Its special
status inhibited the rapid development in
economic terms. Using formula
(F=(47.800*F1+24.007*F2+18.756*F3)/90.563)
to calculate F. The rank of higher score citie
s are Beijing ,Shenzhen ,Chongqing ,Tianjin ,
Hangzhou ,Nanjing .and the low score cities
are Xining ,Haikou ,Lanzhou, Hohhot.
5 Conclusion
On the size , the historic city bigger than th
e new city; on the level of urban facilities, south
ern of China is better than the North, the new
cities are better than the old cities; on the level
of urban development ,the eastern region
of China
is higher than the western region. On the othe
r hand, distributing near the origin in
two-dimensional coordinate system are cities
that the urban factor score is less than zero.
Not only their low development level but simi
lar mode. They mostly locate in the northwe
st of China . lastly , The central region of
China has a rapidly development in the past
years.
6 Acknowledgement
I would like to express my thanks to my Stat
i-stics teacher Dr Li and the professor Tian w
ho give me much help with the English writin
g methods. in additional , I want to thank the
Li-brarian who give me favor during search f
or data. It would not have been possible wit
hout their assistance. Thank you so much!
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