Empirical Analysis on Urban Retail Business Spatial Distribution Influence Factors

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Empirical Analysis on Urban Retail Business Spatial Distribution
Influence Factors
ZHOU Chunhua, CHEN Jiandong
School of Economics, University of Jinan, Shandong, 250022
Abstract: The development of urban retail business is the most active factor to promote the
development of the city. Because of the backward of China urban retail business spatial distribution and
lack of theory research and methods exploration, the layoff and plan of retail business is comparatively
difficult. This paper classifies 35 capital cities (vice-provincial cities) according to the development
degree of urban retail business and find out the key factors that affect the retail business spatial
distribution by using factor analysis, regression analysis and cluster analysis. Hope to provide some
useful advice for the whole development of urban retail business spatial distribution.
Keywords: retail business, spatial distribution, multivariate statistical
1 Introduction
Retail business is one of the fastest growing and the highest degree of market opening industry in China.
As the important space of retail business, city has the close relationship with it. Scholars abroad start to
study the retail business spatial distribution earlier. In 1920s R.E.Park, L.Wirth and E.W.Burgess from
Chicago University USA, did lots of research of urban residential area, industrial area and central
business area's formation and change and created concentric ring model. In 1960s, with the development
of economy and technology, economists study the spatial distribution of retail business from a more
rational point of view. D.L.Huff proposed his retail gravitation model based on probability in 1963.At
the end of the 20th century, Badcock developed the commercial location theory by further studying the
central place theory and other traditional business location theories. The studies of domestic scholars
mainly focus on the scale and level distribution of retail network. Professor Ning Yuemin (1984) first
created a set of indicators to define the business center and analyzed the factors that influence Shanghai
business center and give some practical suggestions. Xue ling and Yang Kaizhong made the quantitative
estimation of population potential and business attractiveness in Haidian District to study the urban
business activity spatial framework by using the spatial interaction theory and model. lai
Zhibin(2009)analyzed population, economy and market competition, the three main factors that affect
the location of retail business network and proposed a retail business network location model based on
GIS and illustrated its practice procedures. Based on the studies before, this paper lists more factors that
could influence the spatial distribution of urban retail business and give a much more deeply analysis by
collecting more data, using factor analysis, regression model and cluster analysis.
2 Selecting Variables and Data Resources
In this paper, 15 factors that may influence the development of urban retail business are chosen. They
are : x1 geographical area of the city construction unit square kilometers , x2 urban population
（
， ：
） （
，
unit：million）, x （population density，unit：people / km）, x （disposable income of urban residents
per capita，unit：Yuan）, x （consumption expenditure of urban residents per capita，unit：Yuan）, x
（urban GDP per capita，unit：Yuan）, x （urban fixed asset investment，unit：million）, x（urbanization，
unit：%）, x （commercial business premises selling prices，unit：Yuan / square meter）, x （the number
of wholesale and retail enterprises limit above (legal representativenumber)，unit ：number）, x
3
4
5
6
7
8
9
10
11
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（year-end area of urban road，unit: square meters）, x （urban road area per capita，unit: square
meters）, x （number of public vehicles，unit: vehicles）, x （ year-end number of taxi，unit:
vehicles）, x （annual public transportation (electric) car passenger volume，unit: million）.Data of this
12
13
14
15
paper is form City Statistical Yearbook2009,Urban Life and Price Yearbook of China 2009,China City
Statistical Yearbook 2009,Statistical Yearbook of China Real Estate 2009.
3 Multivariate Statistical Analysis on Factors Affecting Urban Retail Business
3.1 Factor Analysis
According to the data that influence urban retail business test each variables with KMO and Bartlett's
Test of Sphericity. The test results are as follows:
KMO and Bartlett's Test of Sphericity
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.722
Bartlett's Test of Sphericity
Approx. Chi-Square
523.277
df
105
Sig.
.000
According to the result above, KMO is 0.722, P is close to 0, so it is suitable to conduct factor analysis.
Use SPSS to analyze, the results are as follows:
Factor loading matrix
Factor score coefficient matrix
Component
1
2
3
Component
4
1
2
3
4
x1
0.847
-0.394
0.069
-0.11
x1
0.046
0.152
-0.026
-0.176
x2
0.735
-0.419
-0.146
0.072
x2
0.146
0.014
-0.174
-0.053
0.615
x3
0.017
-0.024
-0.257
0.739
-0.066
x4
-0.11
0.326
0.032
-0.008
x3
x4
0.092
0.816
0.58
-0.144
0.231
0.486
x5
0.789
-0.205
0.499
-0.114
x5
-0.125
0.343
0.047
-0.064
x6
0.635
-0.035
0.681
-0.007
x6
-0.193
0.39
0.024
0.103
x7
0.884
-0.279
0.029
0.074
x7
0.094
0.097
-0.124
0.013
x8
0.578
0.662
0.128
0.073
x8
0.013
0.026
0.172
0.253
x9
0.425
0.547
-0.129
-0.578
x9
-0.012
-0.012
0.601
-0.411
x10
0.904
0.051
-0.272
-0.023
x10
0.188
-0.073
0.045
-0.045
0.153
x11
0.136
-0.058
0.018
0.217
-0.214
x12
-0.115
0.037
0.423
0.034
x11
x12
0.756
0.009
0.367
0.821
-0.104
0.186
x13
0.905
0.147
-0.323
0.075
x13
0.223
-0.123
0.003
0.059
x14
0.881
-0.105
-0.335
0.093
x14
0.231
-0.106
-0.092
0.012
x15
0.864
0.173
-0.383
0.031
x15
0.234
-0.152
0.041
0.015
F1 = 0.046x1 + 0.146x2 + 0.017 x3 − 0.110 x4 − 0.125 x5 − 0.193 x6 + 0.094 x7 + 0.013x8 − 0.012 x9
+0.188x10 + 0.136 x11 − 0.115 x12 + 0.223x13 + 0.231x14 + 0.234 x15
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F2 = 0.152x1 + 0.014 x2 − 0.024x3 + 0.326x4 + 0.343x5 + 0.390x6 + 0.097 x7 + 0.026x8 -0.012x9
-0.073x10 − 0.058x11 + 0.037x12 -0.123x13 − 0.106 x14 -0.152x15
F3 = −0.026x1 − 0.174 x2 − 0.257 x3 + 0.032x4 + 0.047x5 + 0.024x6 − 0.124 x7 + 0.172x8 + 0.601x9
+0.045 x10 + 0.018 x11 + 0.423x12 + 0.003x13 − 0.092 x14 + 0.041x15
F4 = − 0.176 x1 − 0.053 x 2 + 0.739 x3 − 0.008 x4 − 0.064 x5 + 0.103 x6 + 0.013 x7 + 0.253 x8 − 0.411 x9
− 0.045 x10 + 0.217 x11 + 0.034 x12 + 0.059 x13 + 0.012 x14 + 0.015 x15
According to the extracted factors, considering retail sales of the corresponding factor on the dependent
variable of the situation, Four factors are used as independent variables in the regression analysis.
3.2 Regression Analysis of Factors
According to the analysis of SPSS, F1, F2, F3, F4 get their scores respectively. Use F1, F2, F3, F4 as the
independent variable, y as the dependent variable to construct the regression equation. Use Eview to do
regression analysis on main components. F3 is insignificant so can be deleted and reconstruct the
regression equation. The result is:
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
29250482
2790304.
10.48290
0.0000
F1
34709270
2838005.
12.23016
0.0000
F2
14160610
2838004.
4.989637
0.0000
F4
5786043.
2838005.
2.038771
0.0518
According to the second regression result, F4is insignificant at the significant level 0.05, but the
difference is very small, it can be considered significant and can pass Parameter estimation by
significant test. form the overall results from the model fitting effect, modified R2reaches 0.858, which
shows that the whole fitting effect is very good. Serial correlation LM test and Heteroscedasticity White
test. The result shows that Serial correlation and Heteroscedasticity don't exist.
LM test results
F-statistic
0.954618
Probability
0.399098
Obs*R-squared
2.210682
Probability
0.331098
White test results
F-statistic
1.823232
Probability
0.126218
Obs*R-squared
13.52060
Probability
0.140430
After a principal component regression, the final regression equation is
Y = 29250482 + 34709270 F1 + 14160610 F2 + 5786043 F4
Put F1, F2, F4into the above equation, The corresponding impact of the variable x on Y can be got. It
can be simplified as follows:
Y=24109014+33261349.77 x1 +50767837.74 x2 -1432237.695 x3 +47820405.16 x4 +49143382.86 x5 +3
9332316.73 x6 +45121298.63 x7 +40019967.16 x8 +34345280.46 x9 +43954376.92 x10 +51062615.04
x11 +24685549.14 x12 +49376340.48 x13 +40341597.29 x14 +47519914.87 x15
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From the result we can see that urban population x2 ,disposable income of urban residents per capita
x4 , consumption expenditure of urban residents per capita x5 , urban fixed asset investment x7 ,
year-end area of urban road x11 ,number of public vehicles x13 ,annual public transportation (electric)
car passenger volume x15 , have much larger effect on Y than other factors.
3.3 Cluster Analysis
Conduct Q cluster analysis on the relative indicators of retail business of 35 capital
cities( vice-provincial cities) ( The data of Lasa is missing in the statistic year book)
From the Q cluster analysis above, 35 capital cities ( vice-provincial cities) can be classified as follows:
The first: Beijing, Shanghai;
The second: Guangzhou, Shenzhen;
The third: Chongqing;
The forth: Tianjin, Nanjing, Wuhan, Chengdu, Shenyang, Xian, Jinan, Qingdao, Dalian, Hangzhou,
Fuzhou, Xiamen, Ningbo. According to Pedigree Chart. It can be classified in detail: Wuhan, Chengdu,
Shenyang, Xian; Jinan, Qingdao, Dalian; Fuzhou, Xiamen; four cities left.
The fifth: the rest 17 cities.
From the classification of Pedigree Chart, it is very clear that on the condition that given the number of
wholesale and retail enterprises above city limits and the whole sales of them, disposable income of
urban residents per capita,consumption expenditure of urban residents per capita, urban fixed asset
investment,year-end area of urban road,number of public vehicles,annual public transportation (electric)
car passenger volume are all comparatively lower than expected, which is the main restrictive factors of
urban retail business.
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4 Conclusion
The factors that can influence urban retail business spatial distribution are very complicated and some
cannot be measured. Besides all the factors above, the local government and planning department have
large influence on it too. With the development of the diversity trend of residents consumption mode,
psychological factors are the field that needs more attention to discuss. From the factors we study above,
we can improve rational planning and distribution, accelerate urban economy development and
enlarge the circulation infrastructure construction to promote the development of urban retail business.
（
）
Author in brief:
Zhou Chunhua 1977- female, lecturer, mainly engaged in regional economy.
Address: 106 Jiwei road school of economics of University of Jinan, Jinan, Shandong
Post code: 250022
Telephone: 18660158256
E-mail: [email protected]
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