# Empirical Analysis on Urban Retail Business Spatial Distribution Influence Factors

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2 (year-end area of urban road,unit: square meters), x(urban 12 road area per capita,unit: square meters), x(number 13 of public vehicles,unit: vehicles), x(year-end 14 number of taxi,unit: vehicles), x(annual 15 public transportation (electric) car passenger volume,unit: million).data of this 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 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 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 Component Factor score coefficient matrix Component x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x F = 0.046x x x 0.110x 0.125x 0.193x x x 0.012x x x 0.115x x x x 293

3 F = 0.152x x 0.024x x x x x x x x 0.058x x x x x F = 0.026x 0.174x 0.257x x x x 0.124x x x x x x x 0.092x x F = 0.176x 0.053x x 0.008x 0.064x x x x 0.411x x x x x x x 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 F F F 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 R 2 reaches 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 Probability Obs*R-squared Probability White test results F-statistic Probability Obs*R-squared Probability After a principal component regression, the final regression equation is Y = F F F 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= x x x x x x x x x x x x x x x

4 From the result we can see that urban population x 2,disposable income of urban residents per capita x 4, consumption expenditure of urban residents per capita x 5, urban fixed asset investment x 7, year-end area of urban road x 11,number of public vehicles x 13,annual public transportation (electric) car passenger volume x 15, 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. 295

5 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: Telephone: References [1]. Yang Ying. the process of western commercial space theory since 1920s. tropical geography, 2000, (20):11 (in Chinese) [2]. Chen Xiangping. the development of urban commercial location theory abroad. Shanghai University of Technology Journal, 2003, 25, (1): 14 (in Chinese) [3]. Ning Yuemin. Study on Shanghai Central Location. Geography Journal, 1984, (2): (in Chinese) [4]. Yang Wuyang. The past, present and future of Beijing retail business network. Geography Journal, 1994, (1): (in Chinese) [5]. Xue Ling, Yang Kaizhong. Business Distribution Based on Space Interaction Model. Geography Study, 2005, (2): (in Chinese) [6]. Lai Zhibin. Retail Business Location Model Based on GIS. Geography Information World, 2009, (2): (in Chinese) [7]. GeJintian. Aggregation and differentiationof Urban Retail Business. Dongyue Forum, 2009, (6): (in Chinese) 296

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