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1824 JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER 2011 REFERENCES [1] Nancy Carter, Candida Brush, Patricia Greene, Elizabeth Gatewood and Myra Hart, “Financing High-Growth Enterprise: Is Gender an Issue?”, J. Women in Business: Access and Success, 2003: 45-52. [2] Candida G. Brush, Nancy M. Carter, Patricia G. Greene, Myra M. Hart, “The role of social capital and gender in linking financial suppliers and entrepreneurial firms: a framework for future research”, J. Venture Capital, 2002, Vol. 4, Issue 4:305 – 323. [3] Coleman S, “Access to capital and terms of credit: A comparison of men-and women owned small businesses”, J. Journal of Small Business Management, 2000, 38 (3):37-5. [4] Sara Carter, Eleanor Shaw, Wing Lam and Fiona Wilson, “Gender, Entrepreneurship and Bank Lending: The Criteria and Processes Used by Bank Loan Officers in Assessing Applications”, J. Entrepreneurship Theory and Practice, 2007, 31(3):427 – 444. [5] Barbara Orser, Sandra Hogarth-Scott, “Opting for Growth: Gender Dimensions of Choosing Enterprise Development”, Canadian Journal of Administrative Sciences, 2002, 19(3): 284 – 300. [6] Cooper, A. C, F. J. Gimeno-Gascon and C. Y. Woo, “Initial Human and Financial Capital as Predictors of New Venture Performance”, Journal of Business Venturing, 1994, 9: 371–395. [7] Loscocco, K. A., J. Robinson, R. H. Hall and J. K. Allen, “Gender and Small Business Success: An Inquiry into Women’s Relative Disadvantage”, J. Social Forces 1991, 70(1): 65–85. [8] Bosma, N., M. van Praag, R. Thurik and G. de Wit, “The Value of Human and Social Capital Investments for the TABLE 4-6 THE MODEL AFTER REMOVING SOME OF THE VARIABLES Variable Model Log Likelihood Change in -2 Log Likelihood df Sig. of the Change Step 1 Cost for social activities -171.306 11.290 1 .001 Step 2 Cost for social activities -167.047 10.341 1 .001 Level in Private Entrepreneur Association -165.660 7.569 1 .006 Step 3 Cost for social activities -163.290 10.160 1 .001 NPC Post -161.876 7.332 1 .007 Level in Private Entrepreneur Association -161.830 7.239 1 .007 Step 4 Work experience -158.210 4.916 1 .027 Cost for social activities -160.773 10.042 1 .002 NPC Post -159.101 6.697 1 .010 Level in Private Entrepreneur Association -159.101 6.698 1 .010 Step 5 Work experience -156.215 4.995 1 .025 Cost for social activities -158.475 9.515 1 .002 Industry and Commerce -155.752 4.069 1 .044 NPC Post -157.328 7.221 1 .007 Level in Private Entrepreneur Association -156.461 5.487 1 .019 © 2011 ACADEMY PUBLISHER Business Performance of Startups”, J. Small Business Economics, 2004, 23(3): 227–236. [9] Bates, T, “Entrepreneur Human Capital Inputs and Small Business Longevity”, J. The Review of Economics and Statistics, 1990, 72(4): 551–559. [10] Fabowale, L., Orser, B. and Riding, A. ,”Gender, structural factors and credit terms between Canadian small businesses and financial institutions”, J. Entrepreneurship Theory and Practice, 1995,19(4): 41 – 66. [11] Boden, R. J. and Nucci, A. R., “On the survival prospects of men’s and women’s new business ventures”, J. Journal of Business Venturing, 2000, 15(4): 347 – 362. [12] Uzzi B, “Embeddedness in the Making of Financial Capital: How Social Relations and Networks Benefit Firms Seeking Financing”, J. American Sociological Review, 1999, 64(4): 481-505. [13] Higgins M, Gulati R, “Getting off to a good start: the effects of upper echelon affiliations on underwriter prestige”, J. Organization Science, 2003, 14(3): 244–263. [14] Shane S, Stuart TE, “Organizational endowments and the performance of university startups”, J. Management Science, 2002, 48(1): 154–170. [15] Priscilla Chu, “Social Network Models of Overseas Chinese Entrepreneurship: The Experience in Hong Kong and Canada”, J. Canadian Journal of Administrative Sciences, Dec.1996; 13(4): 358-365. [16] Tjosvold, Dean, Weieker, David, “Cooperative and competitive networking by entrepreneurs: A critical incident study”, J. Journal of Small Business Management. Milwaukee: 1993, 31(1):11 一 22. [17] Zhilong Tian, Yongqiang Gao et al, “Chinese Enterprises’ Political Strategy and Behavior Study”, J. Management World, 2003, 12.
JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER 2011 1825 A Spatial Econometric Analysis of China’s Manufacturing Agglomeration based on Geoda and Matlab Huayin Yu Department of Statistics, Anhui University of Financial and Economics, Bengbu, China Email: y_hyin@163.com Weiping Gu Department of Statistics, Anhui University of Financial and Economics, Bengbu, China Abstract—Industrial agglomeration has gradually become an economic focus in recent years. Scholars has done a lot of research about the formation mechanism of industry agglomeration and its influencing factors, but the spatial correlation of variables has still been neglected. Firstly this paper gives a brief introduction about Geoda software and Matlab neural network toolbox, then use spatial statistical methods to describe the 1999-2008 China's manufacturing industry agglomeration. Secondly this paper uses spatial econometric methods to analyze the influencing factors of China’s provincial manufacturing Agglomeration. The results show that the spatial econometric model is superior to the traditional econometric models and the analysis based on spatial econometric model are more accurate. Finally, the paper also gives a brief forecast of the manufacturing Agglomeration. Index Terms—manufacturing Agglomeration, spatial correlation, spatial lag model, spatial error model, BP neural network I. INTRODUCTION Geoda is a collection of software developed by Luc Anselin. It has a friendly and graphical interface that users can easily implement exploratory spatial data analysis (ESDA) with it, such as spatial autocorrelation analysis and spatial econometric analysis. The Geoda software includes an interactive environment that combines maps with statistical graphics, using dynamic-linked-window technology. Its original version date back to the first contribution made to develop a bridge between ESRI’s ArcInfo GIS and the SpaceStat software. The second version of Geoda made an improvement to ESRI’s ArcView 3.x GIS that it can implement linked windows and brushing. In contrast to the previous versions, the current Geoda is independent software that runs under any of the Microsoft Windows operating systems without a specific GIS system. Matlab is an advanced language and interactive environment that users can implement numerical computation with it. And its operational efficiency is much higher than traditional programming languages such as C, C++, and FORTRAN due to the excellent © 2011 ACADEMY PUBLISHER doi:10.4304/jcp.6.9.1825-1831 design. Matlab can perform many complex tasks such as signal and image processing, computation, control system design, test and measurement, financial modeling and analysis. There are more than 30 Toolboxes in Matlab and they can be divided into two categories: functional toolbo x and field-based toolbox. The functional toolbox is mainly used to expand symbolic computing, modeling and simulation capabilities, word processing and hardware real-time interactivity. Functional toolbox can be used in a variety of disciplines. In the Opposite, the field-based toolbox is highly professional, such as the control system toolbox, signal processing toolbox and finance toolbox. And neural network toolbox is one of them. It extends Matlab with tools for designing, implementing, visualizing, and simulating neural networks. II. DESCRIPTIVE STATISTICS As a branch of econometrics, spatial econometrics focuses on dealing with spatial interaction and spatial structure in cross-sectional data and panel data regression model. This area has developed rapidly in recent years. Spatial econometrics is widely used in applied economics and policy analysis, particularly in regional economics, residential economics, environmental and resource economics and development economics and other fields. Firstly, this paper made an exploratory spatial data analysis of China’s manufacturing agglomeration with Geoda. Secondly, we performed a spatial econometric analysis on influencing factors of china’s manufacturing agglomeration using spatial lag model and spatial error model. Finally, we used Matlab neural network toolbox to predict China’s manufacturing agglomeration based on the existing data. From an economic point of view, this article can also be seen as an example of spatial econometric analysis. A. Dependent Variable and Indicators We have many indicators to measure the industrial agglomeration in the actual study. In this paper, we chose
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A Modified Technique for Analysis o
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