ANALYSIS ON URBANITY DEVELOPMENT DIFFERENTIATIONS OF COUNTY AREAS IN JIANGSU PROVINCE BASED ON BP NEURAL NETWORK
YU Hua1, ZHANG Xiao-lin1,2, HUANG Fei-fei1, CUI Kai-jun1, WANG Li-li1
1. College of Geographic Sciences, Nanjing Normal University, Nanjing, 210046, China;
2. Research Center for Regional Development & Planning, Nanjing Normal University, Nanjing 210046, China
Abstract：Urbanity refers to the manifestation degree of urban nature in a certain region, which should be evaluated comprehensively by an integrated index system. Urbanity index refers to the choice of different comparison units and the determination of different evaluation criteria. Artificial Neural Network is a nonlinear system, and has good features of self-organization, self-adaption and self-learning. As one of the most widely-used networks, BP neural network takes advantages over fault tolerance, robustness and self-adaption. So this paper builds nonlinear model of BP neural network to avoid subjectivity of index weight to measure urbanity. Choosing fifty-two counties in Jiangsu province as study object, this paper first selects 16 representative indicators from the aspects of space concentration level, economic progress level, social development level and infrastructural facility construction level, and constructs index system to evaluate urbanity comprehensive value by using BP neural network theory and method according to comprehensive, representative, comparable, operational and regional principle, based on statistic data of Jiangsu province in 2005. Then, urbanity comprehensive evaluation value of fifty-two county areas are classified into five degrees, including the highest urbanity, higher urbanity, middle urbanity, lower urbanity and the lowest urbanity. Moreover, MapInfo software is applied to make a thematic map to express the urbanity disparities of these fifty-two counties. Besides, the paper analyzes frequency distribution features and calculates variation coefficient, weighted variation coefficient, William coefficient and maximal and minimal coefficient, finding:1) urbanity comprehensive evaluation value is declined from south to north; 2) its frequency distribution has positive skewness features, and a bigger proportion of counties in the third degree and fourth degree; and 3) internal differentiations of three regions increase from south to north. Finally, the paper holds that the coordination between population concentration and geographical expansion as well as synchronous development between economy and science technology should be promoted in the course of rapid urbanization. Meanwhile, space concentration level and social development level should be enhanced.
余华, 张小林, 黄飞飞, 崔开俊, 王丽莉. 江苏省县域城市性发展差异的BP神经网络测定[J]. 人文地理, 2009, 24(4): 38-42,49.
YU Hua, ZHANG Xiao-lin, HUANG Fei-fei, CUI Kai-jun, WANG Li-li. ANALYSIS ON URBANITY DEVELOPMENT DIFFERENTIATIONS OF COUNTY AREAS IN JIANGSU PROVINCE BASED ON BP NEURAL NETWORK. HUMAN GEOGRAPHY, 2009, 24(4): 38-42,49.
