A COMPARATIVE STUDY OF MIGRANT URBAN INTEGRATION WITH PRINCIPAL COMPONENT ANALYSIS
TIAN Ming1, BO Jun-li2
1. School of Social Development and Public Policy, Beijing Normal University, Beijing 100875, China;
2. Department of Geography, the Experimental High School Attached Beijing Normal University, Beijing 100032, China
Abstract：Based on the survey questionnaires data of the floating population in Beijing, Dongguan, Wenzhou, Qingdao, Wuxi and Shenyang 6 cities in eastern China,this paper builds up an indicator system that is in keeping with China's reality to assesses the degree of floating population integration into urban with principal component analysis. In our study we interpret urban integration with respect to economic integration, social integration, mental integration, spatial integration and institutional integration. The results show that the degree of urban integration in Qingdao city is highest, the second highest is that of in Shenyang city. And the lowest city is Wenzhou and Dongguan. Wuxi and Beijing fall in between. Basically, that of north cities is higher than south cities in China. The urban integration is an interactive process between floating population and local society. The impacts of city character to urban integration of floating population are in two aspects. One is that city characters could bring about some positive or negative effect. Positive effect can shorten the social distance and make floating population feel friendliness to cities, but negative effect would deepen the gap. The other is the choice mechanism that different city need different population in development process, so it is more difficult for some floating population who are not necessary labors for some cities. The local dialect of cities has important impact. So, to popularize Beijing pronunciation(Mandarin) is an effective method of promotion urban integration of floating population.
田明, 薄俊丽. 东部地区流动人口城市融入的比较研究[J]. 人文地理, 2014, 29(1): 43-48.
TIAN Ming, BO Jun-li. A COMPARATIVE STUDY OF MIGRANT URBAN INTEGRATION WITH PRINCIPAL COMPONENT ANALYSIS. HUMAN GEOGRAPHY, 2014, 29(1): 43-48.
