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### 中原地区多尺度城乡收入的时空分异

1. 1. 河南大学环境与规划学院,黄河中下游数字地理重点实验室,河南 开封 475004
2. 中原经济区三化协调发展河南省协同创新中心,郑州 450046
• 收稿日期:2014-05-04 修回日期:2014-10-08 出版日期:2015-01-10 发布日期:2015-03-17
• 作者简介:

作者简介:丁志伟(1983- ),男,河南荥阳人,博士,讲师,研究方向为城市规划与设计、城市与区域综合发展。E-mail:dingzhiwei1216@163.com

• 基金资助:
国家自然科学基金项目(41271144);河南省政府决策研究招标课题项目(2014190/2014207);河南省科技发展计划软科学项目(142400410684)

### Spatial-temporal differentiation of urban-rural income in Central Plains Region at different scales

Zhiwei DING1,2(), Gaisu ZHANG1,2(), Fazeng WANG1,2

1. 1. College of Environment and Planning/Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Kaifeng 475004, Henan China
2. Henan Three New-types Coordinated Development Center, Zhengzhou, 475004, Henan, China
• Received:2014-05-04 Revised:2014-10-08 Online:2015-01-10 Published:2015-03-17

Abstract:

Coordinating the urban and rural development and promoting the construction of new socialist countryside are two important strategies for Chinese modernization. In this study, urban-rural income indexes such as urban per capita income, rural per capita income and urban-rural income ratio, to a certain degree, can be used to measure the gap of urban-rural residents’ living quality and their coordinated development. This paper aims to investigate the spatial-temporal differentiation of urban-rural income in the Central Plains Region (CPR) at multiple spatial scales. Based on the three urban-rural income indicators, we use CV, Theil Index, Moran's I and scale variance to examine the changing trends and spatial patterns of urban-rural development in CPR from 2000 to 2012 at regional, city and county levels. The results are summarized as follows. (1) From the calculation results of CV, Theil index and Moran's I, we find that the regional difference of urban per capita income is gradually decreasing at the three spatial scales, while that of rural per capita income is gradually increasing since 2000. The indicator of urban-rural income ratio is gradually declining at regional and city levels, but increasing at county level; and the overall trend is similar to the change of rural per capita income. (2) The results of the scale variance show different patterns obviously. The variance of the urban per capita income is decreased from county level to city and regional levels; however, that of rural per capita income and urban-rural ratio is decreased from county level to regional and city levels. (3) The statistical results of Moran's I indicate positive local spatial autocorrelation patterns of urban-rural income, and tend to be stable since 2000. The maps of local Moran's I show the significant clusters: LL for both of urban and rural per capita income, and HH for urban-rural income ratio. (4) The spatial classification of the three indexes of the urban-rural income in ArcGIS 10.0 show very different results. The city level indexes demonstrate a clear clustering pattern, while the spatial structure of county level indexes is much complex, which is mainly dominated by irregular circles with different sizes. Our research illustrates that the spatial-temporal analysis of the urban-rural income is practical to measure the spatial patterns and growth trends at multiple scales, and thus it provides in-depth information for urban-rural development. The research results also provide theoretical basis for the urban-rural development strategy in CPR and other regions.